Improved Multi-Plant Disease Recognition Method Using Deep Convolutional Neural Networks in Six Diseases of Apples and Pears

Abstract

Plant diseases are a major concern in the agricultural sector; accordingly, it is very important to identify them automatically. In this study, we propose an improved deep learning-based multiplant disease recognition method that combines deep features extracted by deep convolutional neural networks and k-nearest neighbors to output similar disease images via query image. Powerful, deep features were leveraged by applying fine-tuning, an existing method. We used 14,304 in-field images with six diseases occurring in apples and pears. As a result of the experiment, the proposed method had a 14.98% higher average similarity accuracy than the baseline method. Furthermore, the deep feature dimensions were reduced, and the image processing time was shorter (0.071–0.077 s) using the proposed 128-sized deep feature-based model, which processes images faster, even for large-scale datasets. These results confirm that the proposed deep learning-based multi-plant disease recognition method improves both the accuracy and speed when compared to the baseline method.