Identification of Leaf Disease Based on Memristor Convolutional Neural Networks

Deep learning methods based on convolutional neural networks can identify subtle disease features in plant leaves, thereby improving the accuracy and efficiency of plant leaf disease detection. Traditional convolutional neural network models have more parameters, lower training efficiency, and require a large amount of computing resources. A TTN-MobileNetV2 neural network model based on memristors for plant leaf disease detection is proposed in this paper. Firstly, integrate the Triplet attention module into the backbone structure of the network to capture local features, and utilize Cross-Norm and Self-Norm(CNSN) normalization techniques to enhance the generalization robustness under distribution changes. In addition, a Mish activation function with enhanced nonlinear characteristics was introduced to improve the accuracy of neural network detection. Experimental results on the Plant Village and Rice leaf disease datasets showed identification accuracies of 99.03% and 99.16%, respectively. On this basis, using the MemTorch simulation environment, the weights of all convolutional layers and fully connected layers in the convolutional neural network are mapped to the conductance values of the memristors in the cross array of memristors, completing the implementation of the memristor TTN-MobileNetV2 network. The performance of the memristor network was tested using two types of memristor models: linear ion drift model and data-driven Verilog-A RRAM. The recognition accuracy losses of the TTN-MobileNetV2 memristor network corresponding to the two memristor models were 0.32, 0.34, and 0.52, 0.61, respectively. So the memristor convolutional neural network can meet the performance requirements of plant leaf disease recognition and has inherent advantages of high speed and low power consumption.