Ji-Yuan Ding1, Wang-Su Jeon2, Sang-Yong Rhee2,*, Chang-Man Zou1,3
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3989-4014, 2024, DOI:10.32604/cmc.2024.057473
- 19 December 2024
Abstract In complex agricultural environments, cucumber disease identification is confronted with challenges like symptom diversity, environmental interference, and poor detection accuracy. This paper presents the DM-YOLO model, which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases. Traditional detection models have a tough time identifying small-scale and overlapping symptoms, especially when critical features are obscured by lighting variations, occlusion, and background noise. The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way. First, the MultiCat module employs a multi-scale feature processing strategy with… More >
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