[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120542-en":3,"doc-seo-120542-105":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},120542,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Crop Classification and Yield Prediction Using Robust Machine Learning Models for Agricultural Sustainability","Agriculture underpins national economies by supporting food production, employment, and raw materials, yet persistent threats such as crop diseases, soil degradation, and water scarcity limit stable output. Machine learning, an AI capability, enables crop classification and yield forecasting while supporting automation for irrigation, fertilization, and crop selection to improve decision-making for food security. This study presents two robust learning architectures: crop recommendation via classification and wheat yield prediction via regression, validated with K-fold cross-validation and enhanced interpretability through XAI methods including feature importance and LIME.","Received 5 October 2024, accepted 22 October 2024, date of publication 25 October 2024, date of current version 13 November 2024. Digital Object Identifier 10.1109/ACCESS.2024.3486653  \nCrop Classification and Yield Prediction Using Robust Machine Learning Models for Agricultural Sustainability  \nABID BADSHAH1, BASEM YOUSEF ALKAZEMI2,(Senior Member, IEEE), FAKHRUD DIN1, KAMAL Z. ZAMLI3,4,(Member, IEEE), AND MUHAMMAD HARIS5  \n1Faculty of Information Technology (IT), Department of Computer Science and IT, University of Malakand, Dir Lower, Chakdara, Khyber Pakhtunkhwa 18800, Pakistan  \n2Department of Software Engineering, College of Computing, Umm Al-Qura University, Makkah 24382, Saudi Arabia  \n3Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pekan, Kuantan, Pahang 26600, Malaysia  \n4Faculty of Science and Technology, Universitas Airlangga, C Campus JI. Dr. H. Soekamo, Mulyorejo, Surabaya 60115, Indonesia  \n5Department of Computer Science and Bioinformatics, Khushal Khan Khattak University, Karak, Pakistan Corresponding author: Kamal Z. Zamli ([kamalz@ump.edu.my](kamalz@ump.edu.my))  \nABSTRACT Agriculture is pivotal for the economy of a country as it is a major source of food, employment and raw materials. However, challenges such as diseases, soil degradation, and water scarcity persist. Technology adoption can address these issues, improving production and quality. Machine learning, a subset of Artificial Intelligence (AI), enables prediction, classification, and automation in agriculture. It optimizes irrigation, fertilization, and crop selection, aiding decision-making for food security and crop management. This study proposes two robust machine learning architectures for classification and regression based on distinct datasets. Firstly, we delve into a crop recommendation dataset obtained from Kaggle, consisting of various input attributes such as the pH of the soil, temperature, humidity, and nutrient levels. Leveraging machine learning classification techniques such as Extra Tree Classifier (ETC), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbour (KNN), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM), we suggest twenty-two different crops founded on these inputs. Through the use of K-fold cross-validation, Explainable AI (XAI) and feature engineering, we identify the bestperforming model, with Random Forest coming out on top scoring an accuracy of 99.7% with precision, recall, F1 score, and confusion matrix. Secondly, we investigate wheat yield prediction data snagged from the World Bank and Food and Agriculture Organization (FAO), covering the years 1992-2013 for Pakistan. Using Multivariate Imputation by Chained Equations (MICE) to tackle data restrictions, we gauge wheat production for 2014-2024 and forecast the 2025 yield using machine learning regression models. Once again, using hyper parameter tuning with K-fold cross-validation, Support Vector Regressor (SVR) stands out asthe top-performing model, achieving an accuracy of 99 .9% with R2 Score. Transparency and confidence in agricultural decision-making are increased when machine learning decisions are made comprehensible using Explainable AI (XAI) approaches. Two widely used XAI approaches, namely Feature Importance and Local Interpretable Model-Agnostic Explanations (LIME) are used to interpret and explain outcomes of the proposed models. The study can increase agricultural productivity, minimize risks, enhance food security, and promote more environmentally friendly farming approaches.  \nINDEX TERMS Agricultural planning, crop recommendation, crop yield forecasting, explainable AI, K-fold cross-validation, machine learning.  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Ikramullah Lali.  \nI. INTRODUCTION  \nMany Asian countries rely heavily on agriculture as the foundation of their economy and the main source of income  \nVOLUME 12","cbCaicmJCAQtONjt","https://ap.wps.com/l/cbCaicmJCAQtONjt","pdf",2223868,1,15,"English","en",105,"# Abstract\n# I. Introduction\n## Agriculture challenges and motivation\n## Machine learning in precision agriculture\n## Proposed approach overview\n# Methodology (from abstract)\n## Crop recommendation dataset and classification models\n## Wheat yield prediction dataset and regression models\n## Data handling, validation, and hyperparameter tuning\n## Explainable AI interpretation (feature importance, LIME)\n# Expected impact","[{\"question\":\"What problem does the study address in agricultural decision-making?\",\"answer\":\"The study targets limitations in reliable crop selection and yield forecasting amid challenges like diseases, soil degradation, and water scarcity, aiming to improve production quality and reduce risks through data-driven models.\"},{\"question\":\"How is crop recommendation performed in the proposed approach?\",\"answer\":\"A Kaggle crop recommendation dataset with soil pH, temperature, humidity, and nutrient attributes is used to train multiple classifiers, where Random Forest achieved the top accuracy of 99.7% with strong evaluation metrics.\"},{\"question\":\"How is wheat yield prediction carried out and interpreted?\",\"answer\":\"Wheat data for Pakistan (1992–2013) from World Bank and FAO is used with MICE to handle missing restrictions, then regression models forecast 2014–2024 production and the 2025 yield; interpretability is provided using Explainable AI methods such as feature importance and LIME.\"}]","Crop Classification and Yield Prediction Using Robust Machine Learning Models for Agricultural Sustainability | PDF",1785730567,38,{"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},"crop-classification-and-yield-prediction-using-robust-machine-learning-models-for-agricultural-sustainability","",{"@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/crop-classification-and-yield-prediction-using-robust-machine-learning-models-for-agricultural-sustainability/120542/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in agricultural decision-making?","Question",{"text":75,"@type":76},"The study targets limitations in reliable crop selection and yield forecasting amid challenges like diseases, soil degradation, and water scarcity, aiming to improve production quality and reduce risks through data-driven models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is crop recommendation performed in the proposed approach?",{"text":80,"@type":76},"A Kaggle crop recommendation dataset with soil pH, temperature, humidity, and nutrient attributes is used to train multiple classifiers, where Random Forest achieved the top accuracy of 99.7% with strong evaluation metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"How is wheat yield prediction carried out and interpreted?",{"text":84,"@type":76},"Wheat data for Pakistan (1992–2013) from World Bank and FAO is used with MICE to handle missing restrictions, then regression models forecast 2014–2024 production and the 2025 yield; 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