{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/real-time-tea-leaf-disease-detection-using","title":"Real-Time Tea Leaf Disease Detection Using Deep Learning-Based Models","arxiv_id":null,"date":"2024-12-20","proceeding":"International Conference on Computer and Information Technology (ICCIT) 2024 12","authors":["Swapnil Sharma Sarker","Ashiqul Islam","Raufun Talukder Raktim","Sanjana Akter Roshni","Sajib Kumar Saha Joy"],"abstract":"Tea leaf diseases pose a significant threat to crop productivity, highlighting the need for efficient and accurate detection methods. The lack of cost-effective, lightweight models for deployment on end devices limits real-time detection. This study addressed this by annotating and utilizing the previously unlabeled Tea Sickness Dataset for object detection and deploying fine-tuned models on mobile devices.The YOLO-NAS-s, YOLOv8n, YOLOv5nu, and SSD-MobileNetV2 models were fine-tuned using this dataset to detect diseased tea leaves, achieving state-of-the-art performance. Among them, YOLOv5nu achieved a maximum mAP@50 of 0.969 and an F1-score of 0.927, demonstrating exceptional accuracy. Its lightweight architecture ensures fast inference and low resource usage, offering a balance between performance and computational efficiency, making it well-suited for real-time deployment. After the models were evaluated, the two most lightweight models were deployed on mobile devices, demonstrating the feasibility of using high-performance and lightweight models for real-time plant disease monitoring.","url_abs":"http://dx.doi.org/10.13140/RG.2.2.10031.24483","url_pdf":"http://dx.doi.org/10.13140/RG.2.2.10031.24483","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"real-time-tea-leaf-disease-detection-using","repo_url":"https://github.com/ineffablekenobi/Real-Time-Tea-Leaf-Disease-Detection-Using-Deep-Learning-Models","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"yolov8","method_name":"YOLOv8"}],"datasets_introduced":[{"slug":"tea-sickness-object-detection","name":"Tea sickness - object detection","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}