[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116990-en":3,"doc-seo-116990-105":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":20,"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},116990,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Study of Crop Yield Prediction Using Machine Learning Approaches","Agriculture underpins food supply, industrial raw materials, and fiber production, yet mounting population growth and climate uncertainty intensify pressure on farming systems to deliver higher output sustainably. Crop yield prediction has therefore become a critical research direction, enabling data-driven decisions that reduce resource waste and environmental impact. This paper studies the significance of predicting crop yields using machine learning approaches, outlining key methods, practical applications, and the potential to transform agricultural production.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-5 Year 2023 Page 1351:1354  \nA Study of Crop Yield Prediction Using Machine Learning Approaches  \nSatish Kumar Kalhotra1, K.C. Prakash2, Manoj Kumar Mishra3*, M S Annapurna  \nKishore Kumar4  \n1Professor Dept. of Education, Rajiv Gandhi University, Rono Hills, Doimukh, India. 2Assistant Professor, Agri-Business, Indian Institute of Plantation Management (IIPM) Bangalore, India *3Professor, Department of Economics, College of Business and Economics, Salale University Fitche, Email: [satish.kalhotra@rgu.ac.in](satish.kalhotra@rgu.ac.in), [kcp.iipmb@gmail.com](kcp.iipmb@gmail.com), [mkmishraeco@gmail.com](mkmishraeco@gmail.com),  \n[annapurnakishore@gmail.com](annapurnakishore@gmail.com)  \n*Corresponding author’[s E-mail: mkmishraeco@gmail.com](s E-mail: mkmishraeco@gmail.com)  \n\n| Article History\u003Cbr>Received: 06 June 2023\u003Cbr>Revised: 05 Sept 2023\u003Cbr>Accepted: 26 Oct 2023\u003Cbr>CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Agriculture plays a pivotal role in our society by providing food, fiber, and raw materials for various industries. The world's population is steadily growing, and there is increasing pressure on agriculture to meet the rising global food demand. In this context, the use of machine learning approaches to predict crop yields has gained significant importance. This paper aim is to study the significance of crop yield prediction through machine learning, its methods, applications, and its potential to revolutionize the agricultural sector.\u003Cbr>Keywords: Agriculture, food, crop, machine learning, raw materials |\n| --- | --- |\n\n1. Introduction  \nAgriculture, the foundation of human civilization, has continually evolved to meet the ever-increasing demands of our burgeoning global population. In this era of unprecedented demographic growth, climate uncertainty, and a pressing need for sustainable resource management, the agricultural sector faces an enormous challenge: ensuring food security while minimizing environmental impact. To address these challenges and revolutionize the way we approach farming, a new frontier has emerged at the intersection of agriculture and technology - the study of crop yield prediction using machine learning approaches.  \nAs the world's population approaches 8 billion and beyond, the critical importance of agriculture in sustaining human life cannot be overstated. It is the source of the food on our tables, the fibers in our clothing, and the raw materials for countless industries. In the face of a rising global demand for food, the agricultural sector is tasked with the herculean responsibility of enhancing productivity, minimizing resource waste, and reducing the environmental footprint of farming practices. Crop yield prediction through the prism of machine learning offers a transformative pathway toward these objectives.  \nNow, Machine learning, a subset of artificial intelligence, empowers computers to learn and make predictions or decisions without being explicitly programmed. When applied to agriculture, this technology becomes a potent tool for forecasting crop yields with unprecedented accuracy and granularity. It can harness the power of vast datasets, including historical crop performance, meteorological data, soil characteristics, and more, to unveil intricate relationships that influence crop yields. By doing so, machine learning enables us to not only anticipate crop outcomes but to empower farmers, policymakers, and stakeholders in the agricultural ecosystem with data-driven insights.  \nSheenoy et al. represented a paper that places an answer for the decrement in cost of transportation. The IOT-based methodology is used to decrease quantity of agents and middle hops between the clients and the ranchers that further supports the rancher. The paper ends up being the inspiration for the research work. The paper executes mechanisms that are integrated and provides a prediction-based mechanism that advise for crops which y","cbCaiaSWRCzDD47c","https://ap.wps.com/l/cbCaiaSWRCzDD47c","pdf",319551,1,4,"English","en",105,"# Introduction\n## Motivation and challenges in agriculture\n## Machine learning for crop yield forecasting\n## Related work and prior methods","[{\"question\":\"Why is crop yield prediction important in agriculture?\",\"answer\":\"Crop yield prediction helps address food security demands while reducing resource waste and minimizing farming’s environmental footprint under population growth and climate uncertainty.\"},{\"question\":\"How does machine learning support crop yield prediction?\",\"answer\":\"Machine learning learns from large datasets such as historical crop performance, meteorological data, and soil characteristics, enabling accurate forecasting and insights for farmers and policymakers.\"},{\"question\":\"What does the paper focus on regarding machine learning approaches?\",\"answer\":\"The paper examines the significance of crop yield prediction using machine learning, including key methods, applications, and the potential impact on the agricultural sector.\"}]","A Study of Crop Yield Prediction Using Machine Learning Approaches | 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is crop yield prediction important in agriculture?","Question",{"text":75,"@type":76},"Crop yield prediction helps address food security demands while reducing resource waste and minimizing farming’s environmental footprint under population growth and climate uncertainty.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning support crop yield prediction?",{"text":80,"@type":76},"Machine learning learns from large datasets such as historical crop performance, meteorological data, and soil characteristics, enabling accurate forecasting and insights for farmers and policymakers.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the paper focus on regarding machine learning approaches?",{"text":84,"@type":76},"The paper examines the significance of crop yield prediction using machine learning, including key methods, applications, and the potential impact on the agricultural 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