Treffer: Revolutionizing Fresh Food Quality Control in Supply Chains With Machine Learning: Current Advances and Future Challenges.
Title:
Revolutionizing Fresh Food Quality Control in Supply Chains With Machine Learning: Current Advances and Future Challenges.
Authors:
Xu J; College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory of Agri-food Resources and High-value Utilization, Zhejiang University, Hangzhou, China.; Innovation Center for Postharvest Agro-Products Technology, Zhejiang University, Hangzhou, China., Luo Z; College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory of Agri-food Resources and High-value Utilization, Zhejiang University, Hangzhou, China.; Innovation Center for Postharvest Agro-Products Technology, Zhejiang University, Hangzhou, China., Zhang X; College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory of Agri-food Resources and High-value Utilization, Zhejiang University, Hangzhou, China.; Innovation Center for Postharvest Agro-Products Technology, Zhejiang University, Hangzhou, China., Li Z; College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory of Agri-food Resources and High-value Utilization, Zhejiang University, Hangzhou, China.; Innovation Center for Postharvest Agro-Products Technology, Zhejiang University, Hangzhou, China., Li D; College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory of Agri-food Resources and High-value Utilization, Zhejiang University, Hangzhou, China.; Innovation Center for Postharvest Agro-Products Technology, Zhejiang University, Hangzhou, China., Chen Y; College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory of Agri-food Resources and High-value Utilization, Zhejiang University, Hangzhou, China.; Innovation Center for Postharvest Agro-Products Technology, Zhejiang University, Hangzhou, China., Lin X; College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory of Agri-food Resources and High-value Utilization, Zhejiang University, Hangzhou, China.; Innovation Center for Postharvest Agro-Products Technology, Zhejiang University, Hangzhou, China., Guillén F; Postharvest Research Group of Fruit and Vegetables, CIAGRO, University Miguel Hernández, Orihuela, Spain., Xu Y; College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory of Agri-food Resources and High-value Utilization, Zhejiang University, Hangzhou, China.; Innovation Center for Postharvest Agro-Products Technology, Zhejiang University, Hangzhou, China.; School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.
Source:
Comprehensive reviews in food science and food safety [Compr Rev Food Sci Food Saf] 2026 Jan; Vol. 25 (1), pp. e70360.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Institute of Food Technologists Country of Publication: United States NLM ID: 101305205 Publication Model: Print Cited Medium: Internet ISSN: 1541-4337 (Electronic) Linking ISSN: 15414337 NLM ISO Abbreviation: Compr Rev Food Sci Food Saf Subsets: MEDLINE
Imprint Name(s):
Original Publication: Chicago, Ill. : Institute of Food Technologists
MeSH Terms:
References:
Abedi‐Firoozjah, R., S. A. Salim, S. Hasanvand, et al. 2023. “Application of Smart Packaging for Seafood: A Comprehensive Review.” Comprehensive Reviews in Food Science and Food Safety 22: 1438–1461. https://doi.org/10.1111/1541‐4337.13117.
Adedeji, A. A., N. Ekramirad, A. Rady, et al. 2020. “Non‐Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables Under Postharvest Conditions: A Critical Review.” Foods 9: 927. https://doi.org/10.3390/foods9070927.
Ahmad, S., I. Shakeel, S. Mehfuz, and J. Ahmad. 2023. “Deep Learning Models for Cloud, Edge, Fog, and IoT Computing Paradigms: Survey, Recent Advances, and Future Directions.” Computer Science Review 49: 100568. https://doi.org/10.1016/j.cosrev.2023.100568.
Akhtar, N., A. J. Siddiqui, M. Ramzan, et al. 2024. “Investigation of Pharmacologically Important Polyphenolic Secondary Metabolites in Plant‐Based Food Samples Using HPLC‐DAD.” Plants 13: 1311. https://doi.org/10.3390/plants13101311.
Al‐Dairi, M., P. B. Pathare, R. Al‐Yahyai, H. Jayasuriya, and Z. Al‐Attabi. 2023. “Postharvest Quality, Technologies, and Strategies to Reduce Losses Along the Supply Chain of Banana: A Review.” Trends in Food Science & Technology 134: 177–191. https://doi.org/10.1016/j.tifs.2023.03.003.
Aloe, C. M., and A. De Maio. 2025. “Balancing Temperature and Humidity Control in Storage Location Assignment: An Optimization Perspective in Refrigerated Warehouses.” Sustainability 17: 7477. https://doi.org/10.3390/su17167477.
Al Sahili, Z., and M. Awad. 2022. “The Power of Transfer Learning in Agricultural Applications: AgriNet.” Frontiers in Plant Science 13: 1–12. https://doi.org/10.3389/fpls.2022.992700.
Amini, P., and M. Khashei. 2019. “A Soft Intelligent Allocation‐based Hybrid Model for Uncertain Complex Time Series Forecasting.” Applied Soft Computing 84: 105736. https://doi.org/10.1016/j.asoc.2019.105736.
An, J.‐P., J. Li, K. Rodrigues‐Stuart, M. M. Dewdney, M. A. Ritenour, and Y. Wang. 2024. “Machine Learning‐Based Metabolomics Analysis Reveals the Early Biomarkers for Diplodia Stem‐end rot in Grapefruit Caused by Lasiodiplodia Theobromae.” Postharvest Biology and Technology 212: 112868. https://doi.org/10.1016/j.postharvbio.2024.112868.
Anusha, K., K. Uma, K. Jayasri, S. Kambham, and S. D. Dandamudi. 2024. “IoT Based Food Spoilage Detection Using Machine Learning Techniques.” In 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). IEEE.
Balasubramaniam, N., M. Kauppinen, A. Rannisto, K. Hiekkanen, and S. Kujala. 2023. “Transparency and Explainability of AI Systems: From Ethical Guidelines to Requirements.” Information and Software Technology 159: 107197. https://doi.org/10.1016/j.infsof.2023.107197.
Ballard, Z., C. Brown, A. M. Madni, and A. Ozcan. 2021. “Machine Learning and Computation‐Enabled Intelligent Sensor Design.” Nature Machine Intelligence 3: 556–565. https://doi.org/10.1038/s42256‐021‐00360‐9.
Beiran, M., C. A. Spencer‐Salmon, and K. Rajan. 2023. “A ‘Programming’ Framework for Recurrent Neural Networks.” Nature Machine Intelligence 5: 570–571. https://doi.org/10.1038/s42256‐023‐00674‐w.
Belay, Z. A., and O. J. Caleb. 2022. “Role of Integrated Omics in Unravelling Fruit Stress and Defence Responses During Postharvest: A Review.” Food Chemistry: Molecular Sciences 5: 100118. https://doi.org/10.1016/j.fochms.2022.100118.
Bergmeir, C., and J. M. Benítez. 2012. “On the Use of Cross‐Validation for Time Series Predictor Evaluation.” Information Sciences 191: 192–213. https://doi.org/10.1016/j.ins.2011.12.028.
Bhargava, A., A. Bansal, and V. Goyal. 2022. “Machine Learning‐Based Detection and Sorting of Multiple Vegetables and Fruits.” Food Analytical Methods 15: 228–242. https://doi.org/10.1007/s12161‐021‐02086‐1.
Brink, S. C. 2023. “Food Security: Solutions Offered by Plant Science.” Trends in Plant Science 28: 489–490. https://doi.org/10.1016/j.tplants.2023.03.012.
Brown, D., I. Van den Bergh, S. de Bruin, L. Machida, and J. van Etten. 2020. “Data Synthesis for Crop Variety Evaluation: A Review.” Agronomy for Sustainable Development 40: 25. https://doi.org/10.1007/s13593‐020‐00630‐7.
Bu, Y., J. Hu, C. Chen, et al. 2024. “ResNet Incorporating the Fusion Data of RGB & Hyperspectral Images Improves Classification Accuracy of Vegetable Soybean Freshness.” Scientific Reports 14: 2568. https://doi.org/10.1038/s41598‐024‐51668‐6.
Cabitza, F., A. Campagner, F. Soares, et al. 2021. “The Importance of Being External: Methodological Insights for the External Validation of Machine Learning Models in Medicine.” Computer Methods and Programs in Biomedicine 208: 106288. https://doi.org/10.1016/j.cmpb.2021.106288.
Cawley, G. C., and N. L. C. Talbot. 2010. “On Over‐fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation.” Journal of Machine Learning Research 11: 2079–2107. https://doi.org/10.5555/1756006.1859921.
Cembrowska‐Lech, D., A. Krzemińska, T. Miller, et al. 2023. “An Integrated Multi‐Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture.” Biology 12: 1298. https://doi.org/10.3390/biology12101298.
Chakraborty, S. K., A. Subeesh, K. Dubey, et al. 2023. “Development of an Optimally Designed Real‐Time Automatic Citrus Fruit Grading‐sorting Machine Leveraging Computer Vision‐Based Adaptive Deep Learning Model.” Engineering Applications of Artificial Intelligence 120: 105826. https://doi.org/10.1016/j.engappai.2023.105826.
Chaudhari, D., and S. Waghmare. 2021. “Machine Vision Based Fruit Classification and Grading—A Review.” In The 4th International Conference on Communications and Cyber Physical Engineering (ICCCE). IEEE.
Chavan, P., K. Lata, T. Kaur, et al. 2023. “Recent Advances in the Preservation of Postharvest Fruits Using Edible Films and Coatings: A Comprehensive Review.” Food Chemistry 418: 135916. https://doi.org/10.1016/j.foodchem.2023.135916.
Chazette, L., and K. Schneider. 2020. “Explainability as a Non‐Functional Requirement: Challenges and Recommendations.” Requirements Engineering 25: 493–514. https://doi.org/10.1007/s00766‐020‐00333‐1.
Chen, S., P. Wang, H. Guo, and Y. Zhang. 2024. “Deciphering Gene Expression Patterns Using Large‐Scale Transcriptomic Data and Its Applications.” Briefings in Bioinformatics 25: bbae590. https://doi.org/10.1093/bib/bbae590.
Czerwiński, K., T. Rydzkowski, J. Wróblewska‐Krepsztul, and V. K. Thakur. 2021. “Towards Impact of Modified Atmosphere Packaging (MAP) on Shelf‐life of Polymer‐Film‐Packed Food Products: Challenges and Sustainable Developments.” Coatings 11: 1504. https://doi.org/10.3390/coatings11121504.
De, S., S. Banerjee, and S. Banerjee. 2024. “Managing Postharvest Losses of Vegetables and Fruits: A Methodological Review.” Recent Advances in Food, Nutrition & Agriculture 15: 138–162. https://doi.org/10.2174/012772574x280698231221203313.
de Araújo Gomes, A., S. M. Azcarate, P. H. G. D. Diniz, D. D. de Sousa Fernandes, and G. Veras. 2022. “Variable Selection in the Chemometric Treatment of Food Data: A Tutorial Review.” Food Chemistry 370: 131072. https://doi.org/10.1016/j.foodchem.2021.131072.
Deekshith, A. 2021. “Data Engineering for AI: Optimizing Data Quality and Accessibility for Machine Learning Models.” International Journal of Management Education for Sustainable Development 4: 1–33.
de Jongh, R. P. H., A. D. J. van Dijk, M. K. Julsing, P. J. Schaap, and D. de Ridder. 2020. “Designing Eukaryotic Gene Expression Regulation Using Machine Learning.” Trends in Biotechnology 38: 191–201. https://doi.org/10.1016/j.tibtech.2019.07.007.
Deng, X., S. Cao, and A. L. Horn. 2021. “Emerging Applications of Machine Learning in Food Safety.” Annual Review of Food Science and Technology 12: 513–538. https://doi.org/10.1146/annurev‐food‐071720‐024112.
de Oliveira, A. N., S. R. F. Bolognini, L. C. Navarro, et al. 2023. “Tomato Classification Using Mass Spectrometry‐Machine Learning Technique: A Food Safety‐Enhancing Platform.” Food Chemistry 398: 133870. https://doi.org/10.1016/j.foodchem.2022.133870.
Devarajan, G. G., S. M. Nagarajan, T.V., R. V. T, U. Ghosh, and W. Alnumay. 2023. “DDNSAS: Deep Reinforcement Learning Based Deep Q‐Learning Network for Smart Agriculture System.” Sustainable Computing: Informatics and Systems 39: 100890. https://doi.org/10.1016/j.suscom.2023.100890.
Dey, A. 2016. “Machine Learning Algorithms: A Review.” International Journal of Computer Science and Information Technologies 7: 1174–1179.
Dhal, S. B., and D. Kar. 2025. “Leveraging Artificial Intelligence and Advanced Food Processing Techniques for Enhanced Food Safety, Quality, and Security: A Comprehensive Review.” Discover Applied Sciences 7: 75. https://doi.org/10.1007/s42452‐025‐06472‐w.
Dhall, D., R. Kaur, and M. Juneja. 2020. “Machine Learning: A Review of the Algorithms and Its Applications.” In Proceedings of ICRIC 2019: Recent Innovations in Computing, 47–63. Springer. https://doi.org/10.1007/978‐3‐030‐29407‐6_5.
Dickinson, E., M. J. Rusilowicz, M. Dickinson, et al. 2018. “Integrating Transcriptomic Techniques and k‐means Clustering in Metabolomics to Identify Markers of Abiotic and Biotic Stress in Medicago Truncatula.” Metabolomics 14: 126. https://doi.org/10.1007/s11306‐018‐1424‐y.
Do, E., M. Kim, D.‐Y. Ko, M. Lee, C. Lee, and K.‐M. Ku. 2024. “Machine Learning for Storage Duration Based on Volatile Organic Compounds Emitted From 'Jukhyang' and 'Merry Queen' Strawberries During Post‐Harvest Storage.” Postharvest Biology and Technology 211: 112808. https://doi.org/10.1016/j.postharvbio.2024.112808.
Doan, T.‐N., and D.‐N. Le‐Thi. 2023. “A Novel Mango Grading System Based on Image Processing and Machine Learning Methods.” International Journal of Advanced Computer Science and Applications 14: 1118–1129. https://doi.org/10.14569/IJACSA.2023.01405115.
Du, Y., Q. Tian, G. Li, J. Yi, X. Hu, and Y. Jiang. 2024. “Advanced Application of Slightly Acidic Electrolyzed Water for Fresh‐Cut Fruits and Vegetables Preservation.” Food Research International 195: 114996. https://doi.org/10.1016/j.foodres.2024.114996.
Duan, H., Z. Peng, L. Xiang, Y. Hu, and B. Li. 2024. “A Verifiable and Privacy‐Preserving Federated Learning Training Framework.” IEEE Transactions on Dependable and Secure Computing 21: 5046–5058. https://doi.org/10.1109/TDSC.2024.3369658.
Duan, Y., G.‐B. Wang, O. A. Fawole, et al. 2020. “Postharvest Precooling of Fruit and Vegetables: A Review.” Trends in Food Science & Technology 100: 278–291. https://doi.org/10.1016/j.tifs.2020.04.027.
El‐Mesery, H. S., O. A. Adelusi, S. Ghashi, P. B. Njobeh, Z. Hu, and W. Kun. 2024. “Effects of Storage Conditions and Packaging Materials on the Postharvest Quality of Fresh Chinese Tomatoes and the Optimization of the Tomatoes' physiochemical Properties Using Machine Learning Techniques.” LWT 201: 116280. https://doi.org/10.1016/j.lwt.2024.116280.
