[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121830-en":3,"doc-seo-121830-105":30,"detail-sidebar-cat-0-en-105":90},{"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},121830,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Agricultural Crop Recommendation - Crop Disease Detection and Price Prediction Using Machine Learning","Agriculture underpins India’s economy, yet farmers often struggle to profit due to limited market access and the pressure of intermediaries that suppress prices. The work proposes an agricultural produce application enabling direct communication between farmers and retailers, supporting product reviews, crop yield-rate prediction, and price prediction from production quantity and historical sales. It further targets climate-impacted farming risks by identifying crop diseases and recommending suitable crops using public datasets, with classical ML models and CNN architectures compared for performance.","Agricultural Crop Recommendation, Crop Disease Detection and Price Prediction Using Machine  \nLearning  \n*Tumma Susmitha1, Jinkala Swathy2, Duvva Laxmiprasanna3  \n1Computer Science & Engineering  \nVasavi College of Engineering  \nHyderabad, India  \n* [susmitha.vce@gmail.com](susmitha.vce@gmail.com)  \n2Computer Science & Engineering Vasavi College of Engineering  \nHyderabad, India  \n[swathijinka231@gmail.com](swathijinka231@gmail.com)  \n3Computer Science & Engineering  \nVasavi College of Engineering  \nHyderabad, India  \n[duvva.prasanna5@gmail.com](duvva.prasanna5@gmail.com)  \nAbstract—India's foundation is its agriculture. With over 60% of the workforce employed and producing over 18% of the nation's GDP, it is a vital sector of the Indian economy. Although there are many ways in which we can use technology to increase product production, a farmer can only profit if he is able to sell his crops. Three laws have been passed by the Indian government to encourage the export of agricultural products across the nation. But today, we witness farmers all over the nation fighting against these regulations to protect their rights. Farmers worry that big merchants will exploit them as puppets and undercut the price at which they sell their goods. After doing a thorough analysis of the situation, we developed the concept of creating an agricultural produce application that facilitates direct communication between farmers and retailers, allows for product reviews and crop yielding rate prediction, and predicts the price of agricultural produce based on quantity produced and previous years' sales rates. Unpredictable rains, unexpected temperature decreases, and heat waves have all been brought on by the shifting climate, and the ecosystem has suffered significant harm. Thankfully, machine learning has produced useful methods for tackling international problems, such as agriculture. These climate change-related agricultural issues can be resolved by using various machine learning methods. The purpose of this piece is to Create a method to identify crop diseases and suggest crops. For both objectives, publicly accessible datasets were utilized. Regarding the crop recommendation system, feature extraction was done, and a variety of machine learning methods were used to train the dataset, including Support Vector Machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Multilayer Perceptron. 99.30% accuracy was attained via the random forest algorithm.CNN architectures such as ResNet50, and EfficientNetV2 were trained and compared for the plant disease identification system. EfficientNetV2 outperformed the rest, with a high accuracy of 96.08% .  \nKeywords-: GPS Navigation, Decision Tree, SVM, Multilayer Perceptron(MLP), Random Forest(RF), CNN, ResNet50, EfficientNetV2  \nI. INTRODUCTION  \nAgriculture produced in our nation needs a strong market. Farmers find it challenging to get customers to buy their goods. India's farmers have limited options for where to sell their goods at markets. All states, with the exception of three, mandate that farm produce be marketed and sold through state-owned mandis, or retail marketplaces, where middlemen put pressure on growers to raise their profit margins. Crop Cost Forecasting, Language Interpreter, Sorting by the farmer's or customer's geographic proximity,  \ntailoring the app to that farmer's crop and profit, etc., utilizing machine learning, deep learning algorithms, such asthe Decision Tree Regression Algorithm for Price Prediction, and other methods like GPS navigation, KNN, Haversine, nearest neighbor search, load balancing, This application would be a great crop selling tool for farmers that make significant profits by eliminating middlemen and mediators entirely, provided there is market analysis and a few number of APIs for geographic proximity, among other factors. An online shopping software that satisfies all needs  \nfor farmers to sell their goods, learn about the costs and revenues","cbCaispPnvEFmWEc","https://ap.wps.com/l/cbCaispPnvEFmWEc","pdf",186689,1,4,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What main problems does this work address for farmers?\",\"answer\":\"It addresses crop disease identification and crop suggestion, and it supports market-oriented functions like yield-rate and price prediction to help farmers sell more profitably.\"},{\"question\":\"How does the system recommend crops and predict outcomes?\",\"answer\":\"Crop recommendation uses feature extraction and multiple machine learning models trained on publicly accessible datasets.\"},{\"question\":\"Which models were evaluated for crop disease detection and with what results?\",\"answer\":\"ResNet50 and EfficientNetV2 CNN architectures were trained and compared; EfficientNetV2 achieved the highest accuracy at 96.08%.\"}]","Agricultural Crop Recommendation - Crop Disease Detection and Price Prediction Using Machine Learning | PDF",1785807105,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"agricultural-crop-recommendation-crop-disease-detection-and-price-prediction-using-machine-learning","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/agricultural-crop-recommendation-crop-disease-detection-and-price-prediction-using-machine-learning/121830/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What main problems does this work address for farmers?","Question",{"text":74,"@type":75},"It addresses crop disease identification and crop suggestion, and it supports market-oriented functions like yield-rate and price prediction to help farmers sell more profitably.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the system recommend crops and predict outcomes?",{"text":79,"@type":75},"Crop recommendation uses feature extraction and multiple machine learning models trained on publicly accessible datasets.",{"name":81,"@type":72,"acceptedAnswer":82},"Which models were evaluated for crop disease detection and with what results?",{"text":83,"@type":75},"ResNet50 and EfficientNetV2 CNN architectures were trained and compared; 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