A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network

Manual approaches to recognize cucumber diseases are often time-consuming, laborious and subjective. A deep convolutional neural network (DCNN) was proposed to conduct symptom-wise recognition of four cucumber diseases, i.e., anthracnose, downy mildew, powdery mildew, and target leaf spots. The symptom images were segmented from cucumber leaf images captured under field conditions. In order to decrease the chance of overfitting, data augmentation methods were utilized to enlarge the datasets formed by the segmented symptom images. With the augmented datasets containing 14,208 symptom images, the DCNN achieved good recognition results, with an accuracy of 93.4%. In order to compare the results of the DCNN, comparative experiments were conducted using conventional classifiers (Random Forest and Support Vector Machines), as well as AlexNet. Results showed that the DCNN was a robust tool for recognizing the cucumber diseases in field conditions.