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convolutional neural networks for mobile capture device-based crop disease classification in the wild","volume":"161","author":"Picon","year":"2019","journal-title":"Comput Electr Agricult"},{"key":"ref9","first-page":"205","article-title":"Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network","volume":"13","author":"Dong","year":"2020","journal-title":"Int J AgricultBiolog Eng"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"215232","DOI":"10.3389\/fpls.2016.01419","article-title":"Using deep learning for image-based plant disease detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front Plant Sci"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3390\/sym10010011","article-title":"Identification of apple leaf diseases based on deep convolutional neural 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annotation","volume":"77","author":"Russell","year":"2008","journal-title":"Int J Comput Vis"},{"key":"ref29","first-page":"1137","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref30","series-title":"Computer Vision-ECCV 2016: 14th European Conference","first-page":"21","article-title":"SSD: single shot multibox detector","author":"Liu","year":"2016"},{"key":"ref31","unstructured":"Yaseen M. What is yolov9: an in-depth exploration of the internal features of the next-generation object detector. arXiv:2409.07813. 2024."},{"key":"ref32","series-title":"2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)","first-page":"1","article-title":"Yolov8: a novel object detection algorithm with enhanced performance and robustness","author":"Varghese","year":"2024 Apr"},{"key":"ref33","series-title":"2023 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","first-page":"1","article-title":"Exploring the frontier of object detection: a deep dive into yolov8 and the coco dataset","author":"Kumar","year":"2023 Dec 10"},{"key":"ref34","series-title":"2024 11th International Conference on Signal Processing and Integrated Networks (SPIN)","first-page":"72","article-title":"Comparative study of YOLOv8 and YOLO-NAS for agriculture application","author":"Kumar","year":"2024 Mar 21"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"1327507","DOI":"10.3389\/fpls.2024.1327507","article-title":"High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits","volume":"15","author":"Chen","year":"2024","journal-title":"Front Plant Sci"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"Li Z, Peng C, Yu G, Zhang X, Deng Y, Sun J. DetNet: a backbone network for object detection. arXiv:1804.06215. 2018.","DOI":"10.1007\/978-3-030-01240-3_21"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"28039","DOI":"10.1007\/s11042-023-16570-9","article-title":"Fruit ripeness identification using YOLOv8 model","volume":"83","author":"Xiao","year":"2024","journal-title":"Multim Tools Applicat"},{"key":"ref38","series-title":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","first-page":"155","article-title":"Plant disease detection and segmentation using end-to-end yolov8: a comprehensive approach","author":"Qadri","year":"2023 Aug 25"},{"key":"ref39","unstructured":"Thuan D. Evolution of Yolo algorithm and Yolov5: the State-of-the-Art object detention algorithm; 2021 [cited 2025 Jan 20]. Available from: https:\/\/www.theseus.fi\/handle\/10024\/452552."},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Sapkota R, Qureshi R, Flores-Calero M, Badgujar C, Nepal U, Poulose A, et al. Yolov10 to its genesis: a decadal and comprehensive review of the you only look once series. 2024 Jun 12 [cited 2025 Jan 20]. Available from: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4874098.","DOI":"10.2139\/ssrn.4874098"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"20939","DOI":"10.1007\/s00521-023-08809-1","article-title":"An improved fire detection approach based on YOLO-v8 for smart cities","volume":"35","author":"Talaat","year":"2023","journal-title":"Neural Comput Applicat"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.3390\/agriculture13122253","article-title":"Research on polygon pest-infected leaf region detection based on YOLOv8","volume":"13","author":"Zhu","year":"2023","journal-title":"Agriculture"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"240","DOI":"10.3390\/agriculture13020240","article-title":"A high-precision detection method of apple leaf diseases using improved faster R-CNN","volume":"13","author":"Gong","year":"2023","journal-title":"Agriculture"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-84-2\/TSP_CMC_60185\/TSP_CMC_60185.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:46:53Z","timestamp":1763344013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v84n2\/62858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":43,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.060185","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}