Farah, J. S., R. N. Cavalcanti, J. T. Guimarães, et al. 2021. “Differential Scanning Calorimetry Coupled With Machine Learning Technique: An Effective Approach to Determine the Milk Authenticity.” Food Control 121: 107585. https://doi.org/10.1016/j.foodcont.2020.107585.
Farid, D. M., L. Zhang, C. M. Rahman, M. A. Hossain, and R. Strachan. 2014. “Hybrid Decision Tree and Naïve Bayes Classifiers for Multi‐Class Classification Tasks.” Expert Systems with Applications 41: 1937–1946. https://doi.org/10.1016/j.eswa.2013.08.089.
Fashi, M., L. Naderloo, and H. Javadikia. 2019. “The Relationship Between the Appearance of Pomegranate Fruit and Color and Size of Arils Based on Image Processing.” Postharvest Biology and Technology 154: 52–57. https://doi.org/10.1016/j.postharvbio.2019.04.017.
Fu, J., Z. Zhang, and D. Lyu. 2019. “Research and Application of Information Service Platform for Agricultural Economic Cooperation Organization Based on Hadoop Cloud Computing Platform Environment: Taking Agricultural and Fresh Products as an Example.” Cluster Computing 22: 14689–14700. https://doi.org/10.1007/s10586‐018‐2380‐z.
Gao, C. 2021. “Genome Engineering for Crop Improvement and Future Agriculture.” Cell 184: 1621–1635. https://doi.org/10.1016/j.cell.2021.01.005.
Gao, Y., S. Zhang, T. Aili, et al. 2022. “Dual Signal Light Detection of Beta‐Lactoglobulin Based on a Porous Silicon Bragg Mirror.” Biosensors and Bioelectronics 204: 114035. https://doi.org/10.1016/j.bios.2022.114035.
Gardezi, M., B. Joshi, D. M. Rizzo, et al. 2024. “Artificial Intelligence in Farming: Challenges and Opportunities for Building Trust.” Agronomy Journal 116: 1217–1228. https://doi.org/10.1002/agj2.21353.
Garg, S., N. P. Rumjit, and S. Roy. 2024. “Smart Agriculture and Nanotechnology: Technology, Challenges, and New Perspective.” Advanced Agrochem 3: 115–125. https://doi.org/10.1016/j.aac.2023.11.001.
Gaye, B., D. Zhang, and A. Wulamu. 2021. “Improvement of Support Vector Machine Algorithm in Big Data Background.” Mathematical Problems in Engineering 2021: 5594899. https://doi.org/10.1155/2021/5594899.
Ghaani, M., C. A. Cozzolino, G. Castelli, and S. Farris. 2016. “An Overview of the Intelligent Packaging Technologies in the Food Sector.” Trends in Food Science and Technology 51: 1–11. https://doi.org/10.1016/j.tifs.2016.02.008.
Gong, X., J. Huang, Y. Xu, et al. 2023. “Deterioration of Plant Volatile Organic Compounds in Food: Consequence, Mechanism, Detection, and Control.” Trends in Food Science & Technology 131: 61–76. https://doi.org/10.1016/j.tifs.2022.11.022.
Goyal, R., P. Singha, and S. K. Singh. 2024. “Spectroscopic Food Adulteration Detection Using Machine Learning: Current Challenges and Future Prospects.” Trends in Food Science & Technology 146: 104377. https://doi.org/10.1016/j.tifs.2024.104377.
Gu, J., Z. Wang, J. Kuen, et al. 2018. “Recent Advances in Convolutional Neural Networks.” Pattern Recognition 77: 354–377. https://doi.org/10.1016/j.patcog.2017.10.013.
Guo, J., X. Wei, E. Lü, Y. Wang, and Z. Deng. 2020. “Ripening Behavior and Quality of 1‐MCP Treated d'Anjou Pears During Controlled Atmosphere Storage.” Food Control 117: 107364. https://doi.org/10.1016/j.foodcont.2020.107364.
Gustavsson, J., C. Cederberg, U. Sonesson, R. V. Otterdijk, and A. Meybeck. 2011. Global Food Losses and Food Waste: Extent, Causes and Prevention. FAO.
Gutiérrez‐Pacheco, M. M., L. A. Ortega‐Ramírez, B. A. Silva‐Espinoza, et al. 2020. “Individual and Combined Coatings of Chitosan and Carnauba Wax With Oregano Essential Oil to Avoid Water Loss and Microbial Decay of Fresh Cucumber.” Coatings 10: 614. https://doi.org/10.3390/coatings10070614.
Habibi, F., D. A. Boakye, Y. Chang, et al. 2024. “Molecular Mechanisms Underlying Postharvest Physiology and Metabolism of Fruit and Vegetables Through Multi‐Omics Technologies.” Scientia Horticulturae 324: 112562. https://doi.org/10.1016/j.scienta.2023.112562.
Hadish, J. A., H. L. Hargarten, H. Zhang, J. P. Mattheis, L. A. Honaas, and S. P. Ficklin. 2024. “Towards Identification of Postharvest Fruit Quality Transcriptomic Markers in Malus domestica.” PLoS ONE 19: e0297015. https://doi.org/10.1371/journal.pone.0297015.
He, X., Y. Pu, L. Chen, et al. 2023. “A Comprehensive Review of Intelligent Packaging for Fruits and Vegetables: Target Responders, Classification, Applications, and Future Challenges.” Comprehensive Reviews in Food Science and Food Safety 22: 842–881. https://doi.org/10.1111/1541‐4337.13093.
He, Y., B. Fan, L. Sun, et al. 2023. “Rapid Appearance Quality of Rice Based on Machine Vision and Convolutional Neural Network Research on Automatic Detection System.” Frontiers in Plant Science 14: 1190591. https://doi.org/10.3389/fpls.2023.1190591.
Homma, F., J. Lyu, and R. A. van der Hoorn. 2024. “Using AlphaFold Multimer to Discover Interkingdom Protein–Protein Interactions.” The Plant Journal 120: 19–28. https://doi.org/10.1111/tpj.16969.
Hong, H., M. F. Rizzi, D. Wang, L. McLandsborough, and J. Lu. 2024. “A Meta‐analysis on the Antimicrobial Effectiveness of Ozonated Water Treatments for Fresh Produce Washing—Effect of Ozonation Methods.” Foods 13: 3906. https://doi.org/10.3390/foods13233906.
Hongyang, T., H. Daming, H. Xingyi, et al. 2021. “Detection of Browning of Fresh‐Cut Potato Chips Based on Machine Vision and Electronic Nose.” Journal of Food Process Engineering 44: e13631. https://doi.org/10.1111/jfpe.13631.
Hu, S., H. Li, C. Chen, et al. 2022. “Raman Spectroscopy Combined With Machine Learning Algorithms to Detect Adulterated Suichang Native Honey.” Scientific Reports 12: 3456. https://doi.org/10.1038/s41598‐022‐07222‐3.
Huang, J., Q. Lin, H. Fei, et al. 2023. “Discovery of Deaminase Functions by Structure‐Based Protein Clustering.” Cell 186: 3182–3195. https://doi.org/10.1016/j.cell.2023.05.041.
Iftikhar, S., N. Anjum, A. B. Siddiqui, M. Ur Rehman, and N. Ramzan. 2025. “Explainable CNN for Brain Tumor Detection and Classification Through XAI Based Key Features Identification.” Brain Informatics 12: 10. https://doi.org/10.1186/s40708‐025‐00257‐y.
Iraji, M. S. 2019. “Comparison Between Soft Computing Methods for Tomato Quality Grading Using Machine Vision.” Journal of Food Measurement and Characterization 13: 1–15. https://doi.org/10.1007/s11694‐018‐9913‐2.
Janiesch, C., P. Zschech, and K. Heinrich. 2021. “Machine Learning and Deep Learning.” Electronic Markets 31: 685–695. https://doi.org/10.1007/s12525‐021‐00475‐2.
Jayasudha, M., M. Elangovan, M. Mahdal, and J. Priyadarshini. 2022. “Accurate Estimation of Tensile Strength of 3D Printed Parts Using Machine Learning Algorithms.” Processes 10: 1158. https://doi.org/10.3390/pr10061158.
Jin, X., C. Liu, T. Xu, L. Su, and X. Zhang. 2020. “Artificial Intelligence Biosensors: Challenges and Prospects.” Biosensors and Bioelectronics 165: 112412. https://doi.org/10.1016/j.bios.2020.112412.
Jin, Z., and Z. Wei. 2024. “Molecular Simulation for Food Protein–ligand Interactions: a Comprehensive Review on Principles, Current Applications, and Emerging Trends.” Comprehensive Reviews in Food Science and Food Safety 23: e13280. https://doi.org/10.1111/1541‐4337.13280.
Kaliappan, J., A. R. Bagepalli, S. Almal, R. Mishra, Y.‐C. Hu, and K. Srinivasan. 2023. “Impact of Cross‐validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise.” Diagnostics 13: 1692. https://doi.org/10.3390/diagnostics13101692.
Kalpana, S., S. R. Priyadarshini, M. Maria Leena, J. A. Moses, and C. Anandharamakrishnan. 2019. “Intelligent Packaging: Trends and Applications in Food Systems.” Trends in Food Science & Technology 93: 145–157. https://doi.org/10.1016/j.tifs.2019.09.008.
Kang, Z., Y. Zhao, L. Chen, Y. Guo, Q. Mu, and S. Wang. 2022. “Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain.” Food Engineering Reviews 14: 596–616. https://doi.org/10.1007/s12393‐022‐09322‐2.
Karmaker, S. K., M. M. Hassan, M. J. Smith, L. Xu, C. Zhai, and K. Veeramachaneni. 2021. “Automl to Date and Beyond: Challenges and Opportunities.” ACM Computing Surveys 54: 1–36. https://doi.org/10.1145/3470918.
Kelchtermans, P., W. Bittremieux, K. De Grave, et al. 2014. “Machine Learning Applications in Proteomics Research: How the Past Can Boost the Future.” Proteomics 14: 353–366. https://doi.org/10.1002/pmic.201300289.
Khan, M. I. H., S. S. Sablani, R. Nayak, and Y. Gu. 2022. “Machine Learning‐based Modeling in Food Processing Applications: State of the Art.” Comprehensive Reviews in Food Science and Food Safety 21: 1409–1438. https://doi.org/10.1111/1541‐4337.12912.
Koleini, S., B. Pahlevanzadeh, and I. Shiraz. 2024. “Enhancing High‐Performance Computing (HPC) Security: A Comprehensive Review of Detection and Protection Strategies.” Journal of Distributed Computing and Systems 6: 12–24.
Kotsiantis, S. B., I. Zaharakis, and P. Pintelas. 2007. “Supervised Machine Learning: A Review of Classification techniques.” In Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems With Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies. IOS Press.
Kotsiantis, S. B., I. D. Zaharakis, and P. E. Pintelas. 2006. “Machine Learning: A Review of Classification and Combining Techniques.” Artificial Intelligence Review 26: 159–190. https://doi.org/10.1007/s10462‐007‐9052‐3.
Koyama, K., M. Tanaka, B.‐H. Cho, Y. Yoshikawa, and S. Koseki. 2021. “Predicting Sensory Evaluation of Spinach Freshness Using Machine Learning Model and Digital Images.” PLoS ONE 16: e0248769. https://doi.org/10.1371/journal.pone.0248769.
Kumar, M., S. A. Khan, A. Bhatia, V. Sharma, and P. Jain. 2023. “Machine Learning Algorithms: A Conceptual Review.” In 2023 1st International Conference on Intelligent Computing and Research Trends (ICRT), 1–7. IEEE.
Lagarda‐Clark, E. A., C. Goulet, and A. Duarte‐Sierra. 2024. “Biochemical Dynamics During Postharvest: Highlighting the Interplay of Stress During Storage and Maturation of Fresh Produce.” Biomolecular Concepts 15: 1–21. https://doi.org/10.1515/bmc‐2022‐0048.
LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521: 436–444. https://doi.org/10.1038/nature14539.
Lee, J.‐S. 2015. “Quality Characteristics, Carbon Dioxide, and Ethylene Production of Asparagus (Asparagus officinalis L.) Treated With 1‐Methylcyclopropene and 2‐Chloroethylphosphonic Acid During Storage.” Korean Journal of Horticultural Science and Technology 33: 675–686. https://doi.org/10.7235/hort.2015.14145.
Li, L., P. Yi, J. Sun, et al. 2023. “Genome‐wide Transcriptome Analysis Uncovers Gene Networks Regulating Fruit Quality and Volatile Compounds in Mango Cultivar 'Tainong' During Postharvest.” Food Research International 165: 112531. https://doi.org/10.1016/j.foodres.2023.112531.
Li, M., D. Li, F. Feng, S. Zhang, F. Ma, and L. Cheng. 2016. “Proteomic Analysis Reveals Dynamic Regulation of Fruit Development and Sugar and Acid Accumulation in Apple.” Journal of Experimental Botany 67: 5145–5157. https://doi.org/10.1093/jxb/erw277.
Li, Q., S. An, W. Liu, and L. Li. 2021. “Semisupervised Learning on Graphs With an Alternating Diffusion Process.” IEEE Transactions on Neural Networks and Learning Systems 32: 2862–2874. https://doi.org/10.1109/TNNLS.2020.3008445.
Li, Q., C. Zhang, H. Wang, et al. 2023. “Machine Learning Technique Combined With Data Fusion Strategies: A Tea Grade Discrimination Platform.” Industrial Crops and Products 203: 117127. https://doi.org/10.1016/j.indcrop.2023.117127.
Li, X., D. Liu, Y. Pu, and Y. Zhong. 2023. “Recent Advance of Intelligent Packaging Aided by Artificial Intelligence for Monitoring Food Freshness.” Foods 12: 2976. https://doi.org/10.3390/foods12152976.
Li, Y., Z. Wang, A. Yang, and X. Yu. 2025. “Integrating Evolutionary Algorithms and Enhanced‐YOLOv8 + for Comprehensive Apple Ripeness Prediction.” Scientific Reports 15: 7307. https://doi.org/10.1038/s41598‐025‐91939‐4.
Liakos, K. G., V. Athanasiadis, E. Bozinou, and S. I. Lalas. 2025. “Machine Learning for Quality Control in the Food Industry: A Review.” Foods 14: 3424. https://doi.org/10.3390/foods14193424.
Lin, E., and H.‐Y. Lane. 2017. “Machine Learning and Systems Genomics Approaches for Multi‐Omics Data.” Biomarker Research 5: 1–6. https://doi.org/10.1186/s40364‐017‐0082‐y.
Liu, S., D. Lang, G. Meng, J. Hu, M. Tang, and X. Zhou. 2022. “Tracing the Origin of Honey Products Based on Metagenomics and Machine Learning.” Food Chemistry 371: 131066. https://doi.org/10.1016/j.foodchem.2021.131066.
Liu, Z., Y. Wang, and J. Feng. 2025. “Identifying Supply Chain R&D Partners via Multilayer Institutional Cooperation Network and Tailored Link Prediction.” Computers & Industrial Engineering 201: 110887. https://doi.org/10.1016/j.cie.2025.110887.
