Improved YOLO v7 for sustainable agriculture significantly improves precision rate for Chinese Cabbage (Brassica pekinensis Rupr.) Seedling Belt (CCSB) detection

Improved YOLO v7 for sustainable agriculture significantly improves precision rate for Chinese Cabbage (Brassica pekinensis Rupr.) Seedling Belt (CCSB) detection

Precise navigation in agricultural applications necessitates accurate guidance from the seedling belt, which the Global Positioning System (GPS) alone cannot provide. The overlapping leaves of Chinese cabbage (Brassica pekinensis Rupr.) present significant challenges for seedling belt fitting due to difficulties in plant identification. This study aims to address these challenges by improving the You Only Look Once (YOLO) v7 model with a novel approach that decouples its network head deriving from the Faster-Regions with Convolutional Neural Network (Faster R-CNN) architecture. Additionally, this study introduced a BiFormer attention mechanism to accurately identify the centers of overlapping Chinese cabbages. Using these identified centers and pixel distance verification, this study achieved precise fitting of the Chinese cabbage seedling belt (CCSB). Our experimental results demonstrated a significant improvement in performance metrics, with our improved model achieving a 2.5% increase in mean average precision compared to the original YOLO v7. Furthermore, our approach attained a 94.2% accuracy in CCSB fitting and a 91.3% Chinese cabbage identification rate. Compared to traditional methods such as the Hough transform and linear regression, our method showed an 18.6% increase in the CCSB identification rate and a 17.6% improvement in angle accuracy. The novelty of this study lies in the innovative combination of the YOLO v7 model with a decoupled head and the BiFormer attention mechanism, which together advance the identification and fitting of overlapping leafy vegetables. This advancement supports intelligent weeding, reduces the reliance on chemical herbicides, and promotes safer, more sustainable agricultural practices. Our research not only improves the accuracy of overlapping vegetable identification, but also provides a robust framework for enhancing precision agriculture.