Improving Crop Residue Biomass Estimation through Ensemble Modeling and Optimized Feature Selection Using UAV Multispectral Imagery

Improving Crop Residue Biomass Estimation through Ensemble Modeling and Optimized Feature Selection Using UAV Multispectral Imagery | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 November 2025 V1 Latest version Share on Improving Crop Residue Biomass Estimation through Ensemble Modeling and Optimized Feature Selection Using UAV Multispectral Imagery Authors : Lilian Yang 0009-0007-3711-5468 [email protected] , Bing Lu , Margaret Schmidt , Pengpeng Zheng , Ali Jamali , and David McCaffrey Authors Info & Affiliations https://doi.org/10.22541/au.176222375.54636464/v1 252 views 134 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Crop residue plays a vital role in maintaining soil health, reducing erosion, enhancing water retention, and contributing to carbon sequestration in agricultural systems. Accurate estimation of crop residue biomass is essential for understanding its distribution patterns, advancing sustainable agricultural practices and improving land management. This study integrates high-resolution UAV multispectral imagery, advanced feature selection methods, and machine learning models to develop a scalable framework for crop residue biomass prediction. An ensemble model, combining predictions from CatBoost, Support Vector Regression, Random Forest, and K-Nearest Neighbor, was created and compared to these individual models to evaluate its performance for predicting crop residue biomass. A variety of predictor variables, including spectral indices, topographic features, textural features, and raw bands, were used in these models. To improve modeling efficiency and accuracy, four feature selection techniques—Recursive Feature Elimination, Pearson correlation, Least Absolute Shrinkage and Selection Operator regression—were tested and compared to identify the most relevant predictor features. Results show that red band and variance from the blue band emerged as consistently selected top predictors across methods. Additionally, the results highlighted the importance of integrating topographic and textural features alongside spectral features to enhance crop residue biomass estimation accuracy. The ensemble approach, combined with Recursive Feature Elimination-selected features, produced the most accurate crop residue biomass predictions (R 2 = 0.425, RMSE = 243.465 g/ha). This study demonstrates the potential of ensemble models with optimized feature selection to enhance crop residue monitoring for precision agriculture and sustainable land management. Supplementary Material File (manuscript - improving crop residue biomass estimation.docx) Download 4.44 MB Information & Authors Information Version history V1 Version 1 04 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords agriculture crop residue biomass machine learning remote sensing uav Authors Affiliations Lilian Yang 0009-0007-3711-5468 [email protected] Simon Fraser University Department of Geography View all articles by this author Bing Lu Simon Fraser University Department of Geography View all articles by this author Margaret Schmidt Simon Fraser University Department of Geography View all articles by this author Pengpeng Zheng Huazhong Agricultural University College of Plant Science and Technology View all articles by this author Ali Jamali Simon Fraser University Department of Geography View all articles by this author David McCaffrey Miraterra Inc View all articles by this author Metrics & Citations Metrics Article Usage 252 views 134 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lilian Yang, Bing Lu, Margaret Schmidt, et al. Improving Crop Residue Biomass Estimation through Ensemble Modeling and Optimized Feature Selection Using UAV Multispectral Imagery. Authorea . 04 November 2025. DOI: https://doi.org/10.22541/au.176222375.54636464/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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