Accurate and rapid identification of corn diseases is a prerequisite for precision spraying in the field. Yet the highly variable outdoor environment, together with the computational cost of deep models, still constrains real-world deployment. To address these challenges, we propose Dycorn-YOLO11, a deployable corn disease detector built on a dual-dynamic feature capture architecture design. First, the online convolutional reparameterization block (OREPA) is embedded in the backbone to reduce training and inference complexity. Second, a dual-dynamic feature capture architecture is constructed, where v3-Dyhead and Dysample work in concert to achieve deformable alignment and content-adaptive sampling, strengthening the representation of small, low-contrast lesions. In addition, a “channel pruning + knowledge distillation” lightweight pipeline compresses the model substantially while preserving the backbone’s feature expressiveness. Experiments show that the improved model attains a precision (P) of 88.2%, recall (R) of 73.9%, and mAP of 81.5%, representing gains of 0.8, 2.1, and 3.4 percentage points over YOLO11n. The parameter count and model size are reduced by 41.5% and 40.0%, respectively, and the inference speed increases to 113.2 f/s, achieving a balanced trade-off among accuracy, compactness, and efficiency. The model also maintains high stability on an external, complex dataset, and delivers strong real-time performance in both static and moving field experiments. Overall, Dycorn-YOLO11 demonstrates robust, deployable performance and provides a feasible technical foundation for precision spraying in corn fields.
Li et al. (Sun,) studied this question.