MLSA: A multi-head latent and self-attention deep learning network for tomato leaf disease identification

The tomato is a nutrient-rich vegetable with multiple health benefits for humans. However, leaf diseases have a greater impact on quality. The present methods for identifying leaf diseases are beset by difficulties in extracting multi-scale disease features, insufficient global feature capture, and low recognition accuracy. As a result, we present our deep learning model Multi-Head Latent and Self-Attention (MLSA) for identifying tomato leaf diseases. The suggested model is built on an enhanced AlexNet and includes the multi-head latent attention mechanism, which is noise resistant and focuses on local characteristics, as well as the multi-head self-attention mechanism, which is used to simulate global features. First, the classical AlexNet is optimized to enhance feature extraction depth. Second, the multi-head latent and self-attention mechanisms are integrated to promote the capacity of global feature modeling. Finally, the early stop mechanism is included to improve the model's classification performance. The experimental data consists of 8599 images, including healthy leaves and three typical diseases (i.e., target spot, leaf blight, and early blight). Experimental results demonstrate that the proposed MLSA model achieves an identification accuracy of 99.47% with 16.34M parameters. Compared to the state-of-the-art algorithms EfficientNet-B0, MobileOne-S0, and OverLoCK-B, the proposed approach has a faster computation speed, higher recognition precision, and stronger generalization ability. The current strategy is conducive to the deployment of edge computing devices and has a wide range of promotional significance and application potential in smart agriculture.