[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120330-en":3,"doc-seo-120330-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120330,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning Methods for Crop Yield Prediction and Climate Change Assessment in Agriculture - Research Focus","Agriculture drives India’s economy, yet food security faces pressure from population growth and rising demand. Crop yield prediction enables data-driven support for deciding which crops to grow and how to act during the growing season, using machine learning and deep learning under variable environmental conditions. The study evaluates yield estimation with rainfall, crop, meteorological conditions, area, production, and yield, comparing decision tree, random forest, and XGBoost regression against convolutional neural network and long short-term memory. Results show random forest achieving up to 98.96% accuracy with low errors and CNN reaching very small loss, yielding a strong predictive model.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 4 , April 2025  \n| \u003Cbr>|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|\u003Cbr>Volume 14, Issue 4, April 2025\u003Cbr>|DOI: 10.15680/IJIRSET.2025.1404501|\u003Cbr>Machine Learning Methods for Crop Yeild Prediciton and Climate Change Assessment in\u003Cbr>Agriculture |\n| --- |\n| \u003Cbr>Dr.S.Maruthuperumal\u003Cbr>\u003Cbr>Associate Professor, Department ofCSE, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, India\u003Cbr>Pujari Shivaram, R.Nithin Kumar, R.Praveen Reddy, P.Koushik Reddy\u003Cbr>B. Tech Students, Department ofCSE, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, India |\n| ABSTRACT: Agriculture contributes a significant amount to the economy of India due to the dependence on humanbeings for their survival. The main obstacle to food security is population expansion leading to rising demand for food. Farmers must produce more on the same land to boost the supply. Through crop yield prediction, technology can assist farmers in producing more. This paper ’s primary goal is to predict crop yield utilizing the variables of rainfall, crop, meteorological conditions, area, production, and yield that have posed a serious threat to the long-term viability of agriculture. Crop yield prediction is a decision-support tool that uses machine learning and deep learning that can be used to make decisions about which crops to produce and what to do in the crop ’s growing season. It can decide which crops to produce and what to do in the crop ’s growing season. Regardless of the distracting environment, machine learning and deep learning algorithms are utilized in crop selection to reduce agricultural yield output losses. To estimate the agricultural yield, machine learning techniques: decision tree, random forest, and XGBoost regression; deep learning techniques- convolutional neural network and long-short term memory network have been used. Accuracy, root mean square error, mean square error, mean absolute error, standard deviation, and losses are compared. Other machine learning and deep learning methods fall short compared to the random forest and convolutional neural network. The random forest has a maximum accuracy of 98.96%, mean absolute error of 1.97, root mean square error of 2.45, and standard deviation of 1.23. The convolutional neural network has been evaluated with a minimum loss of 0.00060. Consequently, a model is developed that, compared to other algorithms, predicts the yield quite well. The findings are then analyzed using the root mean square error metric to understand better how the model ’s errors compare to those of the other methods.\u003Cbr>I. INTRODUCTION\u003Cbr>Agriculture is vital for the development of the world. We, humans, benefit from agriculture one way or the other, which has made agriculture a key area of study. Farmers will always need information to refer to, most especially when growing crops that are not common in their land or culture .\u003Cbr>The average farmer has access to crude sources of information such as TV, radio, newspapers, fellow farmers, government agricultural agencies, farm supply, and traders. There is, therefore, a need for a system that allows farmers access to relevant information. Machine learning is among the trending technologies; hence, there exist several technologies and systems that run on a machine learning framework.\u003Cbr>Project Overview\u003Cbr>In recent times, several machine learning systems in agriculture have been tested and created. Research of several machine learning algorithms’ effectiveness in agriculture and other application domains has also been conducted and this is because machine learning is a very effective tool for efficient use of resources, p","cbCaimsSrlan8Opy","https://ap.wps.com/l/cbCaimsSrlan8Opy","pdf",1989036,1,"English","en",105,"# Abstract\n## Introduction\n## Project Overview\n## Literature Survey\n## Crop Yield Production Using Random Forest Algorithm","[{\"question\":\"What problem does the paper address in agriculture?\",\"answer\":\"The paper targets food security challenges caused by population growth and the need to increase production on the same land, while also handling climate-related unpredictability that leads to yield losses.\"},{\"question\":\"Which variables are used for crop yield prediction?\",\"answer\":\"The study uses rainfall, crop type, meteorological conditions, area, production, and yield to predict agricultural output and support crop selection decisions.\"},{\"question\":\"How do the evaluated models compare in performance?\",\"answer\":\"Random forest outperforms other methods, reporting up to 98.96% accuracy and low error metrics, while the convolutional neural network shows a very low minimum loss; the paper notes other machine learning/deep learning methods fall short.\"}]","Machine Learning Methods for Crop Yield Prediction and Climate Change Assessment in Agriculture - Research Focus | PDF",1785729495,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-methods-for-crop-yield-prediction-and-climate-change-assessment-in-agriculture-research-focus","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-methods-for-crop-yield-prediction-and-climate-change-assessment-in-agriculture-research-focus/120330/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the paper address in agriculture?","Question",{"text":74,"@type":75},"The paper targets food security challenges caused by population growth and the need to increase production on the same land, while also handling climate-related unpredictability that leads to yield losses.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which variables are used for crop yield prediction?",{"text":79,"@type":75},"The study uses rainfall, crop type, meteorological conditions, area, production, and yield to predict agricultural output and support crop selection decisions.",{"name":81,"@type":72,"acceptedAnswer":82},"How do the evaluated models compare in performance?",{"text":83,"@type":75},"Random forest outperforms other methods, reporting up to 98.96% accuracy and low error metrics, while the convolutional neural network shows a very low minimum loss; 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