Lopez, E., J. Etxebarria‐Elezgarai, J. M. Amigo, and A. Seifert. 2023. “The Importance of Choosing a Proper Validation Strategy in Predictive Models: A Tutorial With Real Examples.” Analytica Chimica Acta 1275: 341532. https://doi.org/10.1016/j.aca.2023.341532.
Luo, D., R. Luo, J. Cheng, and X. Liu. 2024. “Quality Detection and Grading of Peach Fruit Based on Image Processing Method and Neural Networks in Agricultural Industry.” Frontiers in Plant Science 15: 1–14. https://doi.org/10.3389/fpls.2024.1415095.
Ma, C., H. H. Zhang, and X. Wang. 2014. “Machine Learning for Big Data Analytics in Plants.” Trends in Plant Science 19: 798–808. https://doi.org/10.1016/j.tplants.2014.08.004.
Ma, H., J. Guo, G. Liu, et al. 2024. “Raman Spectroscopy Coupled With Chemometrics for Identification of Adulteration and Fraud in Muscle Foods: A Review.” Critical Reviews in Food Science and Nutrition 65, no. 11: 2008–2030. https://doi.org/10.1080/10408398.2024.2329956.
Mao, D., F. Wang, Z. Hao, and H. Li. 2018. “Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain.” International Journal of Environmental Research and Public Health 15: 1627. https://doi.org/10.3390/ijerph15081627.
Marondedze, C. 2017. “Date Fruit Proteomics During Development and Ripening Stages.” In Date Palm Biotechnology Protocols Volume II: Germplasm Conservation and Molecular Breeding 381–398. Humana Press. https://doi.org/10.1007/978‐1‐4939‐7159‐6_28.
Mathabe, P. M., Z. A. Belay, T. Ndlovu, and O. J. Caleb. 2020. “Progress in Proteomic Profiling of Horticultural Commodities During Postharvest Handling and Storage: A Review.” Scientia Horticulturae 261: 108996. https://doi.org/10.1016/j.scienta.2019.108996.
Matindoust, S., M. Baghaei‐Nejad, M. H. Shahrokh Abadi, Z. Zou, and L. R. Zheng. 2016. “Food Quality and Safety Monitoring Using Gas Sensor Array in Intelligent Packaging.” Sensor Review 36: 169–183. https://doi.org/10.1108/SR‐07‐2015‐0115.
McCafferty, C. L., S. Klumpe, R. E. Amaro, W. Kukulski, L. Collinson, and B. D. Engel. 2024. “Integrating Cellular Electron Microscopy With Multimodal Data to Explore Biology Across Space and Time.” Cell 187: 563–584. https://doi.org/10.1016/j.cell.2024.01.005.
Mogilipalem, K., A. K. Poodari, Y. Pulipati, and K. B. Sangeetha. 2024. “Food Spoilge Detection Using IoT and Machine Learning.” In 2024 5th International Conference for Emerging Technology (INCET), 1–4. IEEE.
Mohammadi, V., K. Kheiralipour, and M. Ghasemi‐Varnamkhasti. 2015. “Detecting Maturity of Persimmon Fruit Based on Image Processing Technique.” Scientia Horticulturae 184: 123–128. https://doi.org/10.1016/j.scienta.2014.12.037.
Mohammed, M., R. Srinivasagan, A. Alzahrani, and N. K. Alqahtani. 2023. “Machine‐Learning‐Based Spectroscopic Technique for Non‐Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged Under Modified Atmospheres.” Sustainability 15: 12871. https://doi.org/10.3390/su151712871.
Montesinos‐López, O. A., A. Montesinos‐López, J. Crossa, D. Gianola, C. M. Hernández‐Suárez, and J. Martín‐Vallejo. 2018. “Multi‐Trait, Multi‐Environment Deep Learning Modeling for Genomic‐Enabled Prediction of Plant Traits.” G3: Genes, Genomes, Genetics 8: 3829–3840. https://doi.org/10.1534/g3.118.200728.
Morales‐Solis, A., A. Pérez‐López, M. E. Ramírez‐Guzmán, T. Espinosa‐Solares, and I. Alia‐Tejacal. 2024. “ARIMAX Modelling: Response of Hass Avocado Respiration Rate to Environmental Factors.” Horticulturae 10: 700. https://doi.org/10.3390/horticulturae10070700.
Mukherjee, A., T. Sarkar, and K. Chatterjee. 2021. “Freshness Assessment of Indian Gooseberry (Phyllanthus emblica) Using Probabilistic Neural Network.” Journal of Biosystems Engineering 46: 399–416. https://doi.org/10.1007/s42853‐021‐00116‐8.
Murphy, K. M., E. Ludwig, J. Gutierrez, and M. A. Gehan. 2024. “Deep Learning in Image‐Based Plant Phenotyping.” Annual Review of Plant Biology 75: 771–795. https://doi.org/10.1146/annurev‐arplant‐070523‐042828.
Nami, M., M. Taheri, J. Siddiqui, I. A. Deen, M. Packirisamy, and M. J. Deen. 2024. “Recent Progress in Intelligent Packaging for Seafood and Meat Quality Monitoring.” Advanced Materials Technologies 9: 2301347. https://doi.org/10.1002/admt.202301347.
Napit, R., S. K. Jaysawal, R. Chowdhury, et al. 2024. “Aptasensors and Advancement in Molecular Recognition Technology.” Advanced Materials Technologies 10, no. 1: 2400504. https://doi.org/10.1002/admt.202400504.
Nasution, I., and K. Gusriyan. 2019. “Nutmeg Grading System Using Computer Vision Techniques.” In: International Conference on Agricultural Technology, Engineering and Environmental Sciences. IOP Publishing.
Neupane, C., M. Pereira, A. Koirala, and K. B. Walsh. 2023. “Fruit Sizing in Orchard: A Review From Caliper to Machine Vision With Deep Learning.” Sensors 23: 3868. https://doi.org/10.3390/s23083868.
Nguyen, G., S. Dlugolinsky, M. Bobák, et al. 2019. “Machine Learning and Deep Learning Frameworks and Libraries for Large‐Scale Data Mining: A Survey.” Artificial Intelligence Review 52: 77–124. https://doi.org/10.1007/s10462‐018‐09679‐z.
Nikolados, E.‐M., A. Wongprommoon, O. M. Aodha, G. Cambray, and D. A. Oyarzún. 2022. “Accuracy and Data Efficiency in Deep Learning Models of Protein Expression.” Nature Communications 13: 7755. https://doi.org/10.1038/s41467‐022‐34902‐5.
Ojha, P. K., and K. Roy. 2018. “Development of a Robust and Validated 2D‐QSPR Model for Sweetness Potency of Diverse Functional Organic Molecules.” Food and Chemical Toxicology 112: 551–562. https://doi.org/10.1016/j.fct.2017.03.043.
Okere, E. E., E. Arendse, A. Ambaw Tsige, W. J. Perold, and U. L. Opara. 2022. “Pomegranate Quality Evaluation Using Non‐Destructive Approaches: A Review.” Agriculture 12: 2034. https://doi.org/10.3390/agriculture12122034.
Opara, I. K., U. L. Opara, J. A. Okolie, and O. A. Fawole. 2024. “Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: a Review.” Plants 13: 1200. https://doi.org/10.3390/plants13091200.
Öztürk, M., and Z. Ayhan. 2023. “Combined Effects of Ethylene Scavenging‐Active Packaging System and Modified Atmosphere to Reduce Postharvest Losses of Ethylene Sensitive Produce: Banana and Kiwifruit.” Packaging Technology and Science 36: 951–967. https://doi.org/10.1002/pts.2764.
Pahar, M., M. Klopper, R. Warren, and T. Niesler. 2021. “COVID‐19 Cough Classification Using Machine Learning and Global Smartphone Recordings.” Computers in Biology and Medicine 135: 104572. https://doi.org/10.1016/j.compbiomed.2021.104572.
Palumbo, M., G. Attolico, V. Capozzi, et al. 2022. “Emerging Postharvest Technologies to Enhance the Shelf‐Life of Fruit and Vegetables: An Overview.” Foods 11: 3925. https://doi.org/10.3390/foods11233925.
Park, B., T. Shin, J.‐S. Cho, J.‐H. Lim, and K.‐J. Park. 2023. “Improving Blueberry Firmness Classification With Spectral and Textural Features of Microstructures Using Hyperspectral Microscope Imaging and Deep Learning.” Postharvest Biology and Technology 195: 112154. https://doi.org/10.1016/j.postharvbio.2022.112154.
Pasa, L., N. Navarin, and A. Sperduti. 2022. “Polynomial‐Based Graph Convolutional Neural Networks for Graph Classification.” Machine Learning 111: 1205–1237. https://doi.org/10.1007/s10994‐021‐06098‐0.
Pathmanaban, P., B. Gnanavel, S. S. Anandan, and S. Sathiyamurthy. 2023. “Advancing Post‐Harvest Fruit Handling Through AI‐Based Thermal Imaging: Applications, Challenges, and Future Trends.” Discover Food 3: 27. https://doi.org/10.1007/s44187‐023‐00068‐2.
Piras, A., C. Ehlert, and G. Gryn'ova. 2021. “Sensing and Sensitivity: Computational Chemistry of Graphene‐Based Sensors.” WIREs Computational Molecular Science 11: e1526. https://doi.org/10.1002/wcms.1526.
Pott, D. M., F. d. A. E Lima, C. Soria, et al. 2020. “Metabolic Reconfiguration of Strawberry Physiology in Response to Postharvest Practices.” Food Chemistry 321: 126747. https://doi.org/10.1016/j.foodchem.2020.126747.
Pounds, K., H. Bao, Y. Luo, et al. 2022. “Real‐Time and Rapid Food Quality Monitoring Using Smart Sensory Films With Image Analysis and Machine Learning.” ACS Food Science & Technology 2: 1123–1134. https://doi.org/10.1021/acsfoodscitech.2c00124?goto=supporting‐info.
Puthongkham, P., S. Wirojsaengthong, and A. Suea‐Ngam. 2021. “Machine Learning and Chemometrics for Electrochemical Sensors: Moving Forward to the Future of Analytical Chemistry.” Analyst 146: 6351–6364. https://doi.org/10.1039/D1AN01148K.
Raihen, M. N., and S. Akter. 2024. “Prediction Modeling Using Deep Learning for the Classification of Grape‐Type Dried Fruits.” International Journal of Mathematics and Computer in Engineering 2: 1–12. https://doi.org/10.2478/ijmce‐2024‐0001.
Ranbir. Kumar, M., G. Singh, J. Singh, N. Kaur, and N. Singh. 2022. “Machine Learning‐Based Analytical Systems: Food Forensics.” ACS Omega 7: 47518–47535. https://doi.org/10.1021/acsomega.2c05632.
Raschka, S., J. Patterson, and C. Nolet. 2020. “Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence.” Information 11: 193. https://doi.org/10.3390/info11040193.
Rashvand, M., G. Altieri, R. Abbaszadeh, et al. 2023. “Prediction of CO2 and Ethylene Produced in Packaged Apricot Under Cold Plasma Treatment by Machine Learning Approach.” Journal of Food Process Engineering 46: e14418. https://doi.org/10.1111/jfpe.14418.
Ricardo‐Rodrigues, S., M. Laranjo, and A. C. Agulheiro‐Santos. 2023. “Methods for Quality Evaluation of Sweet Cherry.” Journal of the Science of Food and Agriculture 103: 463–478. https://doi.org/10.1002/jsfa.12144.
Rizzo, M., M. Marcuzzo, A. Zangari, A. Gasparetto, and A. Albarelli. 2023. “Fruit Ripeness Classification: A Survey.” Artificial Intelligence in Agriculture 7: 44–57. https://doi.org/10.1016/j.aiia.2023.02.004.
Romruen, O., P. Kaewprachu, S. Sai‐Ut, et al. 2024. “Impact of Environmental Storage Conditions on Properties and Stability of a Smart Bilayer Film.” Scientific Reports 14: 23038. https://doi.org/10.1038/s41598‐024‐74004‐4.
Rong, D., X. Rao, and Y. Ying. 2017. “Computer Vision Detection of Surface Defect on Oranges by Means of a Sliding Comparison Window Local Segmentation Algorithm.” Computers and Electronics in Agriculture 137: 59–68. https://doi.org/10.1016/j.compag.2017.02.027.
Rosenblatt, F. 1958. “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.” Psychological Review 65: 386–408. https://doi.org/10.1037/h0042519.
Saberi Riseh, R., M. Vatankhah, M. Hassanisaadi, and J. F. Kennedy. 2023. “Chitosan‐Based Nanocomposites as Coatings and Packaging Materials for the Postharvest Improvement of Agricultural Product: A Review.” Carbohydrate Polymers 309: 120666. https://doi.org/10.1016/j.carbpol.2023.120666.
Salazar, J., C. Jorquera, R. Campos‐Vargas, C. Jorgensen, P. Zapata, and R. Infante. 2019. “Effect of the Application Timing of 1‐MCP on Postharvest Traits and Sensory Quality of a Yellow‐Fleshed Kiwifruit.” Scientia Horticulturae 244: 82–87. https://doi.org/10.1016/j.scienta.2018.09.028.
Santo, L. A. G. E., R. J. D. Placido, and N. B. Linsangan. 2021. “Potato Skin Defect Detection and Classification Through Image Processing.” In Thirteenth International Conference on Graphics and Image Processing (ICGIP). IEEE.
Schonlau, M., and R. Y. Zou. 2020. “The Random Forest Algorithm for Statistical Learning.” The Stata Journal 20: 3–29. https://doi.org/10.1177/1536867X20909688.
Schudel, S., K. Shoji, C. Shrivastava, D. Onwude, and T. Defraeye. 2023. “Solution Roadmap to Reduce Food Loss Along Your Postharvest Supply Chain From Farm to Retail.” Food Packaging and Shelf Life 36: 101057. https://doi.org/10.1016/j.fpsl.2023.101057.
Sekhar, P. K., T. Ludwig, M. Wilhelm, D. Graf, M. A. Riheen, and S. Mathur. 2019. “Potentiometric Ethene Sensor for Postharvest Detection Applications.” Journal of the Electrochemical Society 166: B1477. https://doi.org/10.1149/2.0501915jes.
Shankaraswamy, J., and T. Radhika. 2024. “Sensor, IoT‐Based Post‐Harvest Shelf Life Determination of Tomato (Lycopersicon esculentum) Through Machine Learning Predictive Analysis for Intelligent Transport.” Journal of Environmental Biology 45: 455–464. https://doi.org/10.22438/jeb/45/4/MRN‐5339.
Shanthini, K., J. Francis, S. N. George, S. George, and B. M. Devassy. 2025. “Early Bruise Detection, Classification and Prediction in Strawberry Using Vis‐NIR Hyperspectral Imaging.” Food Control 167: 110794. https://doi.org/10.1016/j.foodcont.2024.110794.
Shen, C., R. Wang, H. Nawazish, B. Wang, K. Cai, and B. Xu. 2024. “Machine Vision Combined With Deep Learning‐Based Approaches for Food Authentication: an Integrative Review and New Insights.” Comprehensive Reviews in Food Science and Food Safety 23: e70054. https://doi.org/10.1111/1541‐4337.70054.
