Plants Disease Image Classification Based on Lightweight Convolution Neural Networks

Abstract

Plants diseases is a major threat to agricultural production. Reduced yield due to plant diseases can lead to immeasurable economic losses. Therefore, the detection and classification of plant diseases are of great significance. Most of the existing plant disease detection methods focus on improving the identification accuracy. However, besides accuracy, real-time performance cannot be ignored. In this paper, a new module named 2-way residual dense layer is presented to effectively decrease the number of parameters in our network. In this module, depth separable convolution is introduced, which reduces the amount of parameter calculation and achieves a performance of over 98%. Our network is verified by an open dataset which includes 4503 images from four classes, including Mango, Arjun, Alstonia Scholaris, Guava, Bael, Jamun, Jatropha, Pongamia Pinnata, Basil, Pomegranate, Lemon, and Chinar. The leaf images of these plants have healthy and diseased condition. The experimental results showed that this method can be practically applied to the identification of plant leaf diseases and provide a basis for the identification of other leaf diseases.

Plant phenotyping relevance

植物葉画像から病害状態を分類する軽量CNN手法の開発・検証が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。

Code and data availability

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