数字农科院2.0

Identification method of vegetable diseases based on transfer learning and attention mechanism

文献类型： 外文期刊

作者： Xue Zhao;Kaiyu Li;Yunxia Li;Juncheng Ma;Lingxian Zhang

作者机构：

关键词： Attention mechanism;Convolutional neural network;Transfer learning;Vegetable disease

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2022 年 193 卷

页码：

收录情况： SCIE（2022版） ; EI（2022版）

摘要： Artificial Intelligence for disease identification is currently the focus of great research interest. Nonetheless, the approach has some problems, for example, identification takes a long time, has low accuracy, and is often limited to a single disease type. Here, we aimed to identify tomato powdery mildew, leaf mold and cucumber downy mildew against simple and complex backgrounds. We developed a vegetable disease identification model, DTL-SE-ResNet50, optimized by SENet and pre-trained by ImageNet to form a new model, SE-ResNet50. The SE-ResNet50 model was trained with the AI Challenger 2018 public database to obtain a new weight. The SE-ResNet50 model with the new weight was then trained by dual transfer learning with a self-built database to create the DTL-SE-ResNet50 model for the identification of vegetable diseases. The model was compared with convolutional neural networks EfficientNet, AlexNet, VGG19, and Inception V3. The experimental results showed that with the same experimental conditions, the identification precision of the new model reached 97.24%, and processing of a single image required 0.13 s. Compared with DTL-CBAM-ResNet50 and DTL-SA-ResNet50, three models has almost the same precision, but time consumption of DTL-SE-ResNet50 was 0.02 s higher than that of DTL-CBAM-ResNet50. Although the time consumption of DTL-SA-ResNet50 was 0.02 s higher than the proposed model, the precision was lower. At the same time, compared with the dual transfer learning model, the model's precision was 4.1% higher, and the processing of a single image was 0.06 s shorter. Compared with convolutional neural networks, the precision of DTL-SE-ResNet50 was 3.19% higher than the best result, the time consumption of a single image was 0.58 s shorter; Recall and F1 also increased. The method proposed in this paper has high identification precision and short identification time, and it meets the requirements for accurate and rapid identification of vegetable diseases.

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