Hierarchical Deep Learning Approach for Plant Disease Detection

Abstract

In this paper we propose a hierarchical deep learning approach for plant disease detection. The detection of diseases in plants using deep image approaches is attracting researchers as a way of taking advantage of cutting-edge learning techniques in scenarios where major benefits can be achieved for mankind. In this work, we focus on diseases of three major different agricultural crops: apple, peach and tomato. Using a real-world dataset composed of nearly 24,000 images, including healthy examples, we propose a hierarchical deep learning approach for plant disease detection and compare it with the standard deep learning approaches. Results permit the conclusion that hierarchical approaches can overcome standard approaches in terms of detection performance.