The invention relates to the technical field of agricultural
disease detection and
crop disease resistance screening, in particular to a rice bacterial leaf
blight disease resistance
screening method based on an unmanned aerial vehicle and
deep learning, and adopts the technical scheme that an optimized YOLOv11-OBB model is composed of a C3k2FC module, an SPPFLSKA module, a SlimNeck module and a LiteHead module, is responsible for extracting features in an image and positioning bacterial leaf
blight spots, and is used for screening the disease resistance of the rice bacterial leaf
blight; bacterial leaf blight can be efficiently and accurately detected; a lightweight
deep learning model is used, efficient operation on a resource-limited unmanned aerial vehicle platform can be realized, and the equipment and calculation cost is reduced; through the cooperation of a plurality of optimization modules, the detection precision of bacterial leaf blight spots is remarkably improved, and a stable detection effect can be kept; the operation process is simplified, and full
automation of
bacterial blight disease resistance screening is realized; therefore, low-cost, efficient, automatic and high-precision
bacterial blight disease resistance screening is realized by utilizing the lightweight and optimized YOLOv11-OBB model and combining module design with high calculation efficiency, and the method is suitable for large-scale field application.