[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121635-en":3,"doc-seo-121635-105":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},121635,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","An Empirical Evaluation of Machine Learning Techniques for Crop Prediction - read online","Agriculture drives economic growth worldwide, and reliable crop prediction depends on soil and environmental traits that vary by region. Machine learning algorithms are applied to identify suitable cultivable crops using soil indicators and environmental factors. The study performs empirical evaluation with bagging, random forest, support vector machine, decision tree, Naïve Bayes, and k-nearest neighbor classifiers, then compares their effectiveness across multiple performance metrics. Classifier suitability is further examined using a GitHub dataset, where ensemble learning outperforms other approaches across all metrics.","An 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 | Early Access 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.  \n I. Introduction  A. Related Work  \nClassification,  \nCrop Rediction, Environmental Characteristics, Machine Learning, Soil Characteristics.  \nDOI: 10. 9781/ijimai.2022.12.004  \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)  \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), classi","cbCaivCs80jN5WV0","https://ap.wps.com/l/cbCaivCs80jN5WV0","pdf",829089,1,9,"English","en",105,"# Abstract\n# Introduction\n## Related Work\n## Methodology and Classifier Evaluation\n## Experimental Results","[{\"question\":\"Why is crop prediction important in agriculture?\",\"answer\":\"Crop prediction supports cultivation decisions and depends strongly on soil and environmental conditions that vary across regions.\"},{\"question\":\"Which machine learning classifiers are evaluated in this research?\",\"answer\":\"The study evaluates bagging, random forest, support vector machine, decision tree, Naïve Bayes, and k-nearest neighbor classifiers.\"},{\"question\":\"How do the classifiers’ performances compare in the experiments?\",\"answer\":\"Experimental results show that the ensemble approach outclasses the other techniques across every performance metric.\"}]","An Empirical Evaluation of Machine Learning Techniques for Crop Prediction - read online | PDF",1785805855,23,{"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-read-online","",{"@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-read-online/121635/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is crop prediction important in agriculture?","Question",{"text":75,"@type":76},"Crop prediction supports cultivation decisions and depends strongly on soil and environmental conditions that vary across regions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning classifiers are evaluated in this research?",{"text":80,"@type":76},"The study evaluates bagging, random forest, support vector machine, decision tree, Naïve Bayes, and k-nearest neighbor classifiers.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the classifiers’ performances compare in the experiments?",{"text":84,"@type":76},"Experimental results show that the ensemble approach outclasses the other techniques across every performance metric.","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"]