Hongyan Wang

Early and accurate identification of tomato leaf diseases constitutes a key safeguard for mitigating economic losses in tomato production. Conventional tomato leaf disease detection methodologies are constrained by inherent limitations, such as low operational efficiency, inadequate detection precision, and limited adaptability to environmental fluctuations. In contrast, the integration of deep learning techniques has yielded improvements in this research domain. Consequently, the development of deep learning-based approaches for the rapid and precise detection of tomato leaf diseases holds considerable theoretical significance and practical application value. To improve the detection accuracy of tomato leaf diseases, this study proposes a transfer learning-based DenseNet disease recognition model named DenseNet-BiFPN-ECA Fusion Network. The bidirectional feature pyramid network (BiFPN) is introduced at the terminal of DenseNet121 to achieve multi-scale feature fusion, while the efficient channel attention (ECA) mechanism is applied to enhance the discriminative capacity of fused features. Classification is ultimately completed via a global average pooling layer and a fully connected layer. The experimental results demonstrate that the improved model achieves an accuracy of 90.63% on the small-sample tomato leaf dataset collected from complex greenhouse environments, representing an improvement of 20.32 percentage points over the original DenseNet121 model. On the large-scale open-source Plant Village dataset, the model attains an accuracy of 98.47%, significantly outperforming the baseline models. Furthermore, a comparative analysis shows that the highest accuracy achieved by DenseNet, ResNet101, and VGG16 models on the same dataset is only 83.59% (within ±0.5%). This result validates the effectiveness of DenseNet-BiFPN-ECA Fusion Network in disease recognition tasks. The model provides a reliable technical reference for the intelligent diagnosis of tomato leaf diseases.