[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123004-en":3,"doc-seo-123004-105":30,"detail-sidebar-cat-0-en-105":95},{"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},123004,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Incorporating Meteorological Data and Pesticide Information to Forecast Crop Yields - Using Machine Learning","The agricultural sector faces intensified risk from climate change and excessive pesticide use, threatening global food security. Accurate crop-yield prediction is critical to support sustainable farming decisions and improve resilience. This research proposes a data-driven crop yield prediction system combining one year of meteorological data, pesticide records, and yield data with machine learning. Three models—Gradient Boosting, K-Nearest Neighbors, and Multivariate Logistic Regression—are trained and evaluated using GridSearchCV within K-fold cross-validation to reduce overfitting. Results show outstanding performance for Gradient Boosting (R2 ≈ 99.99%) and identify key meteorological conditions through correlation analysis between predicted and actual yields.","Received 31 January 2024, accepted 20 March 2024, date of publication 29 March 2024, date of current version 5 April 2024. Digital Object Identifier 10.1109/ACCESS.2024.3383309  \nIncorporating Meteorological Data and Pesticide Information to Forecast Crop Yields  \nUsing Machine Learning  \nMD JIABUL HOQUE1,2, MD. SAIFUL ISLAM2, JIA UDDIN3,  \nMD. ABDUS SAMAD4,(Member, IEEE), BEATRIZ SAINZ DE ABAJO5, DÉBORA LIBERTAD RAMÍREZ VARGAS6,7,8, AND IMRAN ASHRAF4  \n1Department of Computer and Communication Engineering, International Islamic University Chittagong, Kumira, Chattogram 4318, Bangladesh  \n2Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh  \n3AI and Big Data Department, Endicott College, Woosong University, Daejeon 34606, South Korea  \n4Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, South Korea  \n5Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain  \n6Universidad Europea del Atlántico, 39011 Santander, Spain  \n7Universidad Internacional Iberoamericana, Campeche 24560, Mexico  \n8Universidad de La Romana, La Romana, Dominica  \nCorresponding authors: Md. Saiful Islam ([saiful05eee@cuet.ac.bd](saiful05eee@cuet.ac.bd)), Md. Abdus Samad ([masamad@yu.ac.kr](masamad@yu.ac.kr)), and Imran Ashraf  \n([ashrafimran@live.com](ashrafimran@live.com))  \nThis work was supported by European University of Atlantic.  \nABSTRACT The agricultural sector is more vulnerable to the adverse effects of climate change and excessive pesticide application, posing a significant risk to global food security. Accurately predicting crop yields is essential for mitigating these risks and providing information on sustainable agricultural practices. This research presents a novel crop yield prediction system that utilizes a year’s worth of meteorological data, pesticide records, crop yield data, and machine learning techniques. We employed rigorous methods to gather, clean, and enhance data and then trained and evaluated three machine learning models: Gradient Boosting, K-Nearest Neighbors, and Multivariate Logistic Regression. We utilized the GridSearchCV method for hyper-parameter tweaking to identify the most suitable hyper-parameter throughout K-Fold cross-validation, aiming to improve the model’s performance by avoiding overfitting. The remarkable performance of the Gradient Boosting model, with an almost flawless coefficient of determination (R2 ) of 99 .99%, demonstrates its promise for precise yield prediction. This research also examined the correlation between projected and actual crop yields and identified the ideal meteorological conditions. It paves the way for data-driven methods in sustainable agriculture and resource distribution, ultimately leading to a more secure future with respect to food availability and resilience to climate change.  \nINDEX TERMS Agriculture, crop yield prediction, machine learning, deep learning.  \nI. INTRODUCTION  \nAagriculture is an economic endeavor that is significantly dependent on meteorological conditions [1] . The viability of seasonal agriculture depends on the prevailing natural weather conditions, sometimes called rainfed agriculture.  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Liandong Zhu.  \nRainfed agriculture, which covers approximately 80% of global cropland, demonstrates favorable crop yields when the weather conditions are favorable [2] . It is essential to recognize that agricultural productivity continues tobe significantly dependent on precipitation and several meteorological factors [3] . In certain cases, farmers may need more time to obtain the anticipated crop yield due to variations in rainfall and other meteorological factors, either due to scarcity or  \n􀀊 2024 The Authors. This work is licensed under a Creative Commons Attribution-","cbCaim3jBxABEl11","https://ap.wps.com/l/cbCaim3jBxABEl11","pdf",2250120,1,19,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"该研究如何构建用于预测作物产量的数据集？\",\"answer\":\"研究综合使用一年的气象数据、农药记录以及作物产量数据，并进行数据收集、清洗与增强后用于建模训练与评估。\"},{\"question\":\"本研究训练并比较了哪些机器学习模型？\",\"answer\":\"使用并评估了三种模型：Gradient Boosting、K-Nearest Neighbors以及Multivariate Logistic Regression。\"},{\"question\":\"如何避免模型过拟合并选择合适的超参数？\",\"answer\":\"通过GridSearchCV在K-Fold交叉验证框架下进行超参数调优，从而提升模型表现并降低过拟合风险。\"},{\"question\":\"Gradient Boosting模型的效果与关键气象条件发现是什么？\",\"answer\":\"Gradient Boosting取得接近完美的决定系数（R2约99.99%），并通过预测与真实产量的相关性分析，识别出更理想的气象条件。\"}]","Incorporating Meteorological Data and Pesticide Information to Forecast Crop Yields - Using Machine Learning | PDF",1785814141,48,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"incorporating-meteorological-data-and-pesticide-information-to-forecast-crop-yields-using-machine-learning","",{"@graph":36,"@context":89},[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/incorporating-meteorological-data-and-pesticide-information-to-forecast-crop-yields-using-machine-learning/123004/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"该研究如何构建用于预测作物产量的数据集？","Question",{"text":75,"@type":76},"研究综合使用一年的气象数据、农药记录以及作物产量数据，并进行数据收集、清洗与增强后用于建模训练与评估。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"本研究训练并比较了哪些机器学习模型？",{"text":80,"@type":76},"使用并评估了三种模型：Gradient Boosting、K-Nearest Neighbors以及Multivariate Logistic Regression。",{"name":82,"@type":73,"acceptedAnswer":83},"如何避免模型过拟合并选择合适的超参数？",{"text":84,"@type":76},"通过GridSearchCV在K-Fold交叉验证框架下进行超参数调优，从而提升模型表现并降低过拟合风险。",{"name":86,"@type":73,"acceptedAnswer":87},"Gradient Boosting模型的效果与关键气象条件发现是什么？",{"text":88,"@type":76},"Gradient Boosting取得接近完美的决定系数（R2约99.99%），并通过预测与真实产量的相关性分析，识别出更理想的气象条件。","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},"General","general"]