## 3 Results
### Model Result Analysis  
The loss function is mainly used to quantify the error between the predicted results and the real annotation of the model in the disease detection task, and guide the continuous updating and optimization of the parameters during the model training process to achieve error minimization, detection performance improvement, and generalization ability enhancement. As shown in Figure 7, the results show that the loss function of the improved YOLOv10 network decreases significantly faster than that of the original YOLOv10. After 500 epochs of training, the improved YOLOv10 achieved Box Loss, Cls Loss, and DFL Loss values of 1.16, 0.66, and 1.24, respectively, under the One-to-Many Head, which represents reductions of 15.94%, 13.16%, and 8.82% compared to the original YOLOv10 network's values of 1.38, 0.76, and 1.36. Under the One-to-One Head, the Box Loss, Cls Loss, and DFL Loss were 1.23, 0.65, and 1.23, respectively, which are 14.58%, 17.72%, and 8.89% lower than those of the original YOLOv10 network (1.44, 0.79, and 1.35). The lower Box Loss value indicates that the improved YOLOv10 exhibits smaller errors in disease region localization and can more accurately capture target boundaries. The reduction in Cls Loss demonstrates that the model possesses higher discriminative accuracy in distinguishing disease categories. Meanwhile, the decrease in DFL Loss indicates that the model is more sensitive in capturing the distribution of fine-grained features, which helps to enhance the overall detection performance.  
As shown in Figure 8, the different colors of the thin lines represent different categories, while the dark blue thick line represents all categories. The precision, recall, and F1 of the improved YOLOv10 network are 85.38%, 88.08%, and 86.71%, respectively, which are 3.4%, 10.05%, and 6.75% higher than the original YOLOv10. The results show that the improved model can more accurately identify the target area when detecting diseases, and reduce the missed detection and false detection.  
Figure 7: Loss function change curve.  
In the confusion matrix, the horizontal axis represents the prediction result of the model, and the vertical axis represents the real labeled category [25]. The larger the value of each element in the matrix, the higher the proportion of the corresponding predicted category for that true category. The more the diagonal region approaches 1, the higher the accuracy of correct predictions for that disease, while the values of the off-diagonal elements reflect the degree of confusion among different categories. As shown in Figure 9, the values of the elements in the matrix are influenced both by the type of disease and by the results of model optimization and improvement. Compared with the original YOLOv10 network, the improved network shows a significant performance improvement in disease detection, with detection accuracy improvements of 6% for anthracose, 20% for bacterial coil disease, and 2% for leaf blight. The research results indicate that the improved network has a higher discriminative ability in extracting and differentiating various disease features, which enables it to more accurately classify the true disease samples, thereby reducing misclassification rates and enhancing the overall detection accuracy.  
Figure 8: Comparison of precision, recall, and F1 before and after the YOLOv10 network improvement.  
Figure 9: Confusion matrix.