MnasNet-SimAM: An Improved Deep Learning Model for the Identification of Common Wheat Diseases in Complex Real-Field Environments
Xiaojie Wen,
Muzaipaer Maimaiti,
Qi Liu,
Fusheng Yu,
Haifeng Gao,
Guangkuo Li,
Jing Chen
Affiliations
Xiaojie Wen
Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China
Muzaipaer Maimaiti
Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China
Qi Liu
Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China
Fusheng Yu
Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China
Haifeng Gao
Institute of Plant Protection, Xinjiang Academy of Agricultural Science, Urumqi 830091, China
Guangkuo Li
Institute of Plant Protection, Xinjiang Academy of Agricultural Science, Urumqi 830091, China
Jing Chen
Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China
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.
