Wheat Powdery Mildew Severity Classification Based on an Improved ResNet34 Model

Meilin Li, Yufeng Guo, Wei Guo, Hongbo Qiao, Lei Shi, Yang Liu, Guang Zheng, Hui Zhang and Qiang Wang ()
Additional contact information
Meilin Li: College of Information and Management Sciences, Henan Agricultural University, Zhengzhou 450046, China
Yufeng Guo: College of Information and Management Sciences, Henan Agricultural University, Zhengzhou 450046, China
Wei Guo: College of Information and Management Sciences, Henan Agricultural University, Zhengzhou 450046, China
Hongbo Qiao: College of Information and Management Sciences, Henan Agricultural University, Zhengzhou 450046, China
Lei Shi: College of Information and Management Sciences, Henan Agricultural University, Zhengzhou 450046, China
Yang Liu: Key Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, China
Guang Zheng: College of Information and Management Sciences, Henan Agricultural University, Zhengzhou 450046, China
Hui Zhang: College of Information and Management Sciences, Henan Agricultural University, Zhengzhou 450046, China
Qiang Wang: College of Information and Management Sciences, Henan Agricultural University, Zhengzhou 450046, China

Agriculture, 2025, vol. 15, issue 15, 1-25

Abstract: Crop disease identification is a pivotal research area in smart agriculture, forming the foundation for disease mapping and targeted prevention strategies. Among the most prevalent global wheat diseases, powdery mildew—caused by fungal infection—poses a significant threat to crop yield and quality, making early and accurate detection crucial for effective management. In this study, we present QY-SE-MResNet34, a deep learning-based classification model that builds upon ResNet34 to perform multi-class classification of wheat leaf images and assess powdery mildew severity at the single-leaf level. The proposed methodology begins with dataset construction following the GBT 17980.22-2000 national standard for powdery mildew severity grading, resulting in a curated collection of 4248 wheat leaf images at the grain-filling stage across six severity levels. To enhance model performance, we integrated transfer learning with ResNet34, leveraging pretrained weights to improve feature extraction and accelerate convergence. Further refinements included embedding a Squeeze-and-Excitation (SE) block to strengthen feature representation while maintaining computational efficiency. The model architecture was also optimized by modifying the first convolutional layer (conv1)—replacing the original 7 × 7 kernel with a 3 × 3 kernel, adjusting the stride to 1, and setting padding to 1—to better capture fine-grained leaf textures and edge features. Subsequently, the optimal training strategy was determined through hyperparameter tuning experiments, and GrabCut-based background processing along with data augmentation were introduced to enhance model robustness. In addition, interpretability techniques such as channel masking and Grad-CAM were employed to visualize the model’s decision-making process. Experimental validation demonstrated that QY-SE-MResNet34 achieved an 89% classification accuracy, outperforming established models such as ResNet50, VGG16, and MobileNetV2 and surpassing the original ResNet34 by 11%. This study delivers a high-performance solution for single-leaf wheat powdery mildew severity assessment, offering practical value for intelligent disease monitoring and early warning systems in precision agriculture.

Keywords: wheat powdery mildew; deep learning; disease severity; image recognition; QY-SE-MResNet34 model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/15/1580/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/15/1580/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:15:p:1580-:d:1707963

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().