Research on Wheat Drought Stress Recognition Based on Improved EfficientNet-B0

Wheat is one of the major staple crops in China, and drought stress can severely affect its growth, development, and yield. Rapid and accurate identification of drought stress levels in wheat is of great significance for agricultural disaster prevention and mitigation, as well as for ensuring food security. To address the problems of insufficient fine-grained feature extraction, class imbalance, and unstable training in wheat drought stress image recognition, this study proposes an improved EfficientNet-B0 model for fine-grained wheat drought stress classification. Based on EfficientNet-B0, an improved lightweight Efficient Multi-scale Attention (EMA) module is introduced after the backbone network to enhance both channel-wise and spatial feature representation. PolyLoss is adopted to enhance the learning of low-confidence and difficult samples under the uneven class distribution, while the Sharpness-Aware Minimization (SAM) optimizer is employed to improve the optimization process. Experiments were conducted on a 15-class wheat drought stress image dataset constructed from three key growth stages and five drought severity levels. The proposed model achieved an accuracy of 98.69% and an F1-score of 98.37% on the internal test set, outperforming the baseline EfficientNet-B0 and comparison models including ResNet-50, DenseNet-121, and MobileNetV3. Component-level ablation experiments further showed that the dual-gating structure, projection residual connection, and intra-group Softmax normalization in the proposed EMA module all contributed positively to model performance. These results indicate that the proposed method provides a lightweight and effective approach for wheat drought stress recognition within the current dataset.