[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122947-en":3,"doc-seo-122947-105":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},122947,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","A Comprehensive Survey on Machine Learning and Deep Learning Techniques for Crop Disease Prediction in Smart Agriculture","Crop diseases caused by bacteria, fungi, and viruses threaten agricultural productivity, while manual daily inspection is labor-intensive and unreliable across diverse farms and conditions. Environmental factors and natural predators further complicate accurate assessment. This survey reviews machine learning and deep learning approaches for crop disease detection and prediction in smart agriculture, summarizing methods, parameters, and performance indicators. It also presents a case study for designing an effective learning model, identifies research gaps, and concludes that deep learning generally delivers higher efficiency than classical machine learning to help prevent crop loss.","|  | Nature Environment and Pollution Technology\u003Cbr>An International Quarterly Scientific Journal |  |  | p-ISSN: 0972-6268 (Print copies up to 2016)\u003Cbr>e-ISSN: 2395-3454 | Vol. 23 | No. 2 |  | pp. 619-632 |  | 2024 |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Original Research Paper |  |  |  [https://doi.org/10.46488/NEPT.2024.v23i02.003](https://doi.org/10.46488/NEPT.2024.v23i02.003) |  |  |  |  |  | Open Access Journal |  |  |\n\nA Comprehensive Survey on Machine Learning and Deep Learning Techniques for Crop Disease Prediction in Smart Agriculture  \nChatla Subbarayudu and Mohan Kubendiran†  \n*School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India †Corresponding author: Mohan Kubendiran; [mohan.k@vit.ac.in](mohan.k@vit.ac.in)  \nABSTRACT  \nDiseases caused by bacteria, fungi, and viruses are a problem for many crops. Farmers have challenges when trying to evaluate their crops daily by manual inspection across all forms of agriculture. Also, it is difficult to assess the crops since they are affected by various environmental factors and predators. These challenges can be addressed by employing crop disease detection approaches using artificial intelligence-based machine learning and deep learning techniques. This paper provides a comprehensive survey of various techniques utilized for crop disease prediction based on machine learning and deep learning approaches. This literature review summarises the contributions of a wide range of research works to the field of crop disease prediction, highlighting their commonalities and differences, parameters, and performance indicators. Further, to evaluate, a case study has been presented on how the paradigm shift will lead us to the design of an efficient learning model for crop disease prediction. It also identifies the gaps in knowledge that are supposed to be addressed to forge a path forward in research. From the survey conducted, it is apparent that the deep learning technique shows high efficiency over the machine learning approaches, thereby preventing crop loss.  \nINTRODUCTION  \nAdvancements in Information and Communication Technologies (ICT) and the Internet of Things (IoT) have revolutionized the agricultural industry by shaping the traditional method of farming in a smarter way to maximize yield and increase food production (Fountas et al. 2015) . Smart farming involves the coagulation of different technologies, namely wireless sensors connected to the Internet of Things, robotics, artificial intelligence, and cloud computing (Wolfert et al. 2017). Agriculture plays a vital role in promoting the growth and economy of a country. In India, agriculture plays a significant role in meeting the expanded food demand raised by the population. However, crops are prone to diseases due to various fungi, bacteria, and viruses (Vishnoi et al. 2021). Crops infected by these disease-causing organisms are supposed to be detected and treated in time (Chen et al. 2020a). If they are left undetected, it may dwindle food production. This may also worsen the food supply chain, which degrades the performance of agricultural activity (Liet al. 2021) . The traditional methods of manual inspection are not conducive, even for small-scale farmers.  \nFood loss per year accounts for nearly 37%, which has to be substantially reduced (Tiwari et al. 2021) . This also  \nresults in financial losses for the farmers. Traditionally, farmers rely on suggestions from experts when samples of infected crops are taken as specimens or a personal visit is needed. This process is tedious since it requires finding an expert and supervision. Sometimes, the findings may not be reliable or effective (Bera et al. 2019) . Therefore, it is essential to detect and treat crop diseases precisely. This challenge motivates the researchers to design an automatic, reliable, and efficient method to detect crop diseases (Hang et al. 2019). To assist in the p","cbCaimvWmIsw2QWs","https://ap.wps.com/l/cbCaimvWmIsw2QWs","pdf",924774,1,14,"English","en",105,"# Abstract\n# Introduction\n# Motivation\n# Crop Disease Detection Methods\n## Image Processing Pipeline\n## Machine Learning and Deep Learning Approaches\n# Datasets, Models, and Performance Indicators\n# Case Study and Research Gaps\n# Conclusion","[{\"question\":\"为什么需要利用机器学习和深度学习来进行作物病害预测？\",\"answer\":\"因为细致的人工检查难以覆盖复杂农业场景，且在环境因素和病原体多样性下难以准确评估。基于AI的检测与预测能够更自动、更高效地辅助识别与分类病害。\"},{\"question\":\"文中提到的图像处理在作物病害检测中包含哪些环节？\",\"answer\":\"图像处理通常包含采集、预处理、分割、特征提取、特征选择以及分类等阶段。文中也强调阈值方法、区域方法与颜色检测在诊断中具有优势。\"},{\"question\":\"卷积神经网络（CNN）在作物病害检测中有什么特点？\",\"answer\":\"CNN被认为是最常用的检测技术之一，能够实现自动特征提取、减少对人工监督的依赖，并在准确率与计算效率方面具有优势，且适合处理大规模数据集。\"}]","A Comprehensive Survey on Machine Learning and Deep Learning Techniques for Crop Disease Prediction in Smart Agriculture | PDF",1785813825,35,{"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},"a-comprehensive-survey-on-machine-learning-and-deep-learning-techniques-for-crop-disease-prediction-in-smart-agriculture","",{"@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/a-comprehensive-survey-on-machine-learning-and-deep-learning-techniques-for-crop-disease-prediction-in-smart-agriculture/122947/",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","2026-08-05","2026-08-04",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},"为什么需要利用机器学习和深度学习来进行作物病害预测？","Question",{"text":76,"@type":77},"因为细致的人工检查难以覆盖复杂农业场景，且在环境因素和病原体多样性下难以准确评估。基于AI的检测与预测能够更自动、更高效地辅助识别与分类病害。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"文中提到的图像处理在作物病害检测中包含哪些环节？",{"text":81,"@type":77},"图像处理通常包含采集、预处理、分割、特征提取、特征选择以及分类等阶段。文中也强调阈值方法、区域方法与颜色检测在诊断中具有优势。",{"name":83,"@type":74,"acceptedAnswer":84},"卷积神经网络（CNN）在作物病害检测中有什么特点？",{"text":85,"@type":77},"CNN被认为是最常用的检测技术之一，能够实现自动特征提取、减少对人工监督的依赖，并在准确率与计算效率方面具有优势，且适合处理大规模数据集。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]