[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128726-en":3,"doc-seo-128726-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128726,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Predictive Modelling Employing Machine Learning, Convolutional Neural Networks (CNNs), and Smartphone RGB Images for Non-destructive Biomass Estimation of Pearl Millet","Digital tools and non-destructive monitoring are essential for real-time evaluation of crop output and health in sustainable agriculture, especially for precise above-ground biomass (AGB) estimation of pearl millet. This study uses transfer learning with pre-trained convolutional neural networks alongside shallow machine learning models including support vector regression, XGBoost, and random forest regression. Smartphone RGB imaging provides cost-effective data, and Shapley additive explanations (SHAP) quantify predictor importance. SHAP identifies NGRDI and plant height as key features, with XGBoost achieving the highest accuracy (R2≈0.98; RMSE≈0.26).","TYPE Original Research PUBLISHED 06 May 2025  \nDOI 10.3389/fpls.2025.1594728  \nOPEN ACCESS  \nEDITED BY  \nXing Yang,  \nAnhui Science and Technology University, China  \nREVIEWED BY  \nKavipriya J,  \nSRM Institute of Science and Technology, India  \nBing Qian,  \nChinese Academy of Tropical Agricultural Sciences, China  \n*CORRESPONDENCE  \nFaten Dhawi  \n [falmuhanna@kfu.edu.sa](falmuhanna@kfu.edu.sa)[ ](falmuhanna@kfu.edu.sa)Abdul Ghafoor  \n [aghafoor@kfu.edu.sa](aghafoor@kfu.edu.sa)  \nRECEIVED 20 March 2025  \nACCEPTED 11 April 2025  \nPUBLISHED 06 May 2025  \nCITATION  \nDhawi F, Ghafoor A, Almousa N, Ali S and Alqanbar S (2025) Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Pennisetum glaucum) .  \nFront. Plant Sci. 16:1594728 .  \ndoi: 10.3389/fpls.2025.1594728  \nCOPYRIGHT  \n© 2025 Dhawi, Ghafoor, Almousa, Ali and Alqanbar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPredictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Pennisetum glaucum)  \nFaten Dhawi 1*, Abdul Ghafoor 2*, Norah Almousa 3, Sakinah Ali 3 and Sara Alqanbar 3  \n1Agricultural Biotechnology Department, College of Agricultural and Food Sciences, King Faisal University, Al Ahsa, Saudi Arabia, 2Center for Water and Environmental Studies, King Faisal University, Al-Ahsa, Saudi Arabia, 3 Fab Lab, Abdulmonem Al Rashed Humanitarian Foundation,  \nAl-Ahsa, Saudi Arabia  \nDigital tools and non-destructive monitoring techniques are crucial for real-time evaluations of crop output and health in sustainable agriculture, particularly for precise above-ground biomass (AGB) computation in pearl millet (Pennisetum glaucum) . This study employed a transfer learning approach using pre-trained convolutional neural networks (CNNs) alongside shallow machine learning algorithms (Support Vector Regression, XGBoost, Random Forest Regression) to estimate AGB. Smartphone-based RGB imaging was used for data collection, and Shapley additive explanations (SHAP) methodology evaluated predictor importance. The SHAP analysis identified Normalized Green-Red Difference Index (NGRDI) and plant height as the most influential features for AGB estimation. XGBoost achieved the highest accuracy (R2 = 0 . 98, RMSE = 0 . 26) with a comprehensive feature set, while CNN-based models also showed strong predictive ability. Random Forest Regression performed best with the two most important features, whereas Support Vector Regression was the least effective. These findings demonstrate the effectiveness of CNNs and shallow machine learning for non-invasive AGB estimation using cost-effective RGB imagery, supporting automated biomass prediction and real-time plant growth monitoring. This approach can aid small-scale carbon inventories in smallholder agricultural systems, contributing to climate-resilient strategies.  \nKEYWORDS  \ndigital agriculture, deep learning, plant monitoring, carbon sequestration, CNN convolutional neural network  \nFrontiers in Plant Science 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nIn sustainable agriculture, digital technologies and nondestructive monitoring methods are essential for real-time assessments оf crop health and productivity. Biomass serves as an indicator оf crop vigor, reflecting vital processes such as photosynthesis, energy transfer, and water exchange with the atmosphere (Yue et al., 2017; Liu et al., 2022; Zheng et al., 2023) . Accurat","cbCairXxAvmzJfEM","https://ap.wps.com/l/cbCairXxAvmzJfEM","pdf",5717601,1,19,"English","en",105,"# Introduction\n## Digital agriculture and nondestructive monitoring\n## Importance of biomass for crop health and carbon sequestration\n## Pearl millet background and resilience\n## Limitations of conventional destructive biomass measurement\n# Methods and modelling approach\n## Transfer learning with CNNs\n## Shallow machine learning models\n## Smartphone RGB data collection\n## SHAP-based feature importance analysis\n# Results and discussion\n## Most influential predictors for AGB estimation\n## Model comparison and performance metrics\n## Implications for automated monitoring and carbon inventories","[{\"question\":\"How does the study estimate above-ground biomass (AGB) without destroying plants?\",\"answer\":\"It combines smartphone-based RGB imaging with machine learning, including transfer learning CNNs and shallow models, to predict AGB from image-derived predictors.\"},{\"question\":\"Which features were found to be most important for AGB estimation?\",\"answer\":\"The SHAP analysis identified the Normalized Green-Red Difference Index (NGRDI) and plant height as the most influential features.\"},{\"question\":\"Which model achieved the best predictive performance and what were its metrics?\",\"answer\":\"XGBoost produced the highest accuracy, with R2 around 0.98 and RMSE around 0.26 when using a comprehensive feature set.\"}]","Predictive Modelling Employing Machine Learning, Convolutional Neural Networks (CNNs), and Smartphone RGB Images for Non-destructive Biomass Estimation of Pearl Millet | 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