Recognition and application of apple defoliation disease based on transfer learning

Convolutional Neural Network (CNN) can be applied to recognize the leaf disease of apples in agricultural production, due to the reduced size of the model and the high generalization. In this study, the recognition model was proposed for the apple defoliation disease using an improved MobileNetV3 (ET3-MobileNetV3) network structure. According to the disease features, the images were divided into five types: altermaria boltch, rust, grey spot, brown spot and health. The images of apple defoliation disease were collected from the standard dataset produced by the Luochuan Apple Experimental Station of Northwest A&F University. The orchard dataset was collected from Huicheng Apple Farm in Yangling City. The final datasets of apple defoliation diseases were a total of 21 950 images, including 19 819 in the training set and 2 131 in the test set. The recognition model of defoliation disease was constructed to improve the attention module. Efficient Channel Attention (ECA) was replaced by Squeeze and Excite (SE), while the Tanh function was used to replace the Sigmoid function. Then, the full connection layer was improved to replace the original Hard-Swish (HS) activation function with the ReLU6 activation function. At the same time, the Dropout layer and the improved Bottleneck operator were introduced to enhance the calculation speed. Finally, transfer learning was utilized to transfer the pre-trained weights into the recognition task in the training. Among them, the pre-trained weights were obtained to train the model on the ImageNet dataset. The generalization was improved on the small sample learning, whereas, there was a decrease in the data demand of the target task, indicating the better performance of the model. The performance of recognition was verified by training the model on datasets before and after expansion, transfer learning and new learning, different learning rates and attention mechanisms. The learning rates included 0.01, 0.001, and 0.000 1. Attention modules included SE, ECA and Efficient Channel Attention-Tanh (ET). The experimental results showed that the transfer learning training obtained a superior performance model in a shorter time, compared with the fully new learning. The model curve converged faster than before, and the more stable curve was achieved after convergence. The generalization was stronger, and the recognition accuracy was higher than before. When the learning rate of transfer learning was 0.000 1, the model curve tended to converge after 50 rounds of training. There was a lower standard deviation in the accuracy and Loss curves, where the accuracy increased by 6.74 to 10.79 percentage points. Secondly, the ET module was introduced using the fully connected layer. The convergence of the loss curve was accelerated to reduce the number of parameters. The generalization was improved to avoid the overfitting of the model. The final model volume was 6.29M, which was reduced by 48%. The standard deviation of the Loss curve was 0.006, indicating a smoother Loss curve. Thirdly, the diversity and quantity of data increased to expand the datasets, thereby reducing the overfitting of the model. At the same time, there was an increase in the diversity of data exposure to model training. The better generalization was suitable for the complex data. The prediction accuracy was also improved for the robustness and stability of the model. The average accuracy and F1-score reached 95.62% and 94.62%, respectively, under transfer learning combined with data expansion. Finally, the ET3-MobileNetV3 model was deployed to the spraying equipment. The apple defoliation diseases were effectively identified with the variable spraying. The high accuracy, strong generalization, and small parameter volume fully met the requirements of disease recognition during spraying in the orchard. The optimal model was deployed in the orchard spraying device. The pesticide spraying was also realized to implement the guidance variable spraying device. The finding can provide a strong reference to detect apple defoliation diseases in modern orchards.