Shen, S., C. Zhan, C. Yang, A. R. Fernie, and J. Luo. 2023. “Metabolomics‐Centered Mining of Plant Metabolic Diversity and Function: Past Decade and Future Perspectives.” Molecular Plant 16: 43–63. https://doi.org/10.1016/j.molp.2022.09.007.
Shen, W., J. Pan, G. Wang, and X. Li. 2021. “Deep Learning‐Based Prediction of TFBSs in Plants.” Trends in Plant Science 26: 1301–1302. https://doi.org/10.1016/j.tplants.2021.06.016.
Shi, H., W. Ma, Z. Xu, and P. Lin. 2023. “A Novel Integrated Strategy of Easy Pruning, Parameter Searching, and Re‐Parameterization for Lightweight Intelligent Lithology Identification.” Expert Systems with Applications 231: 120657. https://doi.org/10.1016/j.eswa.2023.120657.
Shu, X., and G. Zhao. 2021. “Scalable Multi‐Label Canonical Correlation Analysis for Cross‐modal Retrieval.” Pattern Recognition 115: 107905. https://doi.org/10.1016/j.patcog.2021.107905.
Shuprajhaa, T., J. M. Raj, S. K. Paramasivam, K. Sheeba, and S. Uma. 2023. “Deep Learning Based Intelligent Identification System for Ripening Stages of Banana.” Postharvest Biology and Technology 203: 112410. https://doi.org/10.1016/j.postharvbio.2023.112410.
Simon, A., M. Singh Deo, V. Selvam, and R. Babu. 2016. “An Overview of Machine Learning and Its Applications.” International Journal of Electrical Sciences Electrical Sciences & Engineering (IJESE) 1: 22–24.
Singh, R. 2022. “Intrusion Bit Detection in Data Packet Transmitted Over Covert Channels Using K‐NN Machine Learning Algorithm.” International Journal for Research in Applied Science & Engineering Technology (IJRASET) 10: 1850–1854. https://doi.org/10.22214/ijraset.2022.46940.
Sinha, A., and D. Das. 2023. “SNRepair: Systematically Addressing Sensor Faults and Self‐Calibration in IoT Networks.” IEEE Sensors Journal 23: 14915–14922. https://doi.org/10.1109/JSEN.2023.3277493.
Springmann, M., M. Clark, D. Mason‐D'Croz, et al. 2018. “Options for Keeping the Food System Within Environmental Limits.” Nature 562: 519–525. https://doi.org/10.1038/s41586‐018‐0594‐0.
Stathers, T., D. Holcroft, L. Kitinoja, et al. 2020. “A Scoping Review of Interventions for Crop Postharvest Loss Reduction in Sub‐Saharan Africa and South Asia.” Nature Sustainability 3: 821–835. https://doi.org/10.1038/s41893‐020‐00622‐1.
Stocchero, M., M. De Nardi, and B. Scarpa. 2021. “PLS for Classification.” Chemometrics and Intelligent Laboratory Systems 216: 104374. https://doi.org/10.1016/j.chemolab.2021.104374.
Suryavanshi, A., R. Thakker, H. Waghela, and P. Kamble. 2022. “A SMART IOT and CLOUD Based Food Monitoring System: Review.” In 2022 5th International Conference on Advances in Science and Technology (ICAST), 508–512. IEEE.
Sutton, R. S. 1992. “Introduction: The Challenge of Reinforcement Learning.” In Reinforcement Learning, edited by R. S. Sutton, 1–3. Springer.
Tian, X., L. Zhu, N. Yang, et al. 2021. “Proteomics and Metabolomics Reveal the Regulatory Pathways of Ripening and Quality in Post‐Harvest Kiwifruits.” Journal of Agricultural and Food Chemistry 69: 824–835. https://doi.org/10.1021/acs.jafc.0c05492.
Tilman, D., and M. Clark. 2014. “Global Diets Link Environmental Sustainability and Human Health.” Nature 515: 518–522. https://doi.org/10.1038/nature13959.
Tirkolaee, E. B., S. Sadeghi, F. M. Mooseloo, H. R. Vandchali, and S. Aeini. 2021. “Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas.” Mathematical Problems in Engineering 2021: 1476043. https://doi.org/10.1155/2021/1476043.
Truong Minh Long, N., and N. Truong Thinh. 2020. “Using Machine Learning to Grade the Mango's Quality Based on External Features Captured by Vision System.” Applied Sciences 10: 5775. https://doi.org/10.3390/app10175775.
Tuly, S. S., M. Mahiuddin, and A. Karim. 2023. “Mathematical Modeling of Nutritional, Color, Texture, and Microbial Activity Changes in Fruit and Vegetables During Drying: A Critical Review.” Critical Reviews in Food Science and Nutrition 63: 1877–1900. https://doi.org/10.1080/10408398.2021.1969533.
Urugo, M. M., T. A. Teka, H. F. Gemede, et al. 2024. “A Comprehensive Review of Current Approaches on Food Waste Reduction Strategies.” Comprehensive Reviews in Food Science and Food Safety 23: e70011. https://doi.org/10.1111/1541‐4337.70011.
Vallese, F. D., S. G. Paoloni, V. Springer, D. D. D. S. Fernandes, P. H. G. D. Diniz, and M. F. Pistonesi. 2024. “Exploiting the Successive Projections Algorithm to Improve the Quantification of Chemical Constituents and Discrimination of Botanical Origin of Argentinean Bee‐Pollen.” Journal of Food Composition and Analysis 126: 105925. https://doi.org/10.1016/j.jfca.2023.105925.
van Dijk, A. D. J., G. Kootstra, W. Kruijer, and D. de Ridder. 2021. “Machine Learning in Plant Science and Plant Breeding.” Iscience 24: 101890. https://doi.org/10.1016/j.isci.2020.101890.
Van Engelen, J. E., and H. H. Hoos. 2020. “A Survey on Semi‐Supervised Learning.” Machine Learning 109: 373–440. https://doi.org/10.1007/s10994‐019‐05855‐6.
Vithu, P., and J. Moses. 2016. “Machine Vision System for Food Grain Quality Evaluation: A Review.” Trends in Food Science & Technology 56: 13–20. https://doi.org/10.1016/j.tifs.2016.07.011.
Wang, C., J. Du, D. Hou, et al. 2023. “Quality Retention and Delay Postharvest Senescence of Figs (Ficus carica L.) Using 1‐Methylcyclopropene and Modified Atmosphere Packaging During Cold Storage.” Food Bioscience 53: 102748. https://doi.org/10.1016/j.fbio.2023.102748.
Wang, D., M. Zhang, M. Li, and J. Lin. 2024. “Fruits and Vegetables Preservation Based on AI Technology: Research Progress and Application Prospects.” Computers and Electronics in Agriculture 226: 109382. https://doi.org/10.1016/j.compag.2024.109382.
Wang, D., M. Zhang, A. S. Mujumdar, and D. Yu. 2022. “Advanced Detection Techniques Using Artificial Intelligence in Processing of Berries.” Food Engineering Reviews 14: 176–199. https://doi.org/10.1007/s12393‐021‐09298‐5.
Wang, G., Y. Guo, Y. Yu, Y. Shi, Y. Ying, and H. Men. 2025. “ColorNet: an AI‐Based Framework for Pork Freshness Detection Using a Colorimetric Sensor Array.” Food Chemistry 471: 142794. https://doi.org/10.1016/j.foodchem.2025.142794.
Wang, J., Y. Wu, S. Li, and F. Nie. 2023. “A Self‐Training Algorithm Based on the Two‐Stage Data Editing Method With Mass‐Based Dissimilarity.” Neural Networks 168: 431–449. https://doi.org/10.1016/j.neunet.2023.09.046.
Wang, X., F. Li, L. Wei, et al. 2024. “Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning‐Assisted Multiplex SERS Fingerprinting.” Analytical Chemistry 96: 4682–4692. https://doi.org/10.1021/acs.analchem.4c00064.
Wang, Y., M. Li, W. Liu, and L. Jiang. 2025. “Illuminating the Future of Food Microbial Control: From Optical Tools to Optogenetic Tools.” Food Chemistry 471: 142474. https://doi.org/10.1016/j.foodchem.2024.142474.
Wu, L., Z. Luo, Y. Shi, et al. 2022. “A Cost‐Effective tsCUT&Tag Method for Profiling Transcription Factor Binding Landscape.” Journal of Integrative Plant Biology 64: 2033–2038. https://doi.org/10.1111/jipb.13354.
Xia, J., W. Huang, X. Wang, Z. Zhu, M. Zhang, and X. Zhang. 2023. “Flexible Sensing Technology for Fruit Quality Control in the Cold Chain: Characterization, Application, and Improvement.” Food Control 154: 109976. https://doi.org/10.1016/j.foodcont.2023.109976.
Xie, Q., Z. Wang, Y. Fang, and Y. Li. 2025. “MABQN: Multi‐Agent Reinforcement Learning Algorithm With Discrete Policy.” Neurocomputing 626: 129552. https://doi.org/10.1016/j.neucom.2025.129552.
Xue, L., X. Liu, S. Lu, et al. 2021. “China's Food Loss and Waste Embodies Increasing Environmental Impacts.” Nature Food 2: 519–528. https://doi.org/10.1038/s43016‐021‐00317‐6.
Yadav, A., N. Kumar, A. Upadhyay, S. Sethi, and A. Singh. 2022. “Edible Coating as Postharvest Management Strategy for Shelf‐Life Extension of Fresh Tomato (Solanum lycopersicum L.): An Overview.” Journal of Food Science 87: 2256–2290. https://doi.org/10.1111/1750‐3841.16145.
Yan, J., and X. Wang. 2023. “Machine Learning Bridges Omics Sciences and Plant Breeding.” Trends in Plant Science 28: 199–210. https://doi.org/10.1016/j.tplants.2022.08.018.
Yang, F., R. Zhao, J. Suo, et al. 2024. “Understanding Quality Differences Between Kiwifruit Varieties During Softening.” Food Chemistry 430: 136983. https://doi.org/10.1016/j.foodchem.2023.136983.
Yang, M., S. Chen, Z. Huang, et al. 2023. “Deep Learning‐Enabled Discovery and Characterization of HKT Genes in Spartina Alterniflora.” The Plant Journal 116: 690–705. https://doi.org/10.1111/tpj.16397.
Yang, Y., O. Jia, Y. Li, et al. 2024. “Effect of High CO2 Controlled Atmosphere Storage on Postharvest Quality of Button Mushroom (Agaricus bisporus).” Foods 13: 3486. https://doi.org/10.3390/foods13213486.
Yang, Y., M. A. Saand, L. Huang, et al. 2021. “Applications of Multi‐Omics Technologies for Crop Improvement.” Frontiers in Plant Science 12: 563953. https://doi.org/10.3389/fpls.2021.563953.
Yao, Z., X. Zhang, P. Nie, et al. 2023. “Identification of Milk Adulteration in Camel Milk Using FT‐Mid‐Infrared Spectroscopy and Machine Learning Models.” Foods 12: 4517. https://doi.org/10.3390/foods12244517.
Yu, X., X. Zhang, X. Liu, et al. 2024. “Comparative Transcriptomic Profile of Two Mandarin Varieties During Maturation Reveals Pectinase Regulating Peelability.” Scientia Horticulturae 331: 113148. https://doi.org/10.1016/j.scienta.2024.113148.
Yun, Z., H. Gao, and Y. Jiang. 2022. “Insights Into Metabolomics in Quality Attributes of Postharvest Fruit.” Current Opinion in Food Science 45: 100836. https://doi.org/10.1016/j.cofs.2022.100836.
Yun, Z., T. Li, H. Gao, et al. 2019. “Integrated Transcriptomic, Proteomic, and Metabolomics Analysis Reveals Peel Ripening of Harvested Banana Under Natural Condition.” Biomolecules 9: 167. https://doi.org/10.3390/biom9050167.
Zaguia, A. 2023. “Smart Greenhouse Management System With Cloud‐Based Platform and IoT Sensors.” Spatial Information Research 31: 559–571. https://doi.org/10.1007/s41324‐023‐00523‐3.
Zareef, M., M. Arslan, M. M. Hassan, et al. 2021. “Recent Advances in Assessing Qualitative and Quantitative Aspects of Cereals Using Nondestructive Techniques: A Review.” Trends in Food Science & Technology 116: 815–828. https://doi.org/10.1016/j.tifs.2021.08.012.
Zhan, W., X. Yang, G. Lu, Y. Deng, and L. Yang. 2022. “A Rapid Quality Grade Discrimination Method for Gastrodia elata Powderusing ATR‐FTIR and Chemometrics.” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 264: 120189. https://doi.org/10.1016/j.saa.2021.120189.
Zhang, L., H. Song, N. Aletras, and H. Lu. 2022. “Node‐Feature Convolution for Graph Convolutional Networks.” Pattern Recognition 128: 108661. https://doi.org/10.1016/j.patcog.2022.108661.
Zhang, X., and C.‐A. Liu. 2023. “Model Averaging Prediction by K‐Fold Cross‐Validation.” Journal of Econometrics 235: 280–301. https://doi.org/10.1016/j.jeconom.2022.04.007.
Zhang, Z., M. Gao, L. Zhang, et al. 2025. “Smartphone‐assisted Fluorescent Film Based on the Flu Grafted on Eu‐MOF for Real‐Time Monitoring of Fresh‐Cut Fruit Freshness.” Biosensors and Bioelectronics 277: 117278. https://doi.org/10.1016/j.bios.2025.117278.
Zhang, Z., D. Liu, B. Li, et al. 2024. “A k‐mer‐Based Pangenome Approach for Cataloging Seed‐Storage‐Protein Genes in Wheat to Facilitate Genotype‐to‐Phenotype Prediction and Improvement of End‐Use Quality.” Molecular Plant 17: 1038–1053. https://doi.org/10.1016/j.molp.2024.05.006.
Zheng, J., X. Yang, Y. Huang, S. Yang, S. Wuchty, and Z. Zhang. 2023. “Deep Learning‐Assisted Prediction of Protein‐Protein Interactions in Arabidopsis thaliana.” The Plant Journal 114: 984–994. https://doi.org/10.1111/tpj.16188.
Zhou, F., and C. Wen. 2023. “Research on the Level of Agricultural Green Development, Regional Disparities, and Dynamic Distribution Evolution in China From the Perspective of Sustainable Development.” Agriculture 13: 1441. https://doi.org/10.3390/agriculture13071441.
Zhou, L., S. Pan, J. Wang, and A. V. Vasilakos. 2017. “Machine Learning on Big Data: Opportunities and Challenges.” Neurocomputing 237: 350–361. https://doi.org/10.1016/j.neucom.2017.01.026.
Zhou, L., C. Zhang, F. Liu, Z. Qiu, and Y. He. 2019. “Application of Deep Learning in Food: A Review.” Comprehensive Reviews in Food Science and Food Safety 18: 1793–1811. https://doi.org/10.1111/1541‐4337.12492.
Zhu, M., Y. Gong, C. Tian, and Z. Zhu. 2024. “A Systematic Survey of Transformer‐based 3D Object Detection for Autonomous Driving: Methods, Challenges and Trends.” Drones 8: 412. https://doi.org/10.3390/drones8080412.
Zhu, Y., S. Chen, H. Yin, et al. 2024. “Classification of Oolong Tea Varieties Based on Computer Vision and Convolutional Neural Networks.” Journal of the Science of Food and Agriculture 104: 1630–1637. https://doi.org/10.1002/jsfa.13049.
