Result: Morphometric trait analysis and machine learning-based yield modeling in wood apple (Feronia limonia L.).
Title:
Morphometric trait analysis and machine learning-based yield modeling in wood apple (Feronia limonia L.).
Authors:
Yadav V; ICAR-Central Horticultural Experiment Station, Gujarat, 389340, Vejalpur, Panchmahals, India., Mishra DS; ICAR-Central Horticultural Experiment Station, Gujarat, 389340, Vejalpur, Panchmahals, India. dsmhort@gmail.com., Rane J; ICAR-Central Institute for Arid Horticulture, Beechwal, Bikaner, 334006, Rajasthan, India., Apparao VV; ICAR-Central Horticultural Experiment Station, Gujarat, 389340, Vejalpur, Panchmahals, India., Ravat P; ICAR-Central Horticultural Experiment Station, Gujarat, 389340, Vejalpur, Panchmahals, India., Kumar P; ICAR-Indian Agricultural Research Institute, New Delhi, 110 012, India., Kaushik P; Chaudhary Charan Singh Haryana Agricultural University, Hisar, 125 004, Haryana, India., Khan MN; Renewable Energy and Environmental Technology Center, University of Tabuk, Tabuk, Saudi Arabia. mo.khan@ut.edu.sa.; Department of Science and Basic Studies, Applied College, University of Tabuk, Tabuk, Saudi Arabia. mo.khan@ut.edu.sa., Alghamdi M; Department of Computer Science, Applied College, University of Tabuk, Tabuk, 71491, Saudi Arabia.
Source:
BMC plant biology [BMC Plant Biol] 2025 Dec 28. Date of Electronic Publication: 2025 Dec 28.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 100967807 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1471-2229 (Electronic) Linking ISSN: 14712229 NLM ISO Abbreviation: BMC Plant Biol Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : BioMed Central, [2001-
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Ahmed M, Babayola M, Bake ID. Role of horticultural crops in food and nutritional security: a review. J Nutr Food Process. 2024;7(8):01–6. https://doi.org/10.31579/2637-8914/226.
Qureshi AA, Kumar KE, Shaista Omer SO. Feronia limonia-a path less travelled. Int J Res Ayur Pharm. 2010;1:98–106.
Ghosh SN, Banik AK, Banik BC, Bera B, Roy S, Kundu A. Conservation, multiplication and utilization of wood Apple (Feronia limonia)-a semi-wild fruit crop in West Bengal (India). Acta Hortic. 2011;948:279–83. https://doi.org/10.17660/ActaHortic.2012.948.32.
Jassim JM, Gurudivya P, Sankar C, Raja V, Sundarrajan RV, Reddy PSK, Thamizhvanan A, Aravind S. Biochemical evaluation of diverse wood Apple (Feronia limonia L.) geno-types under Tamil Nadu conditions. Int J Plant Soil Sci. 2025;37(8):354–61. https://doi.org/10.9734/ijpss/2025/v37i85637.
Kruger RR, Navarro L. Citrus germplasm resources. In: Khan IA, editor. Citrus genetics, breeding and biotechnology. UK: CABI; 2007. pp. 45–140.
Srivastava R, Mishra N, Agarwal S, Mishra N. Pharmacological and phytochemical properties of Kaitha (Feronia limonia L.): a review. Plant Arch. 2019;19(1):608–15.
Sharma P, Tenguria RK. Phytochemical properties and health benefits of Limonia acidissima: A review. Int Res J Plant Sci. 2021;12(3):1–6.
Mohamed Jassim J, Gurudivya P, Sankar C, Sundarrajan RV, Anchana K, Reddy PS, Raja V, Aravind S. Evaluation of wood Apple genotypes for yield traits under Tamil Nadu condition. J Adv Biol Biotechnol. 2025;28(8):670–77. https://doi.org/10.9734/jabb/2025/v28i82741.
Anonymous. 2024. Director of Horticulture, Agriculture, farmers welfare and Co-operation department. Govt Gujarat. Available at https://share.google/YCISBzPEIh7c50ejL. Accessed 9 Dec 2025. https://share.google/YCISBzPEIh7c50ejL.
Thakur N, Chugh V, Dwivedi S. Wood apple: an underutilized miracle fruit of India. Pharma Innov J. 2020;9:198–202.
Shukla U, Lata R, Maji S, Meena RC. Historical background, origin, distribution & present status of wood Apple. J Adv Biol Biotechnol. 2024;27(10):1457–67. https://doi.org/10.9734/jabb/2024/v27i101566.
Yadav V, Singh AK, Mishra DS, Yadav LP, Apparao VV, Sahil A, Ravat P, Rane J, Meena MK, Siddiqui MH, Alamri S, Khan S. Genetic diversity, quality traits, antioxidant properties, and nutrient composition of Feronia limonia accessions from a semiarid region for breeding and quality improvement. Sci Rep. 2025;15:35352. https://doi.org/10.1038/s41598-025-19356-1.
Filippi P, Jones EJ, Wimalathunge NS, Somarathna PD, Pozza LE, Ugbaje SU, Jephcott TG, Pat-erson SE, Whelan BM, Bishop TF. Precis Agric. 2019;20:1015–29. An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. https://doi.org/10.1007/s11119-018-09628-4.
