[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120599-en":3,"doc-seo-120599-105":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},120599,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Enhanced Agricultural Decision-Making - Machine Learning Approaches for Crop Prediction and Analysis in India","Agriculture in India faces declining soil quality, unpredictable weather, and the need for more efficient decision-making for sustainable growth. The study presents machine learning as a transformative approach to improve agricultural decision-making through crop prediction and productivity analysis. Using a mixed-methods dataset with crops and environmental parameters, it develops predictive models that support crop selection optimization, disease outbreak prediction, and anticipation of market fluctuations. Experiments with KNN, SVM, Decision Tree, and Random Forest report strong accuracy and highlight improved yield potential and profitability.","Enhanced Agricultural Decision-Making: Machine Learning Approaches for Crop Prediction and Analysis in India  \nSandeep Gupta1,2, Abu Bakar Abdul Hamid1, Tadiwa Elisha Nyamasvisva3, Nitin Tyagi4 , Vishal  \nJain1,5, Ng Khai Mun1, Danish Ather6  \n1Kuala Lumpur University of Science & Technology, Unipark Suria, Jalan Ikram-Uniten, Kajang, Selangor, Kuala Lumpur, Malaysia  \n2Department of Computer Science & Engineering, SSCSE, Sharda University, Plot no. 32, 34 KP –III, Greater Noida 201301(U.P.), India  \n3Faculty of Engineering Science and Technology, Kuala Lumpur University of Science & Technology, Unipark Suria, Jalan Ikram-Uniten, Kajang, Selangor, Kuala Lumpur, Malaysia  \n4Department of CSE and Allied Branches, Accurate Institute of Management and Technology, Greater Noida, U. P.- 201306, India  \n5Department of Computer Science & Engineering, School of Engineering & Technology, Vivekananda Institute of Professional Studies-Technical Campus, New Delhi, India  \n6Amity University, Tashkent City, Street Labzak, Building-70, 100028, Uzbekistan  \nArticle Info  \nArticle history:  \nReceived April17, 2025 Revised June 15, 2025  \nAccepted July13, 2025  \nPublished November10, 2025  \nKeywords:  \nAgriculture Crop Prediction India  \nMachine Learning Precision Farming  \nCorresponding Author:  \nNitin Tyagi  \nProfessor and Head  \nThis paper addresses the critical aspects of agriculture in the Indian economy and the challenges faced by this sector, including soil quality decline, unpredictable weather, and the need for efficient decisionmaking. It presents machine learning as a transformative approach for improved agricultural decision-making, enabling enhanced crop prediction and productivity. Machine learning (ML) algorithms are shown to effectively analyze vast datasets to generate predictive models that aid in crop selection optimization, disease outbreak prediction, and market fluctuation anticipation, thus leading to increased yields and profitability. Focusing on crop prediction, the paper discusses models leveraging historical data and advanced algorithms to forecast crop yields. Additionally, the application of machine learning in precision farming, such as optimizing fertilizer application, is explored. The paper uses a mixed-method approach on a dataset encompassing various crops and environmental parameters. In this paper the various techniques such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Decision Tree (DT) and Random Forest (RF) algorithms have been employed to demonstrate the utility of ML in the agricultural fields. The KNN at the value of K=4 and SVM with polynomial kernel resulted the accuracy of 0.982 and 0.989 respectively. Whereas DT and RT gave the results in terms of accuracy of 0.987 and 0.970 respectively. Overall, it can be said that all these techniques used in the present work showed the better accuracy for agricultural sustainability.  \nABSTRACT  \nDepartment of Computer Science Engineering and Allied Branches Accurate Institute of Management and Technology  \nPlot 49, Knowledge Park III, Greater Noida-201306, Uttar Pradesh, India [Email: nitinnitin8218@gmail.com](Email: nitinnitin8218@gmail.com)  \n1. INTRODUCTION  \nAgriculture plays a critical role in the Indian economy, contributing significantly to the nation’s GDP and presuming livelihood for a vast majority of the population [1] . However, the sector faces numerous challenges, including declining soil quality, unpredictable weather patterns, and the need for more efficient decision-making processes [2] . To address these challenges, the application of ML in agriculture has emerged as a promising approach, offering opportunities for enhanced decision-making, improved crop prediction, and overall productivity enhancement as well as other predictive analysis [3- 5] .  \nML algorithms have the potential to analyze large datasets, including historical crop yields, weather patterns, soil characteristics, and market prices, to develop predictive models","cbCaisl3H0AlWjJB","https://ap.wps.com/l/cbCaisl3H0AlWjJB","pdf",724526,1,11,"English","en",105,"# Introduction\n## Challenges in Indian agriculture\n## Role of machine learning in decision-making\n# Crop prediction and analysis\n## Data-driven yield forecasting\n## Soil and environmental suitability insights\n# Precision farming applications\n## Optimizing inputs such as fertilizers\n# Methodology and models\n## Mixed-method dataset\n## KNN, SVM, Decision Tree, Random Forest results","[{\"question\":\"What challenges in Indian agriculture motivate the use of machine learning?\",\"answer\":\"The paper highlights declining soil quality, unpredictable weather patterns, and the need for more efficient decision-making processes. It also notes barriers such as limited data, restricted technology access, and the need for capacity building.\"},{\"question\":\"How does the proposed work use machine learning for crop prediction?\",\"answer\":\"It leverages historical data and environmental parameters to forecast crop yields. The models aim to help with planting and harvesting planning by predicting likely outcomes.\"},{\"question\":\"Which machine learning algorithms are evaluated, and what accuracy is reported?\",\"answer\":\"The paper evaluates K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Decision Tree (DT), and Random Forest (RF). Reported accuracies include KNN with K=4 at 0.982, SVM with a polynomial kernel at 0.989, DT at 0.987, and RF (listed as RT) at 0.970.\"}]","Enhanced Agricultural Decision-Making - Machine Learning Approaches for Crop Prediction and Analysis in India | PDF",1785730830,28,{"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},"enhanced-agricultural-decision-making-machine-learning-approaches-for-crop-prediction-and-analysis-in-india","",{"@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/enhanced-agricultural-decision-making-machine-learning-approaches-for-crop-prediction-and-analysis-in-india/120599/",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","2026-08-03",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 challenges in Indian agriculture motivate the use of machine learning?","Question",{"text":75,"@type":76},"The paper highlights declining soil quality, unpredictable weather patterns, and the need for more efficient decision-making processes. It also notes barriers such as limited data, restricted technology access, and the need for capacity building.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed work use machine learning for crop prediction?",{"text":80,"@type":76},"It leverages historical data and environmental parameters to forecast crop yields. The models aim to help with planting and harvesting planning by predicting likely outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms are evaluated, and what accuracy is reported?",{"text":84,"@type":76},"The paper evaluates K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Decision Tree (DT), and Random Forest (RF). Reported accuracies include KNN with K=4 at 0.982, SVM with a polynomial kernel at 0.989, DT at 0.987, and RF (listed as RT) at 0.970.","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"]