[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117956-en":3,"doc-seo-117956-105":30,"detail-sidebar-cat-0-en-105":92},{"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},117956,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Sustainable Agriculture Practice using Machine Learning - Crop Recommendation and Plant Disease Detection","Changing climate patterns bring unpredictable rainfall, abnormal temperature drops, and heat waves, causing substantial environmental and agricultural losses. Machine learning offers practical tools to address these climate-driven challenges. This work builds a crop recommendation and plant disease detection system using publicly available datasets. Crop recommendation trains models with Decision Tree, Logistic Regression, Random Forest, SVM, and Multilayer Perceptron, reaching 99.31% accuracy with Random Forest. Disease detection compares CNNs including VGG16, ResNet50, and EfficientNetV2, achieving 96.07% with EfficientNetV2.","Sustainable Agriculture Practice using Machine  \nLearning  \nP. Ashwini1, K. Srividya2, Dr. N. Vadivelan3  \n1Department ofCSE, VASAVI College of Engineering  \nHyderbad, India  \n[ashwinireddy90@gmail.com](ashwinireddy90@gmail.com)  \n2Department ofCSE, VASAVI college of Engineering  \nHyderbad, India  \n[ksrividya0508@gmail.com](ksrividya0508@gmail.com)  \n3Professor  \n3Department ofCSE, Teegala Krishna Reddy Engineering College  \nHyderbad, India  \nAbstract-The changing climate has caused unpredictable rainfall, unusual temperature drops, and heat waves, leading to considerable damage to the environment. Fortunately Machine Learning has provided effective tools to address global issues, including agriculture. By employing different ML algorithms, it is possible to solve the agricultural problems caused by these climate changes. The objective of this article is to develop a system for crop recommendation and disease detection in a plant. Publicly available datasets were used for both tasks. For the crop recommendation system, feature extraction was performed, and the dataset was trained using various Machine Learning algorithms, namely Decision Tree, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron. The random forest algorithm achieved an excellent accuracy of 99.31%.For the plant disease identification system, CNN architectures like - VGG16, ResNet50, and EfficientNetV2-were trained and compared. Among these, EfficientNetV2 achieved high accuracy of 96.07% .  \nKeywords-SVM, Multilayer Perceptron(MLP), Random Forest(RF), CNN, VGG16, ResNet50, EfficientNetV2 .  \nI. INTRODUCTION  \nMachines with the ability to learn have the potential to tackle difficult problems that are difficult for people to solve. It is applicable in various fields, including agriculture, sports and business. It has the potential to carry out activities including categorization, prediction, and identification. The fundamental purpose of this article is to develop a website that addresses two urgent challenges, namely crop recommendation and crop disease identification. This approach will cater to the needs of the agricultural industry and will cater to the needs of the agricultural industry [1].In order to come up with answers to these problems, models were trained with the help of datasets that were available to the general public, and their findings were compared. The models demonstrated an acceptable level of accuracy was included and it may be utilized in the cloud. This was done so in order to make the models more accessible.  \nOver the course of the past five years, the climate has undergone substantial shifts, which has had a huge influence on agriculture. Lack of information of scientific agricultural practices frequently leads to the selection of crops that are not suitable for the intended use [2] . When it comes to making decisions, farmers sometimes have to rely on a limited amount of experience, which can make them more prone to making  \nmistakes. As a direct result of this, the agricultural industry suffers enormous losses as a direct result of the inefficient exploitation of essential information, such as the composition of the soil, the pH of the soil, and the prompt detection of plant diseases [3] . As a direct result of this, the agricultural sector suffers enormous losses as a direct result of the inefficient exploitation of vital information. The issue can be fixed by employing modern technology in an effective manner, which will make this possible. Due to the fact that it employs both machine learning and the web, this strategy is able to target individuals who have access to mobile phones that are capable of connecting to the internet.This paper is arranged in 8 sections; Section 2 presents Literature review, Section 3&4 covers Classification algorithms, Section 5&6 describes crop recommendation system and plant disease detection, Section 7 presents Results and section 8 includes conclusion & future sc","cbCailmads4UeQr5","https://ap.wps.com/l/cbCailmads4UeQr5","pdf",348946,1,5,"English","en",105,"# Introduction\n## Literature Survey\n## Classification Algorithm\n## Crop Recommendation System\n## Plant Disease Detection\n## Results\n## Conclusion & Future Scope","[{\"question\":\"What problems does the proposed system address?\",\"answer\":\"It targets two urgent agricultural challenges: crop recommendation and crop disease identification under climate-related uncertainty.\"},{\"question\":\"Which algorithms are used for crop recommendation and what accuracy is reported?\",\"answer\":\"It trains models with Decision Tree, Logistic Regression, Random Forest, SVM, and Multilayer Perceptron; Random Forest achieves 99.31% accuracy.\"},{\"question\":\"How is plant disease detection performed and which CNN works best?\",\"answer\":\"Plant disease detection uses CNN architectures (VGG16, ResNet50, EfficientNetV2). EfficientNetV2 delivers the highest accuracy at 96.07%.\"}]","Sustainable Agriculture Practice using Machine Learning - Crop Recommendation and Plant Disease Detection | PDF",1785680522,13,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"sustainable-agriculture-practice-using-machine-learning-crop-recommendation-and-plant-disease-detection","",{"@graph":36,"@context":86},[37,54,69],{"@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/sustainable-agriculture-practice-using-machine-learning-crop-recommendation-and-plant-disease-detection/117956/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-09-04","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problems does the proposed system address?","Question",{"text":76,"@type":77},"It targets two urgent agricultural challenges: crop recommendation and crop disease identification under climate-related uncertainty.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which algorithms are used for crop recommendation and what accuracy is reported?",{"text":81,"@type":77},"It trains models with Decision Tree, Logistic Regression, Random Forest, SVM, and Multilayer Perceptron; Random Forest achieves 99.31% accuracy.",{"name":83,"@type":74,"acceptedAnswer":84},"How is plant disease detection performed and which CNN works best?",{"text":85,"@type":77},"Plant disease detection uses CNN architectures (VGG16, ResNet50, EfficientNetV2). 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