A Deep Learning Model for Accurate Maize Disease Detection Based on State-Space Attention and Feature Fusion

作者：Tong Zhu, Fengxia Yan, Xinyang Lv, Hanyi Zhao, Zihang Wang, Keqin Dong, Zhitao Fu, Ruihao Jia, Chunli Lv · 发表于：Plants · 年份：2024 · DOI：10.3390/plants13223151 · 被引用次数：18 · 研究领域：Smart Agriculture and AI、Spectroscopy and Chemometric Analyses、Advanced Chemical Sensor Technologies

In improving agricultural yields and ensuring food security, precise detection of maize leaf diseases is of great importance. Traditional disease detection methods show limited performance in complex environments, making it challenging to meet the demands for precise detection in modern agriculture. This paper proposes a maize leaf disease detection model based on a state-space attention mechanism, aiming to effectively utilize the spatiotemporal characteristics of maize leaf diseases to achieve efficient and accurate detection. The model introduces a state-space attention mechanism combined with a multi-scale feature fusion module to capture the spatial distribution and dynamic development of maize diseases. In experimental comparisons, the proposed model demonstrates superior performance in the task of maize disease detection, achieving a precision, recall, accuracy, and F1 score of 0.94. Compared with baseline models such as AlexNet, GoogLeNet, ResNet, EfficientNet, and ViT, the proposed method achieves a precision of 0.95, with the other metrics also reaching 0.94, showing significant improvement. Additionally, ablation experiments verify the impact of different attention mechanisms and loss functions on model performance. The standard self-attention model achieved a precision, recall, accuracy, and F1 score of 0.74, 0.70, 0.72, and 0.72, respectively. The Convolutional Block Attention Module (CBAM) showed a precision of 0.87, recall of 0.83, accuracy of 0.85, and F1 scor...