[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116988-en":3,"doc-seo-116988-105":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},116988,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning for Leaf Disease Classification - Data, Techniques and Applications","Growing demand for sustainable agriculture drives advanced information technologies that strengthen plant pathology workflows. Machine learning, a branch of artificial intelligence, has produced breakthroughs that improve leaf disease classification across academic research and industrial deployments. This survey consolidates current progress by covering publicly available datasets, summarizing widely used machine learning methods including shallow, deep, and augmented learning, and outlining relevant application scenarios. It serves researchers, engineers, managers, and entrepreneurs pursuing smart agriculture.","arXiv :2310 . 12509v1 [ cs .CV] 19 Oct 2023  \nSpringer Nature 2021 LATEX template  \nMachine Learning for Leaf Disease Classification: Data, Techniques and Applications  \nJianping Yao 1 , Son N. Tran 1*, Samantha Sawyer2  \nand Saurabh Garg 1  \n1 School of Information and Communication Technology, University of Tasmania, Launceston, 7248, TAS, Australia.  \n2 Tasmania Institute of Agriculture, University of Tasmania, Prospect, 7250, TAS, Australia.  \n*Corresponding author(s). E-mail(s): [sn.tran@utas.edu.au](sn.tran@utas.edu.au) ; Contributing [authors: jianping.yao@utas.edu.au](authors: jianping.yao@utas.edu.au);  \n[samantha.saywer@utas.edu.au](samantha.saywer@utas.edu.au) ; [saurabh.garg@utas.edu.au](saurabh.garg@utas.edu.au) ;  \nAbstract  \nThe growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular.  \nKeywords: Plant Pathology, Leaf Disease, Machine Learning, Deep Learning, Augmented Learning, Smart Agriculture.  \nSpringer Nature 2021 LATEX template  \n2 ML for Leaf Disease Classification: Data, Techniques & Apps  \n1 Introduction  \nIn recent years, Machine Learning (ML) has been emerging as a game changerin multiple aspects of life. In agriculture, machine learning has been widely used as an effective means of production, including but not limited to automatic harvesting machines, production estimation, pest control, weeds control, irrigation control, plant pathology (leaf disease classification), and fruit classification. Generally, diseases of a plant can react in different parts, such as its leaves, flowers and roots. Among them, plants’ leaf is one of the most dominant and pronounced parts. Because leaves can participate in providing the nutrients the plant needs to grow, which is the photosynthesis in leaves produces the chlorophyll from sunlight [1] . Some disease of leaves may cause their drop or wither, directly affecting the plant’s yield and even survival. Furthermore, it will bring negative impacts, leading to crop productivity decrease, and production costs rise. In the past, farms generally rely on labour and experts for routine inspections and disease management. Their disadvantages are obvious. First, lots of manpower and costs are required. Second, labours need training and easily get fatigued on manual jobs. Third, it is difficult to detect leaf disease timely and on a large scale. Forth, diagnosis may be subjective due to human errors and bias. Thus, an effective leaf disease classification approach is the most basic need for plant cultivation. Fortunately, ML approaches have been recently emerging as a better solution compared to traditional methods, showing their effectiveness and ease of use in plant leaf pathology classification through plant leaf image analysing. Plant leaf images have several advantages. Datasets of leaves are relatively easy to collect, analyse and reproduc","cbCaisc5WwaetwIe","https://ap.wps.com/l/cbCaisc5WwaetwIe","pdf",8638338,1,46,"English","en",105,"# Introduction\n## Motivation and challenges of leaf disease diagnosis\n## Machine learning’s role in smart agriculture\n# Survey scope and structure\n## Public datasets\n## Learning techniques: traditional, deep, augmented\n## Applications and future directions","[{\"question\":\"Why is leaf disease classification important for agriculture?\",\"answer\":\"Leaf diseases can cause leaf drop or withering, reducing yield and even threatening plant survival. They also increase production costs and complicate large-scale, timely diagnosis.\"},{\"question\":\"How does the paper structure the survey on leaf disease classification?\",\"answer\":\"It begins with publicly available datasets, then summarizes common machine learning techniques including shallow, deep, and augmented learning, and finally discusses related applications.\"},{\"question\":\"Who can benefit from this survey?\",\"answer\":\"Researchers, engineers, managers, and entrepreneurs seeking a comprehensive view of recent machine learning technologies and applications for leaf disease detection in smart agriculture.\"}]","Machine Learning for Leaf Disease Classification - Data, Techniques and Applications | PDF",1785672981,116,{"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},"machine-learning-for-leaf-disease-classification-data-techniques-and-applications","",{"@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/machine-learning-for-leaf-disease-classification-data-techniques-and-applications/116988/",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","2026-08-02",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},"Why is leaf disease classification important for agriculture?","Question",{"text":75,"@type":76},"Leaf diseases can cause leaf drop or withering, reducing yield and even threatening plant survival. They also increase production costs and complicate large-scale, timely diagnosis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper structure the survey on leaf disease classification?",{"text":80,"@type":76},"It begins with publicly available datasets, then summarizes common machine learning techniques including shallow, deep, and augmented learning, and finally discusses related applications.",{"name":82,"@type":73,"acceptedAnswer":83},"Who can benefit from this survey?",{"text":84,"@type":76},"Researchers, engineers, managers, and entrepreneurs seeking a comprehensive view of recent machine learning technologies and applications for leaf disease detection in smart agriculture.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]