DCTnet: a double-channel transformer network for peach disease detection using UAVs - Jie Zhang; Dailin Li; Xiaoping Shi; Fengxian Wang; Linwei Li; Yibin Chen (Complex & Intelligent Systems)
Abstract The use of unmanned aerial vehicle (UAV) technology to inspect extensive peach orchards to improve fruit yield and quality is currently a major area of research. The challenge is to accurately detect peach diseases in real time, which is critical to improving peach production. The dense arrangement of peaches and the uneven lighting conditions significantly hamper the accuracy of disease detection. To overcome this, this paper presents a dual-channel transformer network (DCTNet) for peach disease detection. First, an Adaptive Dual-Channel Affine Transformer (ADCT) is developed to efficiently capture key information in images of diseased peaches by integrating features across spatial and channel dimensions within blocks. Next, a Robust Gated Feed Forward Network (RGFN) is constructed to extend the receptive field of the model by improving its context aggregation capabilities. Finally, a Local–Global Network is proposed to fully capture the multi-scale features of peach disease images through a collaborative training approach with input images. Furthermore, a peach disease dataset including different growth stages of peaches is constructed to evaluate the detection performance of the proposed method. Extensive experimental results show that our model outperforms other sophisticated models, achieving an $${AP}_{50}$$ AP 50 of 95.57% and an F1 score of 0.91. The integration of this method into UAV systems for surveying large peach orchards ensures accurate disease detection, thereby safeguarding peach production.