Zou, Z., T. Long, Q. Wang, et al. 2022. “Implementation of Apple's Automatic Sorting System Based on Machine Learning.” Food Science and Technology 42: e24922. https://doi.org/10.1590/fst.24922.
Zuo, Z., P. Jiang, D. Chen, et al. 2024. “Improving the Storage Quality and Antioxidant Capacity of Postharvest Winter Jujube by Laser Microporous Modified Atmosphere Packaging.” Scientia Horticulturae 337: 113477. https://doi.org/10.1016/j.scienta.2024.113477.
Adedeji, A. A., N. Ekramirad, A. Rady, et al. 2020. “Non‐Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables Under Postharvest Conditions: A Critical Review.” Foods 9: 927. https://doi.org/10.3390/foods9070927.
Ahmad, S., I. Shakeel, S. Mehfuz, and J. Ahmad. 2023. “Deep Learning Models for Cloud, Edge, Fog, and IoT Computing Paradigms: Survey, Recent Advances, and Future Directions.” Computer Science Review 49: 100568. https://doi.org/10.1016/j.cosrev.2023.100568.
Akhtar, N., A. J. Siddiqui, M. Ramzan, et al. 2024. “Investigation of Pharmacologically Important Polyphenolic Secondary Metabolites in Plant‐Based Food Samples Using HPLC‐DAD.” Plants 13: 1311. https://doi.org/10.3390/plants13101311.
Al‐Dairi, M., P. B. Pathare, R. Al‐Yahyai, H. Jayasuriya, and Z. Al‐Attabi. 2023. “Postharvest Quality, Technologies, and Strategies to Reduce Losses Along the Supply Chain of Banana: A Review.” Trends in Food Science & Technology 134: 177–191. https://doi.org/10.1016/j.tifs.2023.03.003.
Aloe, C. M., and A. De Maio. 2025. “Balancing Temperature and Humidity Control in Storage Location Assignment: An Optimization Perspective in Refrigerated Warehouses.” Sustainability 17: 7477. https://doi.org/10.3390/su17167477.
Al Sahili, Z., and M. Awad. 2022. “The Power of Transfer Learning in Agricultural Applications: AgriNet.” Frontiers in Plant Science 13: 1–12. https://doi.org/10.3389/fpls.2022.992700.
Amini, P., and M. Khashei. 2019. “A Soft Intelligent Allocation‐based Hybrid Model for Uncertain Complex Time Series Forecasting.” Applied Soft Computing 84: 105736. https://doi.org/10.1016/j.asoc.2019.105736.
An, J.‐P., J. Li, K. Rodrigues‐Stuart, M. M. Dewdney, M. A. Ritenour, and Y. Wang. 2024. “Machine Learning‐Based Metabolomics Analysis Reveals the Early Biomarkers for Diplodia Stem‐end rot in Grapefruit Caused by Lasiodiplodia Theobromae.” Postharvest Biology and Technology 212: 112868. https://doi.org/10.1016/j.postharvbio.2024.112868.
Anusha, K., K. Uma, K. Jayasri, S. Kambham, and S. D. Dandamudi. 2024. “IoT Based Food Spoilage Detection Using Machine Learning Techniques.” In 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). IEEE.
Balasubramaniam, N., M. Kauppinen, A. Rannisto, K. Hiekkanen, and S. Kujala. 2023. “Transparency and Explainability of AI Systems: From Ethical Guidelines to Requirements.” Information and Software Technology 159: 107197. https://doi.org/10.1016/j.infsof.2023.107197.
Ballard, Z., C. Brown, A. M. Madni, and A. Ozcan. 2021. “Machine Learning and Computation‐Enabled Intelligent Sensor Design.” Nature Machine Intelligence 3: 556–565. https://doi.org/10.1038/s42256‐021‐00360‐9.
Beiran, M., C. A. Spencer‐Salmon, and K. Rajan. 2023. “A ‘Programming’ Framework for Recurrent Neural Networks.” Nature Machine Intelligence 5: 570–571. https://doi.org/10.1038/s42256‐023‐00674‐w.
Belay, Z. A., and O. J. Caleb. 2022. “Role of Integrated Omics in Unravelling Fruit Stress and Defence Responses During Postharvest: A Review.” Food Chemistry: Molecular Sciences 5: 100118. https://doi.org/10.1016/j.fochms.2022.100118.
Bergmeir, C., and J. M. Benítez. 2012. “On the Use of Cross‐Validation for Time Series Predictor Evaluation.” Information Sciences 191: 192–213. https://doi.org/10.1016/j.ins.2011.12.028.
Bhargava, A., A. Bansal, and V. Goyal. 2022. “Machine Learning‐Based Detection and Sorting of Multiple Vegetables and Fruits.” Food Analytical Methods 15: 228–242. https://doi.org/10.1007/s12161‐021‐02086‐1.
Brink, S. C. 2023. “Food Security: Solutions Offered by Plant Science.” Trends in Plant Science 28: 489–490. https://doi.org/10.1016/j.tplants.2023.03.012.
Brown, D., I. Van den Bergh, S. de Bruin, L. Machida, and J. van Etten. 2020. “Data Synthesis for Crop Variety Evaluation: A Review.” Agronomy for Sustainable Development 40: 25. https://doi.org/10.1007/s13593‐020‐00630‐7.
Bu, Y., J. Hu, C. Chen, et al. 2024. “ResNet Incorporating the Fusion Data of RGB & Hyperspectral Images Improves Classification Accuracy of Vegetable Soybean Freshness.” Scientific Reports 14: 2568. https://doi.org/10.1038/s41598‐024‐51668‐6.
Cabitza, F., A. Campagner, F. Soares, et al. 2021. “The Importance of Being External: Methodological Insights for the External Validation of Machine Learning Models in Medicine.” Computer Methods and Programs in Biomedicine 208: 106288. https://doi.org/10.1016/j.cmpb.2021.106288.
Cawley, G. C., and N. L. C. Talbot. 2010. “On Over‐fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation.” Journal of Machine Learning Research 11: 2079–2107. https://doi.org/10.5555/1756006.1859921.
Cembrowska‐Lech, D., A. Krzemińska, T. Miller, et al. 2023. “An Integrated Multi‐Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture.” Biology 12: 1298. https://doi.org/10.3390/biology12101298.
Chakraborty, S. K., A. Subeesh, K. Dubey, et al. 2023. “Development of an Optimally Designed Real‐Time Automatic Citrus Fruit Grading‐sorting Machine Leveraging Computer Vision‐Based Adaptive Deep Learning Model.” Engineering Applications of Artificial Intelligence 120: 105826. https://doi.org/10.1016/j.engappai.2023.105826.
Chaudhari, D., and S. Waghmare. 2021. “Machine Vision Based Fruit Classification and Grading—A Review.” In The 4th International Conference on Communications and Cyber Physical Engineering (ICCCE). IEEE.
Chavan, P., K. Lata, T. Kaur, et al. 2023. “Recent Advances in the Preservation of Postharvest Fruits Using Edible Films and Coatings: A Comprehensive Review.” Food Chemistry 418: 135916. https://doi.org/10.1016/j.foodchem.2023.135916.
Chazette, L., and K. Schneider. 2020. “Explainability as a Non‐Functional Requirement: Challenges and Recommendations.” Requirements Engineering 25: 493–514. https://doi.org/10.1007/s00766‐020‐00333‐1.
Chen, S., P. Wang, H. Guo, and Y. Zhang. 2024. “Deciphering Gene Expression Patterns Using Large‐Scale Transcriptomic Data and Its Applications.” Briefings in Bioinformatics 25: bbae590. https://doi.org/10.1093/bib/bbae590.
Czerwiński, K., T. Rydzkowski, J. Wróblewska‐Krepsztul, and V. K. Thakur. 2021. “Towards Impact of Modified Atmosphere Packaging (MAP) on Shelf‐life of Polymer‐Film‐Packed Food Products: Challenges and Sustainable Developments.” Coatings 11: 1504. https://doi.org/10.3390/coatings11121504.
De, S., S. Banerjee, and S. Banerjee. 2024. “Managing Postharvest Losses of Vegetables and Fruits: A Methodological Review.” Recent Advances in Food, Nutrition & Agriculture 15: 138–162. https://doi.org/10.2174/012772574x280698231221203313.
de Araújo Gomes, A., S. M. Azcarate, P. H. G. D. Diniz, D. D. de Sousa Fernandes, and G. Veras. 2022. “Variable Selection in the Chemometric Treatment of Food Data: A Tutorial Review.” Food Chemistry 370: 131072. https://doi.org/10.1016/j.foodchem.2021.131072.
Deekshith, A. 2021. “Data Engineering for AI: Optimizing Data Quality and Accessibility for Machine Learning Models.” International Journal of Management Education for Sustainable Development 4: 1–33.
de Jongh, R. P. H., A. D. J. van Dijk, M. K. Julsing, P. J. Schaap, and D. de Ridder. 2020. “Designing Eukaryotic Gene Expression Regulation Using Machine Learning.” Trends in Biotechnology 38: 191–201. https://doi.org/10.1016/j.tibtech.2019.07.007.
Deng, X., S. Cao, and A. L. Horn. 2021. “Emerging Applications of Machine Learning in Food Safety.” Annual Review of Food Science and Technology 12: 513–538. https://doi.org/10.1146/annurev‐food‐071720‐024112.
de Oliveira, A. N., S. R. F. Bolognini, L. C. Navarro, et al. 2023. “Tomato Classification Using Mass Spectrometry‐Machine Learning Technique: A Food Safety‐Enhancing Platform.” Food Chemistry 398: 133870. https://doi.org/10.1016/j.foodchem.2022.133870.
Devarajan, G. G., S. M. Nagarajan, T.V., R. V. T, U. Ghosh, and W. Alnumay. 2023. “DDNSAS: Deep Reinforcement Learning Based Deep Q‐Learning Network for Smart Agriculture System.” Sustainable Computing: Informatics and Systems 39: 100890. https://doi.org/10.1016/j.suscom.2023.100890.
Dey, A. 2016. “Machine Learning Algorithms: A Review.” International Journal of Computer Science and Information Technologies 7: 1174–1179.
Dhal, S. B., and D. Kar. 2025. “Leveraging Artificial Intelligence and Advanced Food Processing Techniques for Enhanced Food Safety, Quality, and Security: A Comprehensive Review.” Discover Applied Sciences 7: 75. https://doi.org/10.1007/s42452‐025‐06472‐w.
Dhall, D., R. Kaur, and M. Juneja. 2020. “Machine Learning: A Review of the Algorithms and Its Applications.” In Proceedings of ICRIC 2019: Recent Innovations in Computing, 47–63. Springer. https://doi.org/10.1007/978‐3‐030‐29407‐6_5.
Dickinson, E., M. J. Rusilowicz, M. Dickinson, et al. 2018. “Integrating Transcriptomic Techniques and k‐means Clustering in Metabolomics to Identify Markers of Abiotic and Biotic Stress in Medicago Truncatula.” Metabolomics 14: 126. https://doi.org/10.1007/s11306‐018‐1424‐y.
Do, E., M. Kim, D.‐Y. Ko, M. Lee, C. Lee, and K.‐M. Ku. 2024. “Machine Learning for Storage Duration Based on Volatile Organic Compounds Emitted From 'Jukhyang' and 'Merry Queen' Strawberries During Post‐Harvest Storage.” Postharvest Biology and Technology 211: 112808. https://doi.org/10.1016/j.postharvbio.2024.112808.
Doan, T.‐N., and D.‐N. Le‐Thi. 2023. “A Novel Mango Grading System Based on Image Processing and Machine Learning Methods.” International Journal of Advanced Computer Science and Applications 14: 1118–1129. https://doi.org/10.14569/IJACSA.2023.01405115.
Du, Y., Q. Tian, G. Li, J. Yi, X. Hu, and Y. Jiang. 2024. “Advanced Application of Slightly Acidic Electrolyzed Water for Fresh‐Cut Fruits and Vegetables Preservation.” Food Research International 195: 114996. https://doi.org/10.1016/j.foodres.2024.114996.
Duan, H., Z. Peng, L. Xiang, Y. Hu, and B. Li. 2024. “A Verifiable and Privacy‐Preserving Federated Learning Training Framework.” IEEE Transactions on Dependable and Secure Computing 21: 5046–5058. https://doi.org/10.1109/TDSC.2024.3369658.
Duan, Y., G.‐B. Wang, O. A. Fawole, et al. 2020. “Postharvest Precooling of Fruit and Vegetables: A Review.” Trends in Food Science & Technology 100: 278–291. https://doi.org/10.1016/j.tifs.2020.04.027.
El‐Mesery, H. S., O. A. Adelusi, S. Ghashi, P. B. Njobeh, Z. Hu, and W. Kun. 2024. “Effects of Storage Conditions and Packaging Materials on the Postharvest Quality of Fresh Chinese Tomatoes and the Optimization of the Tomatoes' physiochemical Properties Using Machine Learning Techniques.” LWT 201: 116280. https://doi.org/10.1016/j.lwt.2024.116280.
Farah, J. S., R. N. Cavalcanti, J. T. Guimarães, et al. 2021. “Differential Scanning Calorimetry Coupled With Machine Learning Technique: An Effective Approach to Determine the Milk Authenticity.” Food Control 121: 107585. https://doi.org/10.1016/j.foodcont.2020.107585.
Farid, D. M., L. Zhang, C. M. Rahman, M. A. Hossain, and R. Strachan. 2014. “Hybrid Decision Tree and Naïve Bayes Classifiers for Multi‐Class Classification Tasks.” Expert Systems with Applications 41: 1937–1946. https://doi.org/10.1016/j.eswa.2013.08.089.
Fashi, M., L. Naderloo, and H. Javadikia. 2019. “The Relationship Between the Appearance of Pomegranate Fruit and Color and Size of Arils Based on Image Processing.” Postharvest Biology and Technology 154: 52–57. https://doi.org/10.1016/j.postharvbio.2019.04.017.
Fu, J., Z. Zhang, and D. Lyu. 2019. “Research and Application of Information Service Platform for Agricultural Economic Cooperation Organization Based on Hadoop Cloud Computing Platform Environment: Taking Agricultural and Fresh Products as an Example.” Cluster Computing 22: 14689–14700. https://doi.org/10.1007/s10586‐018‐2380‐z.
Gao, C. 2021. “Genome Engineering for Crop Improvement and Future Agriculture.” Cell 184: 1621–1635. https://doi.org/10.1016/j.cell.2021.01.005.
Gao, Y., S. Zhang, T. Aili, et al. 2022. “Dual Signal Light Detection of Beta‐Lactoglobulin Based on a Porous Silicon Bragg Mirror.” Biosensors and Bioelectronics 204: 114035. https://doi.org/10.1016/j.bios.2022.114035.
Gardezi, M., B. Joshi, D. M. Rizzo, et al. 2024. “Artificial Intelligence in Farming: Challenges and Opportunities for Building Trust.” Agronomy Journal 116: 1217–1228. https://doi.org/10.1002/agj2.21353.
Garg, S., N. P. Rumjit, and S. Roy. 2024. “Smart Agriculture and Nanotechnology: Technology, Challenges, and New Perspective.” Advanced Agrochem 3: 115–125. https://doi.org/10.1016/j.aac.2023.11.001.
