Remote diagnosis system of banana diseases based on deep learning

Objective  To realize remote diagnosis of banana diseases.

Method  Deep learning method was used to diagnose seven common diseases of banana plant. A total of 5 944 images of diseased and healthy banana plants were collected and divided into training set, validation set and testing set according to the ratio of 7∶1∶2. Transfer learning was used to train GoogLeNet which is a deep convolutional neural network for obtaining the diagnosis model. A software system including a mobile application (APP) and a remote server was further developed.

Result  By comparing different iteration times and optimizers, the model of MomentumOptimizer with 10000 iteration times was finally selected, and the average test accuracy was 98%. The designed mobile APP could acquire banana images in situ, and communicate with the remote server which was integrated with a diagnosis model via the network to obtain diagnosis results in real time.

Conclusion  The disease diagnosis model can identify the main diseases with high accuracy. The online diagnosis system is simple and easy to operate, it can diagnose common banana diseases online quickly and effectively, and therefore it has a wide application prospect.