[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123316-en":3,"doc-seo-123316-105":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},123316,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","An Empirical Evaluation of Machine Learning Techniques for Crop Prediction","Agriculture drives national economic growth, making crop prediction essential for effective cultivation. Crop suitability depends on soil and environmental traits, where nutrient variation and climatic factors shape yield and farming decisions. This research conducts an empirical study using bagging, random forest, support vector machine, decision tree, Naïve Bayes, and k-nearest neighbor classifiers to identify appropriate cultivable crops based on soil and environment characteristics. Classifier suitability is assessed with a GitHub dataset, and results show the ensemble method outperforms other techniques across performance metrics.","International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 8, Nº4  \nAn Empirical Evaluation of Machine Learning Techniques for Crop Prediction  \nG. Mariammal1, A. Suruliandi2, S.P. Raja3, E. Poongothai4 *  \n1 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai-600 062, Tamilnadu,(India)  \n2 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli – 627012, Tamilnadu,(India)  \n3 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu,(India)  \n4 Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamilnadu,(India)  \nReceived 5 June 2021 | Accepted 11 April 2022 | Published 23 December 2022  \nAbstract   \nAgriculture is the primary source driving the economic growth of every country worldwide. Crop prediction, which is critical to agriculture, depends on the soil and environment. Nutrient levels differ from area to area and greatly influence in crop cultivation. Earlier, the tasks of crop forecast and cultivation were undertaken by farmers themselves. Today, however, crop prediction is determined by climatic variations. This is where machine learning algorithms step in to identify the most relevant crop for cultivation. This research undertakesan empirical analysis using the bagging, random forest, support vector machine, decision tree, Naïve Bayes and k-nearest neighbor classifiers to predict the most appropriate cultivable crop for certain areas, based on environment and soil traits. Further, the suitability of the classifiers is examined using a GitHub prisoners’dataset. The experimental results of all the classification techniques were assessed to show that the ensemble outclassed the rest with respect to every performance metric.  \nI. Introduction  \nGRICULTURE is key to the development of human civilization,  \nAwvariesithacrfaorsmsinargeapsl, aywiigh aecarcithicapl role inossessinthgeuprnioqcueesss.Coilr,opclcultimavicatiaonnd geographic characteristics. Soil is central to crop cultivation, and nutrients namely potassium, nitrogen, and phosphorus impact yield. Geography and climatic conditions, including the seasons, soil types, rainfall, and temperature also greatly influence in crop prediction. Based on these factors, the most suitable cultivable crop is predicted using several Machine Learning (ML) [1] techniques. Classification is fundamental to machine learning, for which it trains the system to obtain results using the given data. The supervised, unsupervised and reinforcement learning types of classification techniques are used in prediction. This research evaluates the performance of supervised learning techniques such as bagging, random forest (RF), support vector machine (SVM), decision tree (DT), Naïve Bayes (NB) and k-nearest neighbor (kNN) to predict a relevant crop for classification, using a GitHub prisoners’ dataset. This work identifies the best classifier for the forecasting process.  \n* Corresponding author.  \nE-mail addresses: [suba.g1212@mail.com](suba.g1212@mail.com) (G. Mariammal),  \n[suruliandi@yahoo.com](suruliandi@yahoo.com) (A. Suruliandi), [avemariaraja@gmail.com](avemariaraja@gmail.com)  \n(S.P. Raja), [poongothai.rp@gmail.com](poongothai.rp@gmail.com) (E. Poongothai)  \nA. Related Work  \nSeveral papers that illustrate key features of common ML models are discussed in this section.  \nSoil characteristics alone are used to predict a suitable crop for cultivation [1] . Belson et al. [2] described the DT classification model as a tree structure, with leaf nodes representing the final decision made after the top-to-bottom path is established. The most efficient techniques used in the literature survey include the Gaussian mixture, the Chi-square Automatic Interaction Detector (CHAID), classification and regression trees, and the Bayesian netw","cbCail3scrxYDhsD","https://ap.wps.com/l/cbCail3scrxYDhsD","pdf",853293,1,9,"English","en",105,"# Abstract\n# Introduction\n# Related Work","[{\"question\":\"哪些机器学习方法用于作物预测？\",\"answer\":\"研究使用 bagging、random forest、support vector machine、decision tree、Naïve Bayes 和 k-nearest neighbor 六类（并在文中以分类器形式组织）来预测适合种植的作物。\"},{\"question\":\"研究如何评估分类器的适用性？\",\"answer\":\"通过使用 GitHub 数据集对分类器进行适用性检验，并对不同分类技术的实验结果按多项性能指标进行对比评估。\"},{\"question\":\"实验结果表明哪类方法表现更好？\",\"answer\":\"综合学习（ensemble）方法在所有性能指标上优于其他分类技术。\"}]","An Empirical Evaluation of Machine Learning Techniques for Crop Prediction | PDF",1785815899,23,{"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},"an-empirical-evaluation-of-machine-learning-techniques-for-crop-prediction","",{"@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/an-empirical-evaluation-of-machine-learning-techniques-for-crop-prediction/123316/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"哪些机器学习方法用于作物预测？","Question",{"text":75,"@type":76},"研究使用 bagging、random forest、support vector machine、decision tree、Naïve Bayes 和 k-nearest neighbor 六类（并在文中以分类器形式组织）来预测适合种植的作物。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"研究如何评估分类器的适用性？",{"text":80,"@type":76},"通过使用 GitHub 数据集对分类器进行适用性检验，并对不同分类技术的实验结果按多项性能指标进行对比评估。",{"name":82,"@type":73,"acceptedAnswer":83},"实验结果表明哪类方法表现更好？",{"text":84,"@type":76},"综合学习（ensemble）方法在所有性能指标上优于其他分类技术。","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]