Effective methods for mitigate the impact of light occlusion on the accuracy of online cabbage recognition in open fields

To address the low recognition accuracy of open-field vegetables under light occlusion, this study focused on cabbage and developed an online target recognition model based on deep learning. Using Yolov8n as the base network, a method was proposed to mitigate the impact of light occlusion on the accuracy of online cabbage recognition. A combination of cabbage image filters was designed to eliminate the effects of light occlusion. A filter parameter adaptive learning module for cabbage image filter parameters was constructed. The image filter combination and adaptive learning module were embedded into the Yolov8n object detection network. This integration enabled precise real-time recognition of cabbage under light occlusion conditions. Experimental results showed recognition accuracies of 97.5 % on the normal lighting dataset, 93.1 % on the light occlusion dataset, and 95.0 % on the mixed dataset. For images with a light occlusion degree greater than 0.4, the recognition accuracy improved by 9.9 % and 13.7 % compared to Yolov5n and Yolov8n models. The model achieved recognition accuracies of 99.3 % on the Chinese cabbage dataset and 98.3 % on the broccoli dataset. The model was deployed on an Nvidia Jetson Orin NX edge computing device, achieving an image processing speed of 26.32 frames per second. Field trials showed recognition accuracies of 96.0 % under normal lighting conditions and 91.2 % under light occlusion. The proposed online cabbage recognition model enables real-time recognition and localization of cabbage in complex open-field environments, offering technical support for target-oriented spraying.