[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290591-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290591-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","comparison-of-classification-algorithm-for-crop-decision-based-on-environmental-factors-using-machine-learning","Comparison of Classification Algorithm for Crop Decision based on Environmental Factors using Machine Learning","","Crop decision in agriculture depends on multiple interacting biotic and abiotic environmental conditions, including nitrogen, phosphorus, potassium, pH, temperature, humidity, and rainfall. Machine learning models are used to predict the most suitable crop for given environmental settings by applying data cleaning and feature selection, followed by training and testing splits. Logistic regression, decision tree, support vector machine, k-nearest neighbors, naïve Bayes, and random forest are compared using accuracy, implemented in Google Colab with Python libraries and WEKA for algorithm evaluation.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/comparison-of-classification-algorithm-for-crop-decision-based-on-environmental-factors-using-machine-learning/290591/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/comparison-of-classification-algorithm-for-crop-decision-based-on-environmental-factors-using-machine-learning/290591.png","ImageObject",300,407,{"name":42,"@type":43},"Oliver","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",19,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Which environmental factors are considered for crop decision prediction?","Question",{"text":63,"@type":64},"The study lists nitrogen, phosphorus, potassium, pH, temperature, humidity, and rainfall as key environmental factors affecting crop choice.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which machine learning algorithms are compared in the paper?",{"text":68,"@type":64},"The paper compares logistic regression, decision tree, support vector machine, k-nearest neighbors, naïve Bayes, and random forest.",{"name":70,"@type":61,"acceptedAnswer":71},"How are the algorithms implemented and evaluated?",{"text":72,"@type":64},"Implementations are carried out using Python on Google Colab with common ML libraries and also evaluated using WEKA, with comparisons based on accuracy across different training and testing splits.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290591,1789642142,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":116,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":80,"read_time":143,"preview_page_count":4},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Comparison of Classification Algorithm for Crop Decision based on Environmental Factors using  \nMachine Learning  \nGuruprasad Deshpande1, Rajani P.K.2, Vishal Khandagle3, Jayesh Kolhe4  \n1Department of Electronics and Telecommunication,  \nPimpri Chinchwad College of Engineering,  \nPune, India  \n[e-mail:](e-mail:1 guruprasad.deshpande19@pccoepune.org)[1](e-mail:1 guruprasad.deshpande19@pccoepune.org)[ guruprasad.deshpande19@pccoepune.org](e-mail:1 guruprasad.deshpande19@pccoepune.org)  \n2Department of Electronics and Telecommunication,  \nPimpri Chinchwad College of Engineering,  \nPune, India  \n[e-mail:](e-mail:2 rajani.pk@pccoepune.org)[2](e-mail:2 rajani.pk@pccoepune.org)[ rajani.pk@pccoepune.org](e-mail:2 rajani.pk@pccoepune.org)  \n3Department of Electronics and Telecommunication,  \nPimpri Chinchwad College of Engineering,  \nPune, India  \n[e-mail:](e-mail:3 vishal.khandagale19@pccoepune.org)[3](e-mail:3 vishal.khandagale19@pccoepune.org)[ vishal.khandagale19@pccoepune.org](e-mail:3 vishal.khandagale19@pccoepune.org)  \n4Department of Electronics and Telecommunication,  \nPimpri Chinchwad College of Engineering,  \nPune, India  \n[e-mail:](e-mail:4 jayesh.kolhe19@pccoepune.org)[4](e-mail:4 jayesh.kolhe19@pccoepune.org)[ jayesh.kolhe19@pccoepune.org](e-mail:4 jayesh.kolhe19@pccoepune.org)  \nAbstract. Crop decision is a very complex process. In Agriculture it plays a vital role. Various biotic and abiotic factors affect this decision. Some crucial Environmental factors are Nitrogen Phosphorus, Potassium, pH, Temperature, Humidity, Rainfall. Machine Learning Algorithm can perfectly predict the crop necessary for this environmental condition. Various algorithms and model are used for this process such as featureselection, data cleaning, Training, and testing split etc. Algorithms such as Logistic regression, Decision Tree, Support vector machine, KNearest Neighbour, Navies Bayes, Random Forest. A comparison based on the accuracy parameter is presented in this paper along with various training and testing split for optimal choice of best algorithm. This comparison is done on two tools i.e., on Google collab using python and its libraries for implementation of Machine Learning Algorithm and WEKA which is a pre-processing tool to compare various algorithm of machine learning.  \nKeywords: Crop Decision, Machine Learning, Algorithms, Google Collaboratory, WEKA, Accuracy, Comparison.  \nI. INTRODUCTION  \nCrop production, water management, fertilizer application, pestand disease management, harvesting, transportation of food products, packing, food preservation, food safety, food storage,  \nand food marketing are the primary stages of agricultural products. Thanks to the Internet of Things (IoT),[1] farmers may now have a global impact on technology and make wellinformed decisions that will increase production, improve resource management, and produce the desired outcomes with the least amount of waste. With IoT-based devices, technology supports our farmers throughout the entire farming process and even after. While many of them borrow money to cultivate a crop in the hopes of making a profit, the most important choice that farmers must make is usually made at random. This choice is made without much analysis and based on extremely general  \nconsiderations. If the crop he chooses has the right conditions,[2] the right demand, and government assistance, his decision could boost the farmer's revenue. As a result, we have suggested a platform that can give the farmer a suitable option. Aid them in recording their profit and understanding the effectiveness of their  \nsoil. The market and farmers will benefit from this platform's ability to produce an excess amount of a certain crop. [3] The platform would assist farmers in planning their harvest under optimal weather circumstances, together with crop efficiency and demand. [11] Additionally, this platform will shield them from losses brought on by bad weather or natural calamities.  \nII. TOOLS A","cbCaimYhJB8Leauk","https://ap.wps.com/l/cbCaimYhJB8Leauk","pdf",527358,"English","# Abstract\n# Introduction\n# Tools and Technology\n## Google Collab\n## Python\n## Weka","[{\"question\":\"Which environmental factors are considered for crop decision prediction?\",\"answer\":\"The study lists nitrogen, phosphorus, potassium, pH, temperature, humidity, and rainfall as key environmental factors affecting crop choice.\"},{\"question\":\"Which machine learning algorithms are compared in the paper?\",\"answer\":\"The paper compares logistic regression, decision tree, support vector machine, k-nearest neighbors, naïve Bayes, and random forest.\"},{\"question\":\"How are the algorithms implemented and evaluated?\",\"answer\":\"Implementations are carried out using Python on Google Colab with common ML libraries and also evaluated using WEKA, with comparisons based on accuracy across different training and testing splits.\"}]","Comparison of Classification Algorithm for Crop Decision based on Environmental Factors using Machine Learning | PDF",23]