Diagnosis of Multiple Cucumber Infections with Convolutional Neural Networks

Abstract

Recent machine learning approaches have shown promising results in the field of automated plant diagnosis. However, all of the systems were designed to diagnose single infections, thus they do not assume multiple infections. In this paper, we created our original on-site cucumber leaf dataset including multiple infections to build a practical plant diagnosis system. Our dataset has a total of 48,311 cucumber leaf images (38,821 leaves infected with any of 11 kinds of diseases, 1,814 leaves infected with multiple diseases, and 7,676 healthy leaves). We developed a convolutional neural networks (CNN) classifier having the sigmoid function with a tunable threshold on each node in the last output layer. Our model attained on average a 95.5% classification accuracy on the entire dataset. On only multiple infected cases, the result was 85.9% and it accurately identified at least one disease in 1,808 out of the total of 1,814 (99.7%).