[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125469-en":3,"doc-seo-125469-105":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},125469,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Advanced Machine Learning Framework for Efficient Plant Disease Prediction - Paper","Machine learning methods are used as a core component in smart agriculture platforms to support farmers through a community-based assistance flow. The framework identifies plant diseases from affected images using deep learning, then applies natural language processing to rank solutions contributed by users. Communication is implemented via a Twitter-based message channel with bot-mediated verification. A concept drift approach adapts solutions to changing conditions such as seasonal variation. Experiments on a benchmark dataset produce accurate, reliable results.","Advanced Machine Learning Framework for Efficient  \nPlant Disease Prediction  \nAswath M Dept. of Electronics and Communication Engineering, VIT, Chennai, India, [m.aswath08@gmail.com](m.aswath08@gmail.com)  \nSowdeshwar S Dept. of Electronics and Communication Engineering, VIT, Chennai, India, [sowdeshstudies@gmail.com](sowdeshstudies@gmail.com)  \nSaravanan M  \nEricsson India Global Services Pvt. Ltd., Ericsson Research, Chennai, India, [m.saravanan@ericsson.com](m.saravanan@ericsson.com)  \nSatheesh K Perepu Ericsson India Global Services Pvt. Ltd., Ericsson Research, Chennai, India,  \nperepu.satheesh.kumar@ericsso[n.com](n.com)  \nAbstract—Recently, Machine Learning (ML) methods are built-in as an important component in many smart agriculture platforms. In this paper, we explore the new combination of advanced ML methods for creating a smart agriculture platform where farmers could reach out for assistance from the public, ora closed circle of experts. Specifically, we focus on an easy way to assist the farmers in understanding plant diseases where the farmers can get help to solve the issues from the members of the community. The proposed system utilizes deep learning techniques for identifying the disease of the plant from the affected image, which acts as an initial identifier. Further, Natural Language Processing techniques are employed for ranking the solutions posted by the user community. In this paper, a message channel is built on top of Twitter, a popular social media platform to establish proper communication among farmers. Since the effect of the solutions can differ based on various other parameters, we extend the use of the concept drift approach and come up with a good solution and propose it to the farmer. We tested the proposed framework on the benchmark dataset, and it produces accurate and reliable results.  \nKeywords—deep learning, natural language processing, concept drift, twitter platform  \nI. INTRODUCTION  \nAgriculture is the fundamental building block of a society. It is important to address the problems faced by farmers and provide them easy ways to follow necessary steps with an advanced technical application [1] . When there is not much technical assistance available in the local area, farmers can leverage the online platform to post their concerns and receive feedback. However, there is an issue of filtering out trusted individuals who give possible responses from the wrong ones. To manage this verification of users, we make use of a virtual bot account. This bot establishes a platform for the farmer’s tweets. The users’ solutions to the farmer’s queries are verified by the bot before it is considered for evaluation. After that, the bot replies to the responses to the farmer’s original tweet.  \nLack of advanced equipment and delay in the identification of plant diseases affect the quantity and quality of the crop yield to a great extent. According to studies, the losses due to plant diseases account for 20 to 40 percent of global annual productivity [2] . Extensive yield loss also contributes to various other factors like increased consumer prices which causes a bump in the earnings for the consumers. Hence, the need for timely identification of plant diseases will play a  \nvital role in ensuring good yield which is highly important for farmers and crop producers in remote areas as they cannot afford a loss in the present competitive scenario. The advancements in machine learning algorithms especially in image processing techniques made it possible to perform disease identification and these can be used in real-time to perform plant disease detection, which can save both time and cost [3] . As technology is developing every day, the devices have become cheaper with better resolution, good picture quality, and so on. Here, image processing has become very precise and effective as well. In this paper, we mainly aim at detecting various plant diseases by applying suitable image processing techniques an","cbCairRNMUBwfkWo","https://ap.wps.com/l/cbCairRNMUBwfkWo","pdf",751056,1,9,"English","en",105,"# Introduction\n## Problem background in plant disease identification\n## Proposed system overview and verification approach\n## Objectives and scope of the proposed framework","[{\"question\":\"How does the framework identify a plant disease from an image?\",\"answer\":\"It uses deep learning techniques to classify the disease from the affected plant image as an initial identifier.\"},{\"question\":\"How are community solutions ranked for a farmer’s query?\",\"answer\":\"Natural language processing ranks the solutions posted by the user community on the social platform.\"},{\"question\":\"Why is concept drift used in the system?\",\"answer\":\"Because the effectiveness of solutions can vary with parameters such as seasonal changes, concept drift helps the system adapt to those changes.\"}]","Advanced Machine Learning Framework for Efficient Plant Disease Prediction - Paper | PDF",1785899179,23,{"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},"advanced-machine-learning-framework-for-efficient-plant-disease-prediction-paper","",{"@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/advanced-machine-learning-framework-for-efficient-plant-disease-prediction-paper/125469/",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","2026-08-05",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},"How does the framework identify a plant disease from an image?","Question",{"text":75,"@type":76},"It uses deep learning techniques to classify the disease from the affected plant image as an initial identifier.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are community solutions ranked for a farmer’s query?",{"text":80,"@type":76},"Natural language processing ranks the solutions posted by the user community on the social platform.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is concept drift used in the system?",{"text":84,"@type":76},"Because the effectiveness of solutions can vary with parameters such as seasonal changes, concept drift helps the system adapt to those changes.","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"]