A lightweight detection model for gummosis on tree branches based on an improved YOLO algorithm.

Gummosis, a common disease among stone fruits such as peaches, plums, and apricot trees, primarily affects the trunks and major branches. Peach trees are among the most frequently affected species of this disease. This study proposes a lightweight detection model, YOLO-Gum, to address the inability to observe high branch and trunk lesions directly, along with their complex morphological features and low differentiation. This model aimed to provide an accurate basis for the prevention and scientific management of peach gummosis. First, the SENetV2 module was integrated into the original YOLOv8 backbone network, replacing some of the original convolutional layers to enhance the representative capability of the model. Second, the cross-scale convolutional feature fusion module (CCFM) structure was introduced into the neck structure to integrate detailed features and contextual information, thereby reducing the number of parameters and improving the computational efficiency. The fusion of the CCFM and SENetV2 structures maintains the lightweight nature of the model while optimizing feature extraction to enhance detection accuracy. The experimental results demonstrated that the improved YOLOv8n model attained a precision of 92.5% and an F1 score of 74.3%. Compared with the original YOLOv8n model, improvements of 5.3% and 6.2%, respectively, were achieved. Furthermore, the parameters of the improved model are 2.79 M, model size is 5.57 MB, and floating-point operations are 7.6 G, which are reduced by 12.54%, 35.4%, and 12.64% compared with those of the original YOLOv8n model. This lightweight, precise, and robust model offers technical support for peach tree growth management and robotic vision systems for disease detection.