[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124257-en":3,"doc-seo-124257-105":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},124257,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Soil temperature prediction based on ensemble tree bagger machine learning algorithm for agricultural decision making","This research predicts surface soil temperature (ST) at a 5 cm depth to support agricultural decisions including sowing time, irrigation management, and soil–plant–atmosphere dynamics. Above-ground weather variables—air temperature, relative humidity, wind velocity, and sunshine duration—measured every 15 minutes are used as inputs for six regression machine learning models. The Ensemble Bagging Tree model yields the highest accuracy, with RMSE values of 2.04 for validation and 1.9 for testing. Variable importance shows air temperature as the dominant predictor, supported by SHAP-based explainable AI analysis.","PLANT SCIENCE TODAY ISSN 2348-1900 (online) Vol 12(2): 1-11  \n[https://doi.org/10.14719/pst.7291](https://doi.org/10.14719/pst.7291)  \nHORIZON e-Publishing Group  \nRESEARCH ARTICLE  \nSoil temperature prediction based on ensemble tree bagger machine learning algorithm for agricultural decision making  \nA Alagesan1, Thukkaiyannan P2*, Satheeshkumar N3, Thiruvarassan S4, Ganesan K5 & Ayyadurai P6  \n1ICAR-Krishi Vigyan Kendra, Tamil Nadu Agricultural University, Pudukottai 622 303, Tamil Nadu, India 2ICAR-Krishi Vigyan Kendra, Tamil Nadu Agricultural University, Tiruppur 641 667, Tamil Nadu, India 3Maize Research Station, Tamil Nadu Agricultural University, Vagarai 624 613, Tamil Nadu, India 4ICAR-Krishi Vigyan Kendra, Tamil Nadu Agricultural University, Villupuram 604 002, Tamil Nadu, India  \n5Directorate of Planning and Monitoring, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India 6Centre of Excellence in Millets, Tamil Nadu Agricultural University, Athiyandal 606 603, Tamil Nadu, India  \n*Email: [thukkaiyannan@tnau.ac.in](thukkaiyannan@tnau.ac.in)  \n OPEN ACCESS  \nARTICLE HISTORY  \nReceived: 09 January 2025  \nAccepted: 25 January 2025 Available online  \nVersion 1.0 : 14 March 2025  \nVersion 2.0 : 01 April 2025  \nAdditional information  \nPeer review: Publisher thanks Sectional Editor and the other anonymous reviewers for their contribution to the peer review of this work.  \nReprints & permissions information is available at [https://horizonepublishing.com/](https://horizonepublishing.com/)[ ](https://horizonepublishing.com/)[journals/index.php/PST/open_access_policy](journals/index.php/PST/open_access_policy)  \n[Publisher](Publisher)’s Note: Horizon e-Publishing Group remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nIndexing: Plant Science Today, published by Horizon e-Publishing Group, is covered by Scopus, Web of Science, BIOSIS Previews, Clarivate Analytics, NAAS, UGC Care, etc See [https://horizonepublishing.com/journals/](https://horizonepublishing.com/journals/)[ ](https://horizonepublishing.com/journals/)[index.php/PST/indexing_abstracting](index.php/PST/indexing_abstracting)  \n[Copyright](Copyright:)[:](Copyright:) © [The Author](The Author)([s](s))[. This is](. This is) an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited ([https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)[ ](https://creativecommons.org/licenses/)[by/4.0/](by/4.0/))  \nCITE THIS ARTICLE  \nAlagesan A, Thukkaiyannan P, Satheeshkumar N, Thiruvarassan S, Ganesan K, Ayyadurai P. Soil temperature prediction based on ensemble tree bagger machine learning algorithm for agricultural decision making. Plant Science Today. 2025; 12(2): 1-11. [https://doi.org/10.14719/](https://doi.org/10.14719/)[ ](https://doi.org/10.14719/)[pst.7291](pst.7291)  \nAbstract  \nThis study focuses on predicting surface soil temperature (ST) at a 5 cm depth, which significantly influences agricultural decisions such as sowing time, irrigation management and soil-plant-atmosphere dynamics. Machine learning (ML) algorithms were used to predict ST using above-ground weather variables viz., air temperature (T), relative humidity (RH), wind velocity (WV) and sunshine duration (SS) measured at 15-min intervals. Six regressionbased ML models (Ensemble, Gaussian Process Regression, Support Vector Machine, Tree, Neural Network and Kernel) were trained and tested for predictive accuracy. The Ensemble Bagging Tree model showed the highest precision, with RMSE values of 2.04 and 1.9 for validation and testing, respectively. Various combinations of the weather variables were tested and the model performed best when using above mentioned variables. Among the predictors, T had the greatest impact on ST prediction, as indicated b","cbCaipszKpREHRks","https://ap.wps.com/l/cbCaipszKpREHRks","pdf",1914437,1,11,"English","en",105,"# Abstract\n# Introduction\n# Materials and Methods\n## Data and Features\n## Machine Learning Models\n# Results and Discussion\n## Model Performance\n## Variable Importance and SHAP Analysis\n# Conclusion","[{\"question\":\"What soil temperature depth and variables does the study use to predict soil temperature?\",\"answer\":\"The study predicts surface soil temperature at a 5 cm depth. It uses above-ground weather variables measured at 15-minute intervals: air temperature, relative humidity, wind velocity, and sunshine duration.\"},{\"question\":\"Which machine learning model performed best for soil temperature prediction?\",\"answer\":\"The Ensemble Bagging Tree model achieved the highest precision, with RMSE values of 2.04 for validation and 1.9 for testing.\"},{\"question\":\"How does the study explain which weather variable most influences the prediction?\",\"answer\":\"Time-dependent air temperature is identified as the most influential predictor using mean absolute Shapley values and SHAP (SHapley Additive exPlanations) analysis. Sunshine duration, relative humidity, and wind velocity follow in importance.\"}]","Soil temperature prediction based on ensemble tree bagger machine learning algorithm for agricultural decision making | PDF",1785821250,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"soil-temperature-prediction-based-on-ensemble-tree-bagger-machine-learning-algorithm-for-agricultural-decision-making","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/soil-temperature-prediction-based-on-ensemble-tree-bagger-machine-learning-algorithm-for-agricultural-decision-making/124257/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What soil temperature depth and variables does the study use to predict soil temperature?","Question",{"text":75,"@type":76},"The study predicts surface soil temperature at a 5 cm depth. It uses above-ground weather variables measured at 15-minute intervals: air temperature, relative humidity, wind velocity, and sunshine duration.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning model performed best for soil temperature prediction?",{"text":80,"@type":76},"The Ensemble Bagging Tree model achieved the highest precision, with RMSE values of 2.04 for validation and 1.9 for testing.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study explain which weather variable most influences the prediction?",{"text":84,"@type":76},"Time-dependent air temperature is identified as the most influential predictor using mean absolute Shapley values and SHAP (SHapley Additive exPlanations) analysis. Sunshine duration, relative humidity, and wind velocity follow in importance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]