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

文献类型： 外文期刊

作者： Wen, Xiaojie 1 ; Maimaiti, Muzaipaer 1 ; Liu, Qi 1 ; Yu, Fusheng 1 ; Gao, Haifeng 3 ; Li, Guangkuo 3 ; Chen, Jing 1 ;

作者机构： 1.Xinjiang Agr Univ, Coll Agron, Key Lab Pest Monitoring & Safety Control Crops & F, Urumqi 830052, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Prevent & Control Invas Alien Species Agr, Urumqi 830052, Peoples R China

3.Xinjiang Acad Agr Sci, Inst Plant Protect, Urumqi 830091, Peoples R China

4.Minist Agr & Rural Affairs, Key Lab Integrated Pest Management Crop Northweste, Urumqi 830091, Peoples R China

关键词： deep learning; remote sensing; wheat disease; MnasNet; attention mechanism

期刊名称：PLANTS-BASEL （ 影响因子：4.1； 五年影响因子：4.5 ）

ISSN： 2223-7747

年卷期： 2024 年 13 卷 16 期

页码：

收录情况： SCI

摘要： 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.

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