Gaye, B., D. Zhang, and A. Wulamu. 2021. “Improvement of Support Vector Machine Algorithm in Big Data Background.” Mathematical Problems in Engineering 2021: 5594899. https://doi.org/10.1155/2021/5594899.
Ghaani, M., C. A. Cozzolino, G. Castelli, and S. Farris. 2016. “An Overview of the Intelligent Packaging Technologies in the Food Sector.” Trends in Food Science and Technology 51: 1–11. https://doi.org/10.1016/j.tifs.2016.02.008.
Gong, X., J. Huang, Y. Xu, et al. 2023. “Deterioration of Plant Volatile Organic Compounds in Food: Consequence, Mechanism, Detection, and Control.” Trends in Food Science & Technology 131: 61–76. https://doi.org/10.1016/j.tifs.2022.11.022.
Goyal, R., P. Singha, and S. K. Singh. 2024. “Spectroscopic Food Adulteration Detection Using Machine Learning: Current Challenges and Future Prospects.” Trends in Food Science & Technology 146: 104377. https://doi.org/10.1016/j.tifs.2024.104377.
Gu, J., Z. Wang, J. Kuen, et al. 2018. “Recent Advances in Convolutional Neural Networks.” Pattern Recognition 77: 354–377. https://doi.org/10.1016/j.patcog.2017.10.013.
Guo, J., X. Wei, E. Lü, Y. Wang, and Z. Deng. 2020. “Ripening Behavior and Quality of 1‐MCP Treated d'Anjou Pears During Controlled Atmosphere Storage.” Food Control 117: 107364. https://doi.org/10.1016/j.foodcont.2020.107364.
Gustavsson, J., C. Cederberg, U. Sonesson, R. V. Otterdijk, and A. Meybeck. 2011. Global Food Losses and Food Waste: Extent, Causes and Prevention. FAO.
Gutiérrez‐Pacheco, M. M., L. A. Ortega‐Ramírez, B. A. Silva‐Espinoza, et al. 2020. “Individual and Combined Coatings of Chitosan and Carnauba Wax With Oregano Essential Oil to Avoid Water Loss and Microbial Decay of Fresh Cucumber.” Coatings 10: 614. https://doi.org/10.3390/coatings10070614.
Habibi, F., D. A. Boakye, Y. Chang, et al. 2024. “Molecular Mechanisms Underlying Postharvest Physiology and Metabolism of Fruit and Vegetables Through Multi‐Omics Technologies.” Scientia Horticulturae 324: 112562. https://doi.org/10.1016/j.scienta.2023.112562.
Hadish, J. A., H. L. Hargarten, H. Zhang, J. P. Mattheis, L. A. Honaas, and S. P. Ficklin. 2024. “Towards Identification of Postharvest Fruit Quality Transcriptomic Markers in Malus domestica.” PLoS ONE 19: e0297015. https://doi.org/10.1371/journal.pone.0297015.
He, X., Y. Pu, L. Chen, et al. 2023. “A Comprehensive Review of Intelligent Packaging for Fruits and Vegetables: Target Responders, Classification, Applications, and Future Challenges.” Comprehensive Reviews in Food Science and Food Safety 22: 842–881. https://doi.org/10.1111/1541‐4337.13093.
He, Y., B. Fan, L. Sun, et al. 2023. “Rapid Appearance Quality of Rice Based on Machine Vision and Convolutional Neural Network Research on Automatic Detection System.” Frontiers in Plant Science 14: 1190591. https://doi.org/10.3389/fpls.2023.1190591.
Homma, F., J. Lyu, and R. A. van der Hoorn. 2024. “Using AlphaFold Multimer to Discover Interkingdom Protein–Protein Interactions.” The Plant Journal 120: 19–28. https://doi.org/10.1111/tpj.16969.
Hong, H., M. F. Rizzi, D. Wang, L. McLandsborough, and J. Lu. 2024. “A Meta‐analysis on the Antimicrobial Effectiveness of Ozonated Water Treatments for Fresh Produce Washing—Effect of Ozonation Methods.” Foods 13: 3906. https://doi.org/10.3390/foods13233906.
Hongyang, T., H. Daming, H. Xingyi, et al. 2021. “Detection of Browning of Fresh‐Cut Potato Chips Based on Machine Vision and Electronic Nose.” Journal of Food Process Engineering 44: e13631. https://doi.org/10.1111/jfpe.13631.
Hu, S., H. Li, C. Chen, et al. 2022. “Raman Spectroscopy Combined With Machine Learning Algorithms to Detect Adulterated Suichang Native Honey.” Scientific Reports 12: 3456. https://doi.org/10.1038/s41598‐022‐07222‐3.
Huang, J., Q. Lin, H. Fei, et al. 2023. “Discovery of Deaminase Functions by Structure‐Based Protein Clustering.” Cell 186: 3182–3195. https://doi.org/10.1016/j.cell.2023.05.041.
Iftikhar, S., N. Anjum, A. B. Siddiqui, M. Ur Rehman, and N. Ramzan. 2025. “Explainable CNN for Brain Tumor Detection and Classification Through XAI Based Key Features Identification.” Brain Informatics 12: 10. https://doi.org/10.1186/s40708‐025‐00257‐y.
Iraji, M. S. 2019. “Comparison Between Soft Computing Methods for Tomato Quality Grading Using Machine Vision.” Journal of Food Measurement and Characterization 13: 1–15. https://doi.org/10.1007/s11694‐018‐9913‐2.
Janiesch, C., P. Zschech, and K. Heinrich. 2021. “Machine Learning and Deep Learning.” Electronic Markets 31: 685–695. https://doi.org/10.1007/s12525‐021‐00475‐2.
Jayasudha, M., M. Elangovan, M. Mahdal, and J. Priyadarshini. 2022. “Accurate Estimation of Tensile Strength of 3D Printed Parts Using Machine Learning Algorithms.” Processes 10: 1158. https://doi.org/10.3390/pr10061158.
Jin, X., C. Liu, T. Xu, L. Su, and X. Zhang. 2020. “Artificial Intelligence Biosensors: Challenges and Prospects.” Biosensors and Bioelectronics 165: 112412. https://doi.org/10.1016/j.bios.2020.112412.
Jin, Z., and Z. Wei. 2024. “Molecular Simulation for Food Protein–ligand Interactions: a Comprehensive Review on Principles, Current Applications, and Emerging Trends.” Comprehensive Reviews in Food Science and Food Safety 23: e13280. https://doi.org/10.1111/1541‐4337.13280.
Kaliappan, J., A. R. Bagepalli, S. Almal, R. Mishra, Y.‐C. Hu, and K. Srinivasan. 2023. “Impact of Cross‐validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise.” Diagnostics 13: 1692. https://doi.org/10.3390/diagnostics13101692.
Kalpana, S., S. R. Priyadarshini, M. Maria Leena, J. A. Moses, and C. Anandharamakrishnan. 2019. “Intelligent Packaging: Trends and Applications in Food Systems.” Trends in Food Science & Technology 93: 145–157. https://doi.org/10.1016/j.tifs.2019.09.008.
Kang, Z., Y. Zhao, L. Chen, Y. Guo, Q. Mu, and S. Wang. 2022. “Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain.” Food Engineering Reviews 14: 596–616. https://doi.org/10.1007/s12393‐022‐09322‐2.
Karmaker, S. K., M. M. Hassan, M. J. Smith, L. Xu, C. Zhai, and K. Veeramachaneni. 2021. “Automl to Date and Beyond: Challenges and Opportunities.” ACM Computing Surveys 54: 1–36. https://doi.org/10.1145/3470918.
Kelchtermans, P., W. Bittremieux, K. De Grave, et al. 2014. “Machine Learning Applications in Proteomics Research: How the Past Can Boost the Future.” Proteomics 14: 353–366. https://doi.org/10.1002/pmic.201300289.
Khan, M. I. H., S. S. Sablani, R. Nayak, and Y. Gu. 2022. “Machine Learning‐based Modeling in Food Processing Applications: State of the Art.” Comprehensive Reviews in Food Science and Food Safety 21: 1409–1438. https://doi.org/10.1111/1541‐4337.12912.
Koleini, S., B. Pahlevanzadeh, and I. Shiraz. 2024. “Enhancing High‐Performance Computing (HPC) Security: A Comprehensive Review of Detection and Protection Strategies.” Journal of Distributed Computing and Systems 6: 12–24.
Kotsiantis, S. B., I. Zaharakis, and P. Pintelas. 2007. “Supervised Machine Learning: A Review of Classification techniques.” In Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems With Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies. IOS Press.
Kotsiantis, S. B., I. D. Zaharakis, and P. E. Pintelas. 2006. “Machine Learning: A Review of Classification and Combining Techniques.” Artificial Intelligence Review 26: 159–190. https://doi.org/10.1007/s10462‐007‐9052‐3.
Koyama, K., M. Tanaka, B.‐H. Cho, Y. Yoshikawa, and S. Koseki. 2021. “Predicting Sensory Evaluation of Spinach Freshness Using Machine Learning Model and Digital Images.” PLoS ONE 16: e0248769. https://doi.org/10.1371/journal.pone.0248769.
Kumar, M., S. A. Khan, A. Bhatia, V. Sharma, and P. Jain. 2023. “Machine Learning Algorithms: A Conceptual Review.” In 2023 1st International Conference on Intelligent Computing and Research Trends (ICRT), 1–7. IEEE.
Lagarda‐Clark, E. A., C. Goulet, and A. Duarte‐Sierra. 2024. “Biochemical Dynamics During Postharvest: Highlighting the Interplay of Stress During Storage and Maturation of Fresh Produce.” Biomolecular Concepts 15: 1–21. https://doi.org/10.1515/bmc‐2022‐0048.
LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521: 436–444. https://doi.org/10.1038/nature14539.
Lee, J.‐S. 2015. “Quality Characteristics, Carbon Dioxide, and Ethylene Production of Asparagus (Asparagus officinalis L.) Treated With 1‐Methylcyclopropene and 2‐Chloroethylphosphonic Acid During Storage.” Korean Journal of Horticultural Science and Technology 33: 675–686. https://doi.org/10.7235/hort.2015.14145.
Li, L., P. Yi, J. Sun, et al. 2023. “Genome‐wide Transcriptome Analysis Uncovers Gene Networks Regulating Fruit Quality and Volatile Compounds in Mango Cultivar 'Tainong' During Postharvest.” Food Research International 165: 112531. https://doi.org/10.1016/j.foodres.2023.112531.
Li, M., D. Li, F. Feng, S. Zhang, F. Ma, and L. Cheng. 2016. “Proteomic Analysis Reveals Dynamic Regulation of Fruit Development and Sugar and Acid Accumulation in Apple.” Journal of Experimental Botany 67: 5145–5157. https://doi.org/10.1093/jxb/erw277.
Li, Q., S. An, W. Liu, and L. Li. 2021. “Semisupervised Learning on Graphs With an Alternating Diffusion Process.” IEEE Transactions on Neural Networks and Learning Systems 32: 2862–2874. https://doi.org/10.1109/TNNLS.2020.3008445.
Li, Q., C. Zhang, H. Wang, et al. 2023. “Machine Learning Technique Combined With Data Fusion Strategies: A Tea Grade Discrimination Platform.” Industrial Crops and Products 203: 117127. https://doi.org/10.1016/j.indcrop.2023.117127.
Li, X., D. Liu, Y. Pu, and Y. Zhong. 2023. “Recent Advance of Intelligent Packaging Aided by Artificial Intelligence for Monitoring Food Freshness.” Foods 12: 2976. https://doi.org/10.3390/foods12152976.
Li, Y., Z. Wang, A. Yang, and X. Yu. 2025. “Integrating Evolutionary Algorithms and Enhanced‐YOLOv8 + for Comprehensive Apple Ripeness Prediction.” Scientific Reports 15: 7307. https://doi.org/10.1038/s41598‐025‐91939‐4.
Liakos, K. G., V. Athanasiadis, E. Bozinou, and S. I. Lalas. 2025. “Machine Learning for Quality Control in the Food Industry: A Review.” Foods 14: 3424. https://doi.org/10.3390/foods14193424.
Lin, E., and H.‐Y. Lane. 2017. “Machine Learning and Systems Genomics Approaches for Multi‐Omics Data.” Biomarker Research 5: 1–6. https://doi.org/10.1186/s40364‐017‐0082‐y.
Liu, S., D. Lang, G. Meng, J. Hu, M. Tang, and X. Zhou. 2022. “Tracing the Origin of Honey Products Based on Metagenomics and Machine Learning.” Food Chemistry 371: 131066. https://doi.org/10.1016/j.foodchem.2021.131066.
Liu, Z., Y. Wang, and J. Feng. 2025. “Identifying Supply Chain R&D Partners via Multilayer Institutional Cooperation Network and Tailored Link Prediction.” Computers & Industrial Engineering 201: 110887. https://doi.org/10.1016/j.cie.2025.110887.
Lopez, E., J. Etxebarria‐Elezgarai, J. M. Amigo, and A. Seifert. 2023. “The Importance of Choosing a Proper Validation Strategy in Predictive Models: A Tutorial With Real Examples.” Analytica Chimica Acta 1275: 341532. https://doi.org/10.1016/j.aca.2023.341532.
Luo, D., R. Luo, J. Cheng, and X. Liu. 2024. “Quality Detection and Grading of Peach Fruit Based on Image Processing Method and Neural Networks in Agricultural Industry.” Frontiers in Plant Science 15: 1–14. https://doi.org/10.3389/fpls.2024.1415095.
Ma, C., H. H. Zhang, and X. Wang. 2014. “Machine Learning for Big Data Analytics in Plants.” Trends in Plant Science 19: 798–808. https://doi.org/10.1016/j.tplants.2014.08.004.
Ma, H., J. Guo, G. Liu, et al. 2024. “Raman Spectroscopy Coupled With Chemometrics for Identification of Adulteration and Fraud in Muscle Foods: A Review.” Critical Reviews in Food Science and Nutrition 65, no. 11: 2008–2030. https://doi.org/10.1080/10408398.2024.2329956.
Mao, D., F. Wang, Z. Hao, and H. Li. 2018. “Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain.” International Journal of Environmental Research and Public Health 15: 1627. https://doi.org/10.3390/ijerph15081627.
Marondedze, C. 2017. “Date Fruit Proteomics During Development and Ripening Stages.” In Date Palm Biotechnology Protocols Volume II: Germplasm Conservation and Molecular Breeding 381–398. Humana Press. https://doi.org/10.1007/978‐1‐4939‐7159‐6_28.
Mathabe, P. M., Z. A. Belay, T. Ndlovu, and O. J. Caleb. 2020. “Progress in Proteomic Profiling of Horticultural Commodities During Postharvest Handling and Storage: A Review.” Scientia Horticulturae 261: 108996. https://doi.org/10.1016/j.scienta.2019.108996.
Matindoust, S., M. Baghaei‐Nejad, M. H. Shahrokh Abadi, Z. Zou, and L. R. Zheng. 2016. “Food Quality and Safety Monitoring Using Gas Sensor Array in Intelligent Packaging.” Sensor Review 36: 169–183. https://doi.org/10.1108/SR‐07‐2015‐0115.
