LightMixer: A novel lightweight convolutional neural network for tomato disease detection.

Abstract

Tomatoes are among the very important crops grown worldwide. However, tomato diseases can harm the health of tomato plants during growth and reduce tomato yields over large areas. The development of computer vision technology offers the prospect of solving this problem. However, traditional deep learning algorithms require a high computational cost and several parameters. Therefore, a lightweight tomato leaf disease identification model called LightMixer was designed in this study. The LightMixer model comprises a depth convolution with a Phish module and a light residual module. Depth convolution with the Phish module represents a lightweight convolution module designed to splice nonlinear activation functions with depth convolution as the backbone; it also focuses on lightweight convolutional feature extraction to facilitate deep feature fusion. The light residual module was built based on lightweight residual blocks to accelerate the computational efficiency of the entire network architecture and reduce the information loss of disease features. Experimental results show that the proposed LightMixer model achieved 99.3% accuracy on public datasets while requiring only 1.5 M parameters, an improvement over other classical convolutional neural network and lightweight models, and can be used for automatic tomato leaf disease identification on mobile devices.

Plant phenotyping relevance

トマト葉の病徴を画像から識別する軽量CNNを開発し、精度・計算量を比較評価しており、植物病害状態の取得・推定手法が中心である。

Code and data availability

The paper's phenotyping inputs are public tomato leaf disease image datasets: the PlantVillage dataset (18,835 tomato leaf images, 10 classes) used for all LightMixer training/evaluation, obtained via Kaggle and the original Mendeley Data deposit (Arun Pandian and Gopal, 2019). No author analysis code, trained model,或