首页|MS-DNet: A mobile neural network for plant disease identification
MS-DNet: A mobile neural network for plant disease identification
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NSTL
Elsevier
? 2022 Elsevier B.V.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 recognizing diverse plant diseases, they have certain drawbacks such as their requirement for a large number of parameters, 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.com/xtu502/Automatic-crop-disease-identification-under-field-conditions.
NSTL
Elsevier
