AUTOMATED IDENTIFICATION OF TEA LEAF DISEASES AND PESTS USING DEEP LEARNING METHODS
Tea is a significant crop and deeply loved by individuals. In earlier times, the identification of tea leaf diseases and pests was manual and inefficient. With the increasing application of AI (artificial intelligence), deep learning and image recognition technology in the field of agriculture, thi...
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Regional Association for Security and crisis management, Belgrade, Serbia
2024-06-01
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| Series: | Operational Research in Engineering Sciences: Theory and Applications |
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| Online Access: | https://oresta.org/menu-script/index.php/oresta/article/view/771 |
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| author | Xianghong Deng Tao Chen Chonlatee Photong |
| author_facet | Xianghong Deng Tao Chen Chonlatee Photong |
| author_sort | Xianghong Deng |
| collection | DOAJ |
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Tea is a significant crop and deeply loved by individuals. In earlier times, the identification of tea leaf diseases and pests was manual and inefficient. With the increasing application of AI (artificial intelligence), deep learning and image recognition technology in the field of agriculture, this paper introduces a method with improved efficiency and precision for intelligent identification in tea leaf diseases and pests. We applied the deep learning target detection model, which is the recent version of YOLO (You Only Look Once), specifically YOLOv10s, for automated recognition of tea leaf diseases and pests. This research primarily involves three models: YOLOv8s, YOLOv9s, and YOLOv10s. After training and validation, we conducted a comprehensive performance evaluation and comparative analysis of these models. The comparison of performance metrics indicated that the model based on YOLOv10s performed the best. As shown by the test evaluation results, precision, recall, mAP50 (mean of Average Precision), F1-Score, these values are all higher than those achieved by YOLOv8s and YOLOv9s. Using the optimal YOLOv10s model, combined with the PyQt5 library, a tea leaf diseases and pests target detection recognition interface was developed. Based on this proposed model with YOLOv10s, the identification of tea leaf diseases and pests will be significantly improved for all the terms of higher efficiency, less costs, as well as enhanced quality and sustainability of tea production.
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| format | Article |
| id | doaj-art-0c4a5e1f83af49818c9a46d8434ef7d4 |
| institution | Kabale University |
| issn | 2620-1607 2620-1747 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Regional Association for Security and crisis management, Belgrade, Serbia |
| record_format | Article |
| series | Operational Research in Engineering Sciences: Theory and Applications |
| spelling | doaj-art-0c4a5e1f83af49818c9a46d8434ef7d42024-11-30T15:22:49ZengRegional Association for Security and crisis management, Belgrade, SerbiaOperational Research in Engineering Sciences: Theory and Applications2620-16072620-17472024-06-0172AUTOMATED IDENTIFICATION OF TEA LEAF DISEASES AND PESTS USING DEEP LEARNING METHODSXianghong Deng0Tao Chen1Chonlatee Photong2Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand. Hunan Mechanical & Electrical Polytechnic, Changsha 410119, China.Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand. Tea is a significant crop and deeply loved by individuals. In earlier times, the identification of tea leaf diseases and pests was manual and inefficient. With the increasing application of AI (artificial intelligence), deep learning and image recognition technology in the field of agriculture, this paper introduces a method with improved efficiency and precision for intelligent identification in tea leaf diseases and pests. We applied the deep learning target detection model, which is the recent version of YOLO (You Only Look Once), specifically YOLOv10s, for automated recognition of tea leaf diseases and pests. This research primarily involves three models: YOLOv8s, YOLOv9s, and YOLOv10s. After training and validation, we conducted a comprehensive performance evaluation and comparative analysis of these models. The comparison of performance metrics indicated that the model based on YOLOv10s performed the best. As shown by the test evaluation results, precision, recall, mAP50 (mean of Average Precision), F1-Score, these values are all higher than those achieved by YOLOv8s and YOLOv9s. Using the optimal YOLOv10s model, combined with the PyQt5 library, a tea leaf diseases and pests target detection recognition interface was developed. Based on this proposed model with YOLOv10s, the identification of tea leaf diseases and pests will be significantly improved for all the terms of higher efficiency, less costs, as well as enhanced quality and sustainability of tea production. https://oresta.org/menu-script/index.php/oresta/article/view/771Tea leaf diseases and pestsDeep learningImage recognition technologyYOLOv10sPyQt5 |
| spellingShingle | Xianghong Deng Tao Chen Chonlatee Photong AUTOMATED IDENTIFICATION OF TEA LEAF DISEASES AND PESTS USING DEEP LEARNING METHODS Operational Research in Engineering Sciences: Theory and Applications Tea leaf diseases and pests Deep learning Image recognition technology YOLOv10s PyQt5 |
| title | AUTOMATED IDENTIFICATION OF TEA LEAF DISEASES AND PESTS USING DEEP LEARNING METHODS |
| title_full | AUTOMATED IDENTIFICATION OF TEA LEAF DISEASES AND PESTS USING DEEP LEARNING METHODS |
| title_fullStr | AUTOMATED IDENTIFICATION OF TEA LEAF DISEASES AND PESTS USING DEEP LEARNING METHODS |
| title_full_unstemmed | AUTOMATED IDENTIFICATION OF TEA LEAF DISEASES AND PESTS USING DEEP LEARNING METHODS |
| title_short | AUTOMATED IDENTIFICATION OF TEA LEAF DISEASES AND PESTS USING DEEP LEARNING METHODS |
| title_sort | automated identification of tea leaf diseases and pests using deep learning methods |
| topic | Tea leaf diseases and pests Deep learning Image recognition technology YOLOv10s PyQt5 |
| url | https://oresta.org/menu-script/index.php/oresta/article/view/771 |
| work_keys_str_mv | AT xianghongdeng automatedidentificationoftealeafdiseasesandpestsusingdeeplearningmethods AT taochen automatedidentificationoftealeafdiseasesandpestsusingdeeplearningmethods AT chonlateephotong automatedidentificationoftealeafdiseasesandpestsusingdeeplearningmethods |