Lightweight dense-scale network (LDSNet) for corn leaf disease identification

Abstract

The identification of corn leaf diseases in real scenarios faces important challenges, such as complex background interference, intra- and inter-class scale changes, and lightweight model deployment. To overcome these challenges, we propose a lightweight dense-scale network (LDSNet) for real-world corn leaf disease image identification. The main component of LDSNet is the improved dense dilated convolution (IDDC) block that has two key improvements relative to existing essential blocks. The first one is that it improves the adaptability to the scale change of corn leaf diseases through the dense connection of different dilation rate convolutions. The second improvement is that it replaces the concatenation connection with a new fusion method, namely, coordinated attention scale fusion, for enhanced extraction of corn leaf features in a complex background. In addition, we propose a new loss function to optimize the LDSNet network model. Experimental results show that the accuracy of the optimized model on the test data set reaches 95.4%, which is better than the accuracy of existing heavyweight networks, such as AlexNet, VGG16, VGG19, and ResNet50, and lightweight networks, such as DenseNet121, GoogleNet, MobileNet (V1, V2, and V3-large), ShuffleNetV2, and GhostNet. The number of parameters accounts for only 45.4% of the minimum number of parameters (ShuffleNetV2, 1.3M) in the compared model. To verify the practical application performance of the proposed network model, we apply the trained model to a mobile phone. A real-world test proves that our model has strong compatibility and high recognition performance.