MS-DNet: A mobile neural network for plant disease identification

Plant disease identification has recently attracted immense attention from the perspective of food security. Owing to the complexity and diversity of plant diseases, plant disease recognition using image processing techniques is a challenging task. Although the widely applied deep neural networks are promising for recog-nizing diverse plant diseases, they have certain drawbacks such as their requirement for a large number of pa-rameters, which necessitates a large amount of annotation data for training models. To overcome this challenge, this study proposes a novel lightweight network architecture called MS-DNet for the recognition of crop diseases; the network has a small model size and high computation speed. The proposed method has attained a satisfactory performance in comparative experiments, with the highest average accuracy of 98.32% in recognizing different crop disease types. The experimental results further show that the proposed method outperforms other state-of-the-art methods and also demonstrate its efficiency and extensibility. Our code is available at https://github.co m/xtu502/Automatic-crop-disease-identification-under-field-conditions.

Automated summary

Plant disease identification is crucial for food security, and recognition of plant diseases using image processing techniques is challenging. This study proposes a lightweight network architecture called MS-DNet, which achieves high accuracy and efficiency in recognizing crop diseases, as demonstrated in comparative experiments.