Cubillas JJ, Ramos MI, Jurado JM, Feito FR. A machine learning model for early prediction of crop yield, nested in a web application in the cloud: a case study in an Olive grove in Southern Spain. Agriculture. 2022;12(9):1345. https://doi.org/10.3390/agriculture12091345.
Xu X, Gao P, Zhu X, Guo W, Ding J, Li C, Zhu M, Wu X. Design of an integrated Climatic as-sessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China. Ecol Indic. 2019;101:943–53. https://doi.org/10.1016/j.ecolind.2019.01.059.
Beulah R. A survey on different data mining techniques for crop yield prediction. Int J Comput Sci Eng. 2019;7:738–44.
Nasr I, Nassar K, Karray F. 2022. Enhancing fresh produce yield forecasting using vegetation indices from satellite images. IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2022;2863–2868. https://doi.org/10.1109/IJCNN55064.2022.9892192.
Suarez LA, Robson A, Brinkhoff J. Early-Season forecasting of citrus block-yield using time series remote sensing and machine learning: A case study in Australian orchards. Int J Appl Earth Obs Geoinf. 2023;122:103434. https://doi.org/10.1016/j.jag.2023.103434.
Jabed MA, Murad MA. Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability. Heliyon. 2024;10(24). https://doi.org/10.1016/j.heliyon.2024.e40836.
Coşkun ÖF, Aydın A, Başak H, Mavi K, Yetişir H, Toprak S. Perspectives on morphology, physiology, genetic polymorphism and machine learning in cucumber grafting under zinc toxicity. BMC Plant Biol. 2025;25(1):1647. https://doi.org/10.1186/s12870-025-07709-x.
Toprak S, Coşkun ÖF. Machine learning-based evaluation of nutrient distribution in grafted cucumber plants. PhytoTalks. 2025;2(3):457–66. https://doi.org/10.21276/pt.2025.v2.i3.3.
Panchbhai KG, Lanjewar MG, Naik AV. Modified MobileNet with leaky ReLU and LSTM with balancing technique to classify the soil types. Earth Sci Inf. 2025;18:77. https://doi.org/10.1007/s12145-024-01521-1.
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Contributed Indexing:
Keywords: Machine learning; Morphometric traits; PCA; Random forest; SHAP; Wood apple; Yield prediction
Entry Date(s):
Date Created: 20251228 Latest Revision: 20251228
Update Code:
20251229
DOI:
10.1186/s12870-025-07978-6
PMID:
41457267
Database:
MEDLINE
Further information
Background: Wood apple is a hardy yet underutilized fruit tree of the Indian subcontinent, valued for its nutritional, medicinal, and ecological significance. Despite its potential as a climate-resilient fruit species, the determinants of yield variability remain poorly characterized. This study aimed to quantify how morphometric descriptors of canopy architecture, floral, and fruit traits explain yield variation across 62 wood apple genotypes. By integrating multivariate statistics with explainable machine-learning models (Random Forest + SHAP), we provide the first data-driven framework for identifying trait combinations that govern productivity in this underutilized tree species. The approach offers a novel, interpretable path toward ideotype selection and precision orchard design.
Results: Extensive morphometric variability was observed across the 62 genotypes for vegetative, foliar, floral, fruit and seed traits, indicating a broad genetic base. Yield per tree ranged widely from 35 to 127 kg, with a mean of 75 kg tree⁻¹. Principal Component Analysis (PCA) showed that canopy architecture, branch traits, and leaf-fruit attributes collectively explained 31.1% of the total variation. Correlation analysis revealed positive associations of yield with tree shape, pulp colour, and fruit-bearing tendency, whereas ornamental fruit traits and excessive spine density were negatively related. The optimized Random Forest (RF) model achieved strong predictive performance on the test dataset (R² = 0.84; RMSE = 9.45 kg; MAE = 7.12 kg), significantly outperforming Multiple Linear Regression (R² = 0.62), Support Vector Regression (R² = 0.76), and the Deep Learning (MLP) model (R² = 0.71). RF identified tree shape (16%), open flower colour (11.3%), and pulp colour (9.0%) as the most influential predictors of yield. SHAP analysis further clarified the non-linear and interactive effects among traits, highlighting the combined influence of canopy vigour, reproductive efficiency, and fruit-quality attributes on productivity. Hierarchical clustering grouped the genotypes into three clusters, with Cluster 2 characterized by compact canopies, superior reproductive traits, and desirable pulp features showing the highest and most stable yield (mean 84.6 kg tree⁻¹). Cluster 0 displayed moderate-to-high yields (79.7 kg tree⁻¹) but with greater variability, while Cluster 1 comprised the lowest-yielding genotypes (70.4 kg tree⁻¹). These findings confirm that productivity in wood apple is jointly regulated by architectural and reproductive traits through coordinated source-sink dynamics.
Conclusions: Wood apple yield is governed by an integrated suite of architectural and reproductive traits, rather than single descriptors. Genotypes with compact canopies, regular bearing habit, and consumer-preferred pulp characteristics emerge as promising ideotypes for high productivity and orchard efficiency. By combining Random Forest and SHAP, this study demonstrates the practical value of explainable machine-learning tools in identifying actionable trait combinations and providing a robust, trait-based framework to support data-driven breeding and climate-smart orchard design in underutilized perennial fruit crops.
(© 2025. The Author(s).)
Declarations. Ethics approval and consent to participate: Not Applicable. Consent for publication: Not Applicable. Competing interests: The authors declare no competing interests.