{"id":6306,"date":"2025-04-08T15:41:53","date_gmt":"2025-04-08T10:11:53","guid":{"rendered":"https:\/\/www.bhumipublishing.com\/jsri\/jsri-issue\/prediction-of-crop-yield-production-system-a-machine-learning-approach\/"},"modified":"2025-04-08T15:41:53","modified_gmt":"2025-04-08T10:11:53","slug":"prediction-of-crop-yield-production-system-a-machine-learning-approach","status":"publish","type":"jsri-issue","link":"https:\/\/www.bhumipublishing.com\/jsri\/jsri-issue\/prediction-of-crop-yield-production-system-a-machine-learning-approach\/","title":{"rendered":"PREDICTION OF CROP YIELD PRODUCTION SYSTEM: A MACHINE LEARNING APPROACH"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"<p>This research focuses on predicting crop yield and identifying the most suitable crop based on soil and environmental features using machine learning techniques. The study uses features like Nitrogen (N), Phosphorus (P), Potassium (K), Rainfall, pH level, and Soil Type to predict crop production. A Linear Regression algorithm is applied to train the model and predict crop yield. The results of the model can assist farmers in making better agricultural decisions, ultimately increasing productivity and profit.<\/p>\n","protected":false},"featured_media":0,"parent":0,"menu_order":0,"template":"","issues_type":[36],"class_list":["post-6306","jsri-issue","type-jsri-issue","status-publish","hentry","issues_type-vol-11-1-2025","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"ptb_metabox":{"ptb_jsri_issue_upload_pdf":{"url":["https:\/\/www.bhumipublishing.com\/jsri\/wp-content\/uploads\/2025\/04\/14.-Prediction-of-Crop-Yield-Production.pdf"],"title":[""]},"ptb_jsri_issue_author":"Sandesh S. Waingade, Nitin S. Chavan, A. M. Chopade, L. G. Mulik and N. S. Kulkarni","ptb_jsri_issue_keywords":"Crop Yield Prediction, Machine Learning, Linear Regression, Agricultural Data, Soil Nutrients"},"ptb_taxonomy":{"issues_type":[{"term_id":36,"name":"Vol. 11 (1) 2025","slug":"vol-11-1-2025","term_group":0,"term_taxonomy_id":36,"taxonomy":"issues_type","description":"","parent":0,"count":17,"filter":"raw"}]},"ptb_featured_image":null,"builder_content":"","_links":{"self":[{"href":"https:\/\/www.bhumipublishing.com\/jsri\/wp-json\/wp\/v2\/jsri-issue\/6306","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.bhumipublishing.com\/jsri\/wp-json\/wp\/v2\/jsri-issue"}],"about":[{"href":"https:\/\/www.bhumipublishing.com\/jsri\/wp-json\/wp\/v2\/types\/jsri-issue"}],"wp:attachment":[{"href":"https:\/\/www.bhumipublishing.com\/jsri\/wp-json\/wp\/v2\/media?parent=6306"}],"wp:term":[{"taxonomy":"issues_type","embeddable":true,"href":"https:\/\/www.bhumipublishing.com\/jsri\/wp-json\/wp\/v2\/issues_type?post=6306"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}