Apple Disease Recognition Based on Comprehensive Application of Residual Neural Network

Abstract

China is the world’s largest apple growing country; Apples are affected by various diseases during the planting process, and timely detection of fruit tree diseases is of great significance for improving apple yield. With the development of artificial intelligence technology, The use of deep learning to detect the condition of fruit trees has become a research hotspot in forest science planting. ResNet50 is used to solve the bottleneck problem of recognition accuracy of traditional neural networks, and data enhancement is used to improve the quality of data samples. transfer learning is used to solve the application of small-scale data samples, and loss function is used to balance sample errors. Improve the ability to obtain important feature information by using a dual channel mixed attention mechanism. Finally, a comprehensive network model combining residual neural network, dual channel mixed attention mechanism, transfer learning and loss function is designed. The experimental results show that the effect of this comprehensive ResNet50 model is better than that of the original ResNet50 model and VGG19 model, and its classification accuracy rate can reach 96.87%. This method improves the performance in fruit disease detection, and has wider applicability in practical applications.