MixNet-CA: A Novel Disease Identification Method for Chinese Roses Based on MixNet-s

Chinese roses have both ornamental and medicinal value. However, Chinese roses are susceptible to various diseases during their growing process. It is a challenge for growers to promptly detect and control diseases, especially in extensive planting scenarios. A novel disease identification approach for Chinese roses is proposed to help growers accurately determine the severity of Chinese rose diseases so that they can take control of the diseases at the appropriate time. In the proposed approach, which is called MixNet-CA for short, coordinate attention and residual connections are introduced. First, the squeeze-and-excitation attention in MixNet-s is replaced by coordinate attention to enhance the information representation capability of the network and more precisely obtain disease features. Second, residual connections are constructed based on the characteristics of the MixNet-s model to enhance the network representation and prevent overfitting. Finally, reducing the network depth ensures that all network layers are fully trained. The experimental results show that the Chinese rose disease identification accuracy of MixNet-CA is 98.82%, which is 3.4% higher than that of the original MixNet-s model. The MixNet-CA model yields classification precision, recall, and F1 score metrics exceeding 97% for each category. It is also evident from the results that MixNet-CA demonstrates excellent performance compared to other networks, achieving a balance in terms of accuracy, size, the number of parameters, and the number of FLOPs. MixNet-CA also demonstrates good generalization ability on different datasets.