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| Abstract: |
| To enhance the recognition accuracy of current network models for apple leaf diseases, a lightweight model that leverages an enhanced MobileNetV3-Small architecture is introduced in this study. The improved model utilizes MobileNetV3-Small, a lightweight architecture with fewer parameters, serving as the primary network for feature extraction. It integrates a weighted bi-directional feature pyramid network that fuses multi-scale features, thereby enhancing the model''s capacity to detect disease characteristics across various scales. Additionally, an efficient multi-scale attention mechanism is integrated to mitigate the influence of complex background noise in natural environments, further improving disease recognition accuracy. The experiment utilizes the AppleLeaf9 public dataset to classify healthy apple leaves and eight distinct disease types. The results indicate that, when using the augmented dataset, the improved model achieves a recognition accuracy of 95.98%, with only 1.72M parameters, 123.16M FLOPs, and an inference time of just 14.10 ms. Compared with eight other lightweight neural network models, including MobileNetV2, ShuffleNet_v2_1.5×, ResNet50, MobileNetV3-Large, EfficientNet-B0, MobileNetV3-Small, MobileNetV4-Conv-Small, and MobileNetV4-Conv-Medium, the improved model demonstrates superior accuracy. In particular, the proposed model achieves a recognition accuracy improvement of 0.93 percentage points compared with the baseline MobileNetV3-Small model. The optimized model introduced in this study effectively improves the accuracy in identifying diseases in apple leaves, while maintaining a low parameter count and fast inference speed, thus offering a novel approach for deploying disease recognition models on agricultural electronic devices. |
| Key words: deep learning feature fusion attention mechanism lightweight disease identification |
| DOI：10.11916/j.issn.1005-9113.24065 |
| Clc Number:TU399 |
| Fund: |
