[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118017-en":3,"doc-seo-118017-105":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},118017,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning-Based Jamun Leaf Disease Detection - A Comprehensive Review","Jamun leaf diseases threaten agricultural productivity by reducing both crop yield and fruit quality. Machine learning enables earlier detection and more accurate diagnosis, which supports effective crop management. No jamun-specific automated system is widely established yet, so the review compiles image-based plant leaf disease approaches that can be adapted to jamun. It evaluates Vision Transformer variants and CNN-based methods, analyzing strengths, limitations, and dataset performance to guide future research in classification accuracy.","Machine Learning-Based Jamun Leaf Disease Detection: A  \nComprehensive Review  \nAuvick Chandra Bhowmik  \nResearcher  \nDepartment of Computer Science and Engineering  \nDaffodil International University, Dhaka, Bangladesh  \n[auvick.bhowmik@yahoo.com](auvick.bhowmik@yahoo.com)  \nDr. Md. Taimur Ahad  \nAssociate Professor  \nDepartment of Computer Science and Engineering  \nDaffodil International University, Dhaka, Bangladesh  \n[taimurahad.cse@diu.edu.bd](taimurahad.cse@diu.edu.bd)  \nYousuf Rayhan Emon  \nTeaching Assistant  \nDepartment of Computer Science and Engineering  \nDaffodil International University, Dhaka, Bangladesh  \n[yousuf15-3220@diu.edu.bd](yousuf15-3220@diu.edu.bd)  \nAbstract: Jamun leaf diseases pose a significant threat to agricultural productivity, negatively impacting both yield and quality in the jamun industry. The advent of machine learning has opened up new avenues for tackling these diseases effectively. Early detection and diagnosis are essential for successful crop management. While no automated systems have yet been developed specifically for jamun leaf disease detection, various automated systems have been implemented for similar types of disease detection using image processing techniques. This paper presents a comprehensive review of machine learning methodologies employed for diagnosing plant leaf diseases through image classification, which can be adapted for jamun leaf disease detection. It meticulously assesses the strengths and limitations of various Vision Transformer models, including Transfer learning model and vision transformer (TLMViT),  \nSLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper reviews models such as Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural Network (CNN), and Locally Reversible Transformer. These machine-learning models have been evaluated on various datasets, demonstrating their real-world applicability. This review not only sheds light on current advancements in the field but also provides valuable insights for future research directions in machine learning-based jamun leaf disease detection and classification.  \nKeywords: ViT, TLMViT, SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, PMViT, EfficientNet, ResNet and Ensemble model.  \nIntroduction:  \nThe Vision Transformer (ViT) stands as a neural network architecture in deep learning designed for image classification purposes (Alzahrani et al., 2023) . Functioning as a detection method rooted in pattern recognition and deep learning, Vision Transformer (ViT) possesses the ability to automatically adapt to image features and employ these features for image classification and prediction (Fu et al., 2023) . The evolution of transformer architecture, prominently featured in natural language processing (NLP) innovation, has led to the emergence of Vision Transformers (ViT), which, being rooted in Natural Language Processing (NLP), has garnered significant attention in the realm of image classification (Hosseini et al., 2023) . In contrast to traditional CNNs that rely on convolution-based architecture, ViT employs a transformer-based architecture, notably effective in tasks related to natural language processing (Alzahrani et al., 2023; Ahmed et al., 2023) . ViT aligns with the established data flow pattern of transformers, facilitating its integration with diverse data types (Li et al., 2023) . The acyclic network structure of the transformer, coupled with parallel computing through encoder-decoder and self-attention mechanisms, significantly reduces training time and enhances performance in machine translation (Zhan et al., 2023) . This marks a significant advancement in visual-based deep learning, where vision transformers have demonstrated substantial promise across tasks extending beyond mere classification (Zhan et al., 2023) . Its remarkable ability to simulate long-range dependencies usin","cbCaia9HqDakVJt2","https://ap.wps.com/l/cbCaia9HqDakVJt2","pdf",295633,1,21,"English","en",105,"# Introduction\n## Vision Transformer (ViT) background\n## Motivation for jamun leaf disease detection\n## Dataset and automated diagnosis context\n# Comprehensive review overview\n## Vision Transformer model families\n## CNN and hybrid/ensemble models\n## Evaluation on datasets\n# Strengths, limitations, and future directions","[{\"question\":\"Why is early detection of jamun leaf diseases important?\",\"answer\":\"Early detection and diagnosis are essential for successful crop management. It helps address disease impact on yield and fruit quality at the right time.\"},{\"question\":\"What approach does the review focus on for jamun leaf disease detection?\",\"answer\":\"The review focuses on machine learning methods using image classification. It centers on Vision Transformer-based models and also covers CNN and ensemble approaches that can be adapted to jamun.\"},{\"question\":\"Which model families are assessed in the comprehensive review?\",\"answer\":\"The review evaluates Vision Transformer variants such as TLMViT, SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. It also discusses DenseNet, ResNet-50V2, EfficientNet, CNN, and ensemble methods, along with other transformer-based models.\"}]","Machine Learning-Based Jamun Leaf Disease Detection - A Comprehensive Review | PDF",1785680768,53,{"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},"machine-learning-based-jamun-leaf-disease-detection-a-comprehensive-review","",{"@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/machine-learning-based-jamun-leaf-disease-detection-a-comprehensive-review/118017/",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","2026-08-02",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},"Why is early detection of jamun leaf diseases important?","Question",{"text":76,"@type":77},"Early detection and diagnosis are essential for successful crop management. It helps address disease impact on yield and fruit quality at the right time.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What approach does the review focus on for jamun leaf disease detection?",{"text":81,"@type":77},"The review focuses on machine learning methods using image classification. It centers on Vision Transformer-based models and also covers CNN and ensemble approaches that can be adapted to jamun.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model families are assessed in the comprehensive review?",{"text":85,"@type":77},"The review evaluates Vision Transformer variants such as TLMViT, SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. 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