McCafferty, C. L., S. Klumpe, R. E. Amaro, W. Kukulski, L. Collinson, and B. D. Engel. 2024. “Integrating Cellular Electron Microscopy With Multimodal Data to Explore Biology Across Space and Time.” Cell 187: 563–584. https://doi.org/10.1016/j.cell.2024.01.005.
Mogilipalem, K., A. K. Poodari, Y. Pulipati, and K. B. Sangeetha. 2024. “Food Spoilge Detection Using IoT and Machine Learning.” In 2024 5th International Conference for Emerging Technology (INCET), 1–4. IEEE.
Mohammadi, V., K. Kheiralipour, and M. Ghasemi‐Varnamkhasti. 2015. “Detecting Maturity of Persimmon Fruit Based on Image Processing Technique.” Scientia Horticulturae 184: 123–128. https://doi.org/10.1016/j.scienta.2014.12.037.
Mohammed, M., R. Srinivasagan, A. Alzahrani, and N. K. Alqahtani. 2023. “Machine‐Learning‐Based Spectroscopic Technique for Non‐Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged Under Modified Atmospheres.” Sustainability 15: 12871. https://doi.org/10.3390/su151712871.
Montesinos‐López, O. A., A. Montesinos‐López, J. Crossa, D. Gianola, C. M. Hernández‐Suárez, and J. Martín‐Vallejo. 2018. “Multi‐Trait, Multi‐Environment Deep Learning Modeling for Genomic‐Enabled Prediction of Plant Traits.” G3: Genes, Genomes, Genetics 8: 3829–3840. https://doi.org/10.1534/g3.118.200728.
Morales‐Solis, A., A. Pérez‐López, M. E. Ramírez‐Guzmán, T. Espinosa‐Solares, and I. Alia‐Tejacal. 2024. “ARIMAX Modelling: Response of Hass Avocado Respiration Rate to Environmental Factors.” Horticulturae 10: 700. https://doi.org/10.3390/horticulturae10070700.
Mukherjee, A., T. Sarkar, and K. Chatterjee. 2021. “Freshness Assessment of Indian Gooseberry (Phyllanthus emblica) Using Probabilistic Neural Network.” Journal of Biosystems Engineering 46: 399–416. https://doi.org/10.1007/s42853‐021‐00116‐8.
Murphy, K. M., E. Ludwig, J. Gutierrez, and M. A. Gehan. 2024. “Deep Learning in Image‐Based Plant Phenotyping.” Annual Review of Plant Biology 75: 771–795. https://doi.org/10.1146/annurev‐arplant‐070523‐042828.
Nami, M., M. Taheri, J. Siddiqui, I. A. Deen, M. Packirisamy, and M. J. Deen. 2024. “Recent Progress in Intelligent Packaging for Seafood and Meat Quality Monitoring.” Advanced Materials Technologies 9: 2301347. https://doi.org/10.1002/admt.202301347.
Napit, R., S. K. Jaysawal, R. Chowdhury, et al. 2024. “Aptasensors and Advancement in Molecular Recognition Technology.” Advanced Materials Technologies 10, no. 1: 2400504. https://doi.org/10.1002/admt.202400504.
Nasution, I., and K. Gusriyan. 2019. “Nutmeg Grading System Using Computer Vision Techniques.” In: International Conference on Agricultural Technology, Engineering and Environmental Sciences. IOP Publishing.
Neupane, C., M. Pereira, A. Koirala, and K. B. Walsh. 2023. “Fruit Sizing in Orchard: A Review From Caliper to Machine Vision With Deep Learning.” Sensors 23: 3868. https://doi.org/10.3390/s23083868.
Nguyen, G., S. Dlugolinsky, M. Bobák, et al. 2019. “Machine Learning and Deep Learning Frameworks and Libraries for Large‐Scale Data Mining: A Survey.” Artificial Intelligence Review 52: 77–124. https://doi.org/10.1007/s10462‐018‐09679‐z.
Nikolados, E.‐M., A. Wongprommoon, O. M. Aodha, G. Cambray, and D. A. Oyarzún. 2022. “Accuracy and Data Efficiency in Deep Learning Models of Protein Expression.” Nature Communications 13: 7755. https://doi.org/10.1038/s41467‐022‐34902‐5.
Ojha, P. K., and K. Roy. 2018. “Development of a Robust and Validated 2D‐QSPR Model for Sweetness Potency of Diverse Functional Organic Molecules.” Food and Chemical Toxicology 112: 551–562. https://doi.org/10.1016/j.fct.2017.03.043.
Okere, E. E., E. Arendse, A. Ambaw Tsige, W. J. Perold, and U. L. Opara. 2022. “Pomegranate Quality Evaluation Using Non‐Destructive Approaches: A Review.” Agriculture 12: 2034. https://doi.org/10.3390/agriculture12122034.
Opara, I. K., U. L. Opara, J. A. Okolie, and O. A. Fawole. 2024. “Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: a Review.” Plants 13: 1200. https://doi.org/10.3390/plants13091200.
Öztürk, M., and Z. Ayhan. 2023. “Combined Effects of Ethylene Scavenging‐Active Packaging System and Modified Atmosphere to Reduce Postharvest Losses of Ethylene Sensitive Produce: Banana and Kiwifruit.” Packaging Technology and Science 36: 951–967. https://doi.org/10.1002/pts.2764.
Pahar, M., M. Klopper, R. Warren, and T. Niesler. 2021. “COVID‐19 Cough Classification Using Machine Learning and Global Smartphone Recordings.” Computers in Biology and Medicine 135: 104572. https://doi.org/10.1016/j.compbiomed.2021.104572.
Palumbo, M., G. Attolico, V. Capozzi, et al. 2022. “Emerging Postharvest Technologies to Enhance the Shelf‐Life of Fruit and Vegetables: An Overview.” Foods 11: 3925. https://doi.org/10.3390/foods11233925.
Park, B., T. Shin, J.‐S. Cho, J.‐H. Lim, and K.‐J. Park. 2023. “Improving Blueberry Firmness Classification With Spectral and Textural Features of Microstructures Using Hyperspectral Microscope Imaging and Deep Learning.” Postharvest Biology and Technology 195: 112154. https://doi.org/10.1016/j.postharvbio.2022.112154.
Pasa, L., N. Navarin, and A. Sperduti. 2022. “Polynomial‐Based Graph Convolutional Neural Networks for Graph Classification.” Machine Learning 111: 1205–1237. https://doi.org/10.1007/s10994‐021‐06098‐0.
Pathmanaban, P., B. Gnanavel, S. S. Anandan, and S. Sathiyamurthy. 2023. “Advancing Post‐Harvest Fruit Handling Through AI‐Based Thermal Imaging: Applications, Challenges, and Future Trends.” Discover Food 3: 27. https://doi.org/10.1007/s44187‐023‐00068‐2.
Piras, A., C. Ehlert, and G. Gryn'ova. 2021. “Sensing and Sensitivity: Computational Chemistry of Graphene‐Based Sensors.” WIREs Computational Molecular Science 11: e1526. https://doi.org/10.1002/wcms.1526.
Pott, D. M., F. d. A. E Lima, C. Soria, et al. 2020. “Metabolic Reconfiguration of Strawberry Physiology in Response to Postharvest Practices.” Food Chemistry 321: 126747. https://doi.org/10.1016/j.foodchem.2020.126747.
Pounds, K., H. Bao, Y. Luo, et al. 2022. “Real‐Time and Rapid Food Quality Monitoring Using Smart Sensory Films With Image Analysis and Machine Learning.” ACS Food Science & Technology 2: 1123–1134. https://doi.org/10.1021/acsfoodscitech.2c00124?goto=supporting‐info.
Puthongkham, P., S. Wirojsaengthong, and A. Suea‐Ngam. 2021. “Machine Learning and Chemometrics for Electrochemical Sensors: Moving Forward to the Future of Analytical Chemistry.” Analyst 146: 6351–6364. https://doi.org/10.1039/D1AN01148K.
Raihen, M. N., and S. Akter. 2024. “Prediction Modeling Using Deep Learning for the Classification of Grape‐Type Dried Fruits.” International Journal of Mathematics and Computer in Engineering 2: 1–12. https://doi.org/10.2478/ijmce‐2024‐0001.
Ranbir. Kumar, M., G. Singh, J. Singh, N. Kaur, and N. Singh. 2022. “Machine Learning‐Based Analytical Systems: Food Forensics.” ACS Omega 7: 47518–47535. https://doi.org/10.1021/acsomega.2c05632.
Raschka, S., J. Patterson, and C. Nolet. 2020. “Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence.” Information 11: 193. https://doi.org/10.3390/info11040193.
Rashvand, M., G. Altieri, R. Abbaszadeh, et al. 2023. “Prediction of CO2 and Ethylene Produced in Packaged Apricot Under Cold Plasma Treatment by Machine Learning Approach.” Journal of Food Process Engineering 46: e14418. https://doi.org/10.1111/jfpe.14418.
Ricardo‐Rodrigues, S., M. Laranjo, and A. C. Agulheiro‐Santos. 2023. “Methods for Quality Evaluation of Sweet Cherry.” Journal of the Science of Food and Agriculture 103: 463–478. https://doi.org/10.1002/jsfa.12144.
Rizzo, M., M. Marcuzzo, A. Zangari, A. Gasparetto, and A. Albarelli. 2023. “Fruit Ripeness Classification: A Survey.” Artificial Intelligence in Agriculture 7: 44–57. https://doi.org/10.1016/j.aiia.2023.02.004.
Romruen, O., P. Kaewprachu, S. Sai‐Ut, et al. 2024. “Impact of Environmental Storage Conditions on Properties and Stability of a Smart Bilayer Film.” Scientific Reports 14: 23038. https://doi.org/10.1038/s41598‐024‐74004‐4.
Rong, D., X. Rao, and Y. Ying. 2017. “Computer Vision Detection of Surface Defect on Oranges by Means of a Sliding Comparison Window Local Segmentation Algorithm.” Computers and Electronics in Agriculture 137: 59–68. https://doi.org/10.1016/j.compag.2017.02.027.
Rosenblatt, F. 1958. “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.” Psychological Review 65: 386–408. https://doi.org/10.1037/h0042519.
Saberi Riseh, R., M. Vatankhah, M. Hassanisaadi, and J. F. Kennedy. 2023. “Chitosan‐Based Nanocomposites as Coatings and Packaging Materials for the Postharvest Improvement of Agricultural Product: A Review.” Carbohydrate Polymers 309: 120666. https://doi.org/10.1016/j.carbpol.2023.120666.
Salazar, J., C. Jorquera, R. Campos‐Vargas, C. Jorgensen, P. Zapata, and R. Infante. 2019. “Effect of the Application Timing of 1‐MCP on Postharvest Traits and Sensory Quality of a Yellow‐Fleshed Kiwifruit.” Scientia Horticulturae 244: 82–87. https://doi.org/10.1016/j.scienta.2018.09.028.
Santo, L. A. G. E., R. J. D. Placido, and N. B. Linsangan. 2021. “Potato Skin Defect Detection and Classification Through Image Processing.” In Thirteenth International Conference on Graphics and Image Processing (ICGIP). IEEE.
Schonlau, M., and R. Y. Zou. 2020. “The Random Forest Algorithm for Statistical Learning.” The Stata Journal 20: 3–29. https://doi.org/10.1177/1536867X20909688.
Schudel, S., K. Shoji, C. Shrivastava, D. Onwude, and T. Defraeye. 2023. “Solution Roadmap to Reduce Food Loss Along Your Postharvest Supply Chain From Farm to Retail.” Food Packaging and Shelf Life 36: 101057. https://doi.org/10.1016/j.fpsl.2023.101057.
Sekhar, P. K., T. Ludwig, M. Wilhelm, D. Graf, M. A. Riheen, and S. Mathur. 2019. “Potentiometric Ethene Sensor for Postharvest Detection Applications.” Journal of the Electrochemical Society 166: B1477. https://doi.org/10.1149/2.0501915jes.
Shankaraswamy, J., and T. Radhika. 2024. “Sensor, IoT‐Based Post‐Harvest Shelf Life Determination of Tomato (Lycopersicon esculentum) Through Machine Learning Predictive Analysis for Intelligent Transport.” Journal of Environmental Biology 45: 455–464. https://doi.org/10.22438/jeb/45/4/MRN‐5339.
Shanthini, K., J. Francis, S. N. George, S. George, and B. M. Devassy. 2025. “Early Bruise Detection, Classification and Prediction in Strawberry Using Vis‐NIR Hyperspectral Imaging.” Food Control 167: 110794. https://doi.org/10.1016/j.foodcont.2024.110794.
Shen, C., R. Wang, H. Nawazish, B. Wang, K. Cai, and B. Xu. 2024. “Machine Vision Combined With Deep Learning‐Based Approaches for Food Authentication: an Integrative Review and New Insights.” Comprehensive Reviews in Food Science and Food Safety 23: e70054. https://doi.org/10.1111/1541‐4337.70054.
Shen, S., C. Zhan, C. Yang, A. R. Fernie, and J. Luo. 2023. “Metabolomics‐Centered Mining of Plant Metabolic Diversity and Function: Past Decade and Future Perspectives.” Molecular Plant 16: 43–63. https://doi.org/10.1016/j.molp.2022.09.007.
Shen, W., J. Pan, G. Wang, and X. Li. 2021. “Deep Learning‐Based Prediction of TFBSs in Plants.” Trends in Plant Science 26: 1301–1302. https://doi.org/10.1016/j.tplants.2021.06.016.
Shi, H., W. Ma, Z. Xu, and P. Lin. 2023. “A Novel Integrated Strategy of Easy Pruning, Parameter Searching, and Re‐Parameterization for Lightweight Intelligent Lithology Identification.” Expert Systems with Applications 231: 120657. https://doi.org/10.1016/j.eswa.2023.120657.
Shu, X., and G. Zhao. 2021. “Scalable Multi‐Label Canonical Correlation Analysis for Cross‐modal Retrieval.” Pattern Recognition 115: 107905. https://doi.org/10.1016/j.patcog.2021.107905.
Shuprajhaa, T., J. M. Raj, S. K. Paramasivam, K. Sheeba, and S. Uma. 2023. “Deep Learning Based Intelligent Identification System for Ripening Stages of Banana.” Postharvest Biology and Technology 203: 112410. https://doi.org/10.1016/j.postharvbio.2023.112410.
Simon, A., M. Singh Deo, V. Selvam, and R. Babu. 2016. “An Overview of Machine Learning and Its Applications.” International Journal of Electrical Sciences Electrical Sciences & Engineering (IJESE) 1: 22–24.
Singh, R. 2022. “Intrusion Bit Detection in Data Packet Transmitted Over Covert Channels Using K‐NN Machine Learning Algorithm.” International Journal for Research in Applied Science & Engineering Technology (IJRASET) 10: 1850–1854. https://doi.org/10.22214/ijraset.2022.46940.
Sinha, A., and D. Das. 2023. “SNRepair: Systematically Addressing Sensor Faults and Self‐Calibration in IoT Networks.” IEEE Sensors Journal 23: 14915–14922. https://doi.org/10.1109/JSEN.2023.3277493.
Springmann, M., M. Clark, D. Mason‐D'Croz, et al. 2018. “Options for Keeping the Food System Within Environmental Limits.” Nature 562: 519–525. https://doi.org/10.1038/s41586‐018‐0594‐0.
