EM-ERNet for image-based banana disease recognition

It is observed that banana production is affected by numerous diseases and causes a large loss to the poor farmers. By using modern image processing technology and deep learning methods, these diseases can be found at an earlier stage and appropriate precautions can be taken at time to avoid more injury and increase banana production. In this paper, a novel banana disease recognition method is proposed. First, a banana leaf feature enhancement framework is designed to enhance the banana features under the complex environment. Then, a novel neural network, EM-ERNet, is designed based on the Resnet backbone architecture. In the EM-ERNet, dilated convolution and multi-scale convolution are combined to improve the ability of feature extraction. Batch normalization is used to prevent the network over-fitting and enhance the model robustness. The ELM algorithm optimized by particle swarm (PELM) is used to speed up the fusion network in the final fully connected layer. The experiment was conducted on the public banana disease detection database. The highest detection accuracy for crown rot, venturia, scorch rot, anthrax and normal bananas could reach 94.27, 93.36, 89.39, 88.87 and 96.39% respectively, which was better than the existing related competitors. In addition, ResNet50 takes 80 min to identify, while EM-ERNet only takes 60 min. We can see that our method improves the parameters by a quarter.