MnasNet-SimAM: An Improved Deep Learning Model for the Identification of Common Wheat Diseases in Complex Real-Field Environments.

Abstract

Deep learning approaches have been widely applied for agricultural disease detection. However, considerable challenges still exist, such as low recognition accuracy in complex backgrounds and high misjudgment rates for similar diseases. This study aimed to address these challenges through the detection of six prevalent wheat diseases and healthy wheat in images captured in a complex natural context, evaluating the recognition performance of five lightweight convolutional networks. A novel model, named MnasNet-SimAM, was developed by combining transfer learning and an attention mechanism. The results reveal that the five lightweight convolutional neural networks can recognize the six different wheat diseases with an accuracy of more than 90%. The MnasNet-SimAM model attained an accuracy of 95.14%, which is 1.7% better than that of the original model, while only increasing the model's parameter size by 0.01 MB. Additionally, the MnasNet-SimAM model reached an accuracy of 91.20% on the public Wheat Fungi Diseases data set, proving its excellent generalization capacity. These findings reveal that the proposed model can satisfy the requirements for rapid and accurate wheat disease detection.

Plant phenotyping relevance

小麦画像から病害状態を推定する深層学習モデルを開発・評価しており、植物病害表現型の取得・推定が研究の中心である。

Code and data availability

The paper's wheat disease image dataset (field photos, web-crawled, and aistudio-sourced images) is not deposited anywhere; the Data Availability Statement says 'Data are contained within the article.' No author code, trained models, or checkpoints are mentioned. The aistudio.baidu.com URL is a generic public platform,

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