Stathers, T., D. Holcroft, L. Kitinoja, et al. 2020. “A Scoping Review of Interventions for Crop Postharvest Loss Reduction in Sub‐Saharan Africa and South Asia.” Nature Sustainability 3: 821–835. https://doi.org/10.1038/s41893‐020‐00622‐1.
Stocchero, M., M. De Nardi, and B. Scarpa. 2021. “PLS for Classification.” Chemometrics and Intelligent Laboratory Systems 216: 104374. https://doi.org/10.1016/j.chemolab.2021.104374.
Suryavanshi, A., R. Thakker, H. Waghela, and P. Kamble. 2022. “A SMART IOT and CLOUD Based Food Monitoring System: Review.” In 2022 5th International Conference on Advances in Science and Technology (ICAST), 508–512. IEEE.
Sutton, R. S. 1992. “Introduction: The Challenge of Reinforcement Learning.” In Reinforcement Learning, edited by R. S. Sutton, 1–3. Springer.
Tian, X., L. Zhu, N. Yang, et al. 2021. “Proteomics and Metabolomics Reveal the Regulatory Pathways of Ripening and Quality in Post‐Harvest Kiwifruits.” Journal of Agricultural and Food Chemistry 69: 824–835. https://doi.org/10.1021/acs.jafc.0c05492.
Tilman, D., and M. Clark. 2014. “Global Diets Link Environmental Sustainability and Human Health.” Nature 515: 518–522. https://doi.org/10.1038/nature13959.
Tirkolaee, E. B., S. Sadeghi, F. M. Mooseloo, H. R. Vandchali, and S. Aeini. 2021. “Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas.” Mathematical Problems in Engineering 2021: 1476043. https://doi.org/10.1155/2021/1476043.
Truong Minh Long, N., and N. Truong Thinh. 2020. “Using Machine Learning to Grade the Mango's Quality Based on External Features Captured by Vision System.” Applied Sciences 10: 5775. https://doi.org/10.3390/app10175775.
Tuly, S. S., M. Mahiuddin, and A. Karim. 2023. “Mathematical Modeling of Nutritional, Color, Texture, and Microbial Activity Changes in Fruit and Vegetables During Drying: A Critical Review.” Critical Reviews in Food Science and Nutrition 63: 1877–1900. https://doi.org/10.1080/10408398.2021.1969533.
Urugo, M. M., T. A. Teka, H. F. Gemede, et al. 2024. “A Comprehensive Review of Current Approaches on Food Waste Reduction Strategies.” Comprehensive Reviews in Food Science and Food Safety 23: e70011. https://doi.org/10.1111/1541‐4337.70011.
Vallese, F. D., S. G. Paoloni, V. Springer, D. D. D. S. Fernandes, P. H. G. D. Diniz, and M. F. Pistonesi. 2024. “Exploiting the Successive Projections Algorithm to Improve the Quantification of Chemical Constituents and Discrimination of Botanical Origin of Argentinean Bee‐Pollen.” Journal of Food Composition and Analysis 126: 105925. https://doi.org/10.1016/j.jfca.2023.105925.
van Dijk, A. D. J., G. Kootstra, W. Kruijer, and D. de Ridder. 2021. “Machine Learning in Plant Science and Plant Breeding.” Iscience 24: 101890. https://doi.org/10.1016/j.isci.2020.101890.
Van Engelen, J. E., and H. H. Hoos. 2020. “A Survey on Semi‐Supervised Learning.” Machine Learning 109: 373–440. https://doi.org/10.1007/s10994‐019‐05855‐6.
Vithu, P., and J. Moses. 2016. “Machine Vision System for Food Grain Quality Evaluation: A Review.” Trends in Food Science & Technology 56: 13–20. https://doi.org/10.1016/j.tifs.2016.07.011.
Wang, C., J. Du, D. Hou, et al. 2023. “Quality Retention and Delay Postharvest Senescence of Figs (Ficus carica L.) Using 1‐Methylcyclopropene and Modified Atmosphere Packaging During Cold Storage.” Food Bioscience 53: 102748. https://doi.org/10.1016/j.fbio.2023.102748.
Wang, D., M. Zhang, M. Li, and J. Lin. 2024. “Fruits and Vegetables Preservation Based on AI Technology: Research Progress and Application Prospects.” Computers and Electronics in Agriculture 226: 109382. https://doi.org/10.1016/j.compag.2024.109382.
Wang, D., M. Zhang, A. S. Mujumdar, and D. Yu. 2022. “Advanced Detection Techniques Using Artificial Intelligence in Processing of Berries.” Food Engineering Reviews 14: 176–199. https://doi.org/10.1007/s12393‐021‐09298‐5.
Wang, G., Y. Guo, Y. Yu, Y. Shi, Y. Ying, and H. Men. 2025. “ColorNet: an AI‐Based Framework for Pork Freshness Detection Using a Colorimetric Sensor Array.” Food Chemistry 471: 142794. https://doi.org/10.1016/j.foodchem.2025.142794.
Wang, J., Y. Wu, S. Li, and F. Nie. 2023. “A Self‐Training Algorithm Based on the Two‐Stage Data Editing Method With Mass‐Based Dissimilarity.” Neural Networks 168: 431–449. https://doi.org/10.1016/j.neunet.2023.09.046.
Wang, X., F. Li, L. Wei, et al. 2024. “Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning‐Assisted Multiplex SERS Fingerprinting.” Analytical Chemistry 96: 4682–4692. https://doi.org/10.1021/acs.analchem.4c00064.
Wang, Y., M. Li, W. Liu, and L. Jiang. 2025. “Illuminating the Future of Food Microbial Control: From Optical Tools to Optogenetic Tools.” Food Chemistry 471: 142474. https://doi.org/10.1016/j.foodchem.2024.142474.
Wu, L., Z. Luo, Y. Shi, et al. 2022. “A Cost‐Effective tsCUT&Tag Method for Profiling Transcription Factor Binding Landscape.” Journal of Integrative Plant Biology 64: 2033–2038. https://doi.org/10.1111/jipb.13354.
Xia, J., W. Huang, X. Wang, Z. Zhu, M. Zhang, and X. Zhang. 2023. “Flexible Sensing Technology for Fruit Quality Control in the Cold Chain: Characterization, Application, and Improvement.” Food Control 154: 109976. https://doi.org/10.1016/j.foodcont.2023.109976.
Xie, Q., Z. Wang, Y. Fang, and Y. Li. 2025. “MABQN: Multi‐Agent Reinforcement Learning Algorithm With Discrete Policy.” Neurocomputing 626: 129552. https://doi.org/10.1016/j.neucom.2025.129552.
Xue, L., X. Liu, S. Lu, et al. 2021. “China's Food Loss and Waste Embodies Increasing Environmental Impacts.” Nature Food 2: 519–528. https://doi.org/10.1038/s43016‐021‐00317‐6.
Yadav, A., N. Kumar, A. Upadhyay, S. Sethi, and A. Singh. 2022. “Edible Coating as Postharvest Management Strategy for Shelf‐Life Extension of Fresh Tomato (Solanum lycopersicum L.): An Overview.” Journal of Food Science 87: 2256–2290. https://doi.org/10.1111/1750‐3841.16145.
Yan, J., and X. Wang. 2023. “Machine Learning Bridges Omics Sciences and Plant Breeding.” Trends in Plant Science 28: 199–210. https://doi.org/10.1016/j.tplants.2022.08.018.
Yang, F., R. Zhao, J. Suo, et al. 2024. “Understanding Quality Differences Between Kiwifruit Varieties During Softening.” Food Chemistry 430: 136983. https://doi.org/10.1016/j.foodchem.2023.136983.
Yang, M., S. Chen, Z. Huang, et al. 2023. “Deep Learning‐Enabled Discovery and Characterization of HKT Genes in Spartina Alterniflora.” The Plant Journal 116: 690–705. https://doi.org/10.1111/tpj.16397.
Yang, Y., O. Jia, Y. Li, et al. 2024. “Effect of High CO2 Controlled Atmosphere Storage on Postharvest Quality of Button Mushroom (Agaricus bisporus).” Foods 13: 3486. https://doi.org/10.3390/foods13213486.
Yang, Y., M. A. Saand, L. Huang, et al. 2021. “Applications of Multi‐Omics Technologies for Crop Improvement.” Frontiers in Plant Science 12: 563953. https://doi.org/10.3389/fpls.2021.563953.
Yao, Z., X. Zhang, P. Nie, et al. 2023. “Identification of Milk Adulteration in Camel Milk Using FT‐Mid‐Infrared Spectroscopy and Machine Learning Models.” Foods 12: 4517. https://doi.org/10.3390/foods12244517.
Yu, X., X. Zhang, X. Liu, et al. 2024. “Comparative Transcriptomic Profile of Two Mandarin Varieties During Maturation Reveals Pectinase Regulating Peelability.” Scientia Horticulturae 331: 113148. https://doi.org/10.1016/j.scienta.2024.113148.
Yun, Z., H. Gao, and Y. Jiang. 2022. “Insights Into Metabolomics in Quality Attributes of Postharvest Fruit.” Current Opinion in Food Science 45: 100836. https://doi.org/10.1016/j.cofs.2022.100836.
Yun, Z., T. Li, H. Gao, et al. 2019. “Integrated Transcriptomic, Proteomic, and Metabolomics Analysis Reveals Peel Ripening of Harvested Banana Under Natural Condition.” Biomolecules 9: 167. https://doi.org/10.3390/biom9050167.
Zaguia, A. 2023. “Smart Greenhouse Management System With Cloud‐Based Platform and IoT Sensors.” Spatial Information Research 31: 559–571. https://doi.org/10.1007/s41324‐023‐00523‐3.
Zareef, M., M. Arslan, M. M. Hassan, et al. 2021. “Recent Advances in Assessing Qualitative and Quantitative Aspects of Cereals Using Nondestructive Techniques: A Review.” Trends in Food Science & Technology 116: 815–828. https://doi.org/10.1016/j.tifs.2021.08.012.
Zhan, W., X. Yang, G. Lu, Y. Deng, and L. Yang. 2022. “A Rapid Quality Grade Discrimination Method for Gastrodia elata Powderusing ATR‐FTIR and Chemometrics.” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 264: 120189. https://doi.org/10.1016/j.saa.2021.120189.
Zhang, L., H. Song, N. Aletras, and H. Lu. 2022. “Node‐Feature Convolution for Graph Convolutional Networks.” Pattern Recognition 128: 108661. https://doi.org/10.1016/j.patcog.2022.108661.
Zhang, X., and C.‐A. Liu. 2023. “Model Averaging Prediction by K‐Fold Cross‐Validation.” Journal of Econometrics 235: 280–301. https://doi.org/10.1016/j.jeconom.2022.04.007.
Zhang, Z., M. Gao, L. Zhang, et al. 2025. “Smartphone‐assisted Fluorescent Film Based on the Flu Grafted on Eu‐MOF for Real‐Time Monitoring of Fresh‐Cut Fruit Freshness.” Biosensors and Bioelectronics 277: 117278. https://doi.org/10.1016/j.bios.2025.117278.
Zhang, Z., D. Liu, B. Li, et al. 2024. “A k‐mer‐Based Pangenome Approach for Cataloging Seed‐Storage‐Protein Genes in Wheat to Facilitate Genotype‐to‐Phenotype Prediction and Improvement of End‐Use Quality.” Molecular Plant 17: 1038–1053. https://doi.org/10.1016/j.molp.2024.05.006.
Zheng, J., X. Yang, Y. Huang, S. Yang, S. Wuchty, and Z. Zhang. 2023. “Deep Learning‐Assisted Prediction of Protein‐Protein Interactions in Arabidopsis thaliana.” The Plant Journal 114: 984–994. https://doi.org/10.1111/tpj.16188.
Zhou, F., and C. Wen. 2023. “Research on the Level of Agricultural Green Development, Regional Disparities, and Dynamic Distribution Evolution in China From the Perspective of Sustainable Development.” Agriculture 13: 1441. https://doi.org/10.3390/agriculture13071441.
Zhou, L., S. Pan, J. Wang, and A. V. Vasilakos. 2017. “Machine Learning on Big Data: Opportunities and Challenges.” Neurocomputing 237: 350–361. https://doi.org/10.1016/j.neucom.2017.01.026.
Zhou, L., C. Zhang, F. Liu, Z. Qiu, and Y. He. 2019. “Application of Deep Learning in Food: A Review.” Comprehensive Reviews in Food Science and Food Safety 18: 1793–1811. https://doi.org/10.1111/1541‐4337.12492.
Zhu, M., Y. Gong, C. Tian, and Z. Zhu. 2024. “A Systematic Survey of Transformer‐based 3D Object Detection for Autonomous Driving: Methods, Challenges and Trends.” Drones 8: 412. https://doi.org/10.3390/drones8080412.
Zhu, Y., S. Chen, H. Yin, et al. 2024. “Classification of Oolong Tea Varieties Based on Computer Vision and Convolutional Neural Networks.” Journal of the Science of Food and Agriculture 104: 1630–1637. https://doi.org/10.1002/jsfa.13049.
Zou, Z., T. Long, Q. Wang, et al. 2022. “Implementation of Apple's Automatic Sorting System Based on Machine Learning.” Food Science and Technology 42: e24922. https://doi.org/10.1590/fst.24922.
Zuo, Z., P. Jiang, D. Chen, et al. 2024. “Improving the Storage Quality and Antioxidant Capacity of Postharvest Winter Jujube by Laser Microporous Modified Atmosphere Packaging.” Scientia Horticulturae 337: 113477. https://doi.org/10.1016/j.scienta.2024.113477.
Contributed Indexing:
Keywords: fresh food; machine learning; quality control; supply chains
Entry Date(s):
Date Created: 20251208 Date Completed: 20251208 Latest Revision: 20251208
Update Code:
20251208
DOI:
10.1111/1541-4337.70360
PMID:
41355592
Database:
MEDLINE
Weitere Informationen
Approximately one-third of fresh food is wasted globally throughout the supply chain. Machine learning (ML), a key branch of artificial intelligence, enhances postharvest logistics and preservation of fresh food by enabling intelligent sensing, precise evaluation, and adaptive control. However, its application is challenged by data standardization, sensor limitations, product variability, and limited model generalizability. This review summarizes current advanced ML applications in the food supply chain, emphasizing their transformative potential for quality control. We explore ML's ability to integrate multi-omics data for deeper insights into molecular changes during transportation and storage, enabling the development and evaluation of management strategies. Practical applications in grading, sensor technology, and intelligent preservation materials are also evaluated. ML models, such as support vector machine (SVM) and convolutional neural network (CNN), enhance precise grading and quality prediction by analyzing sensory attributes and chemical composition. By capturing complex molecular interactions, ML enables innovative sensor surface design with enhanced sensitivity and specificity. ML-driven sensors further support real-time environmental monitoring, while intelligent packaging materials powered by ML maintain freshness and reduce spoilage through adaptive responses to internal conditions. To ensure model robustness and generalizability, appropriate validation strategies such as cross-validation and external validation are essential. Despite its substantial potential, the widespread adoption of ML still faces challenges, including limited varietal and regional generalization, decision-making transparency, computational demands, limited data availability, and algorithm selection. Addressing these issues is critical for achieving effective and sustainable ML integration in postharvest quality control systems.
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