Diagnosis Method of Tomato Leaf Disease Based on an Improved Deep Convolution Neural Network for Real-life Agriculture Environment

Crop disease diagnosis is of great significance to crop yield and agricultural production. The image identification methods have become the standard methods to address challenges related to disease diagnosis. However, it is still difficult to diagnose accurately for real-life agricultural environment. It has diverse disease conditions and complex backgrounds. We propose an improved deep convolution neural network to identify tomato leaf diseases better for real-life agriculture environments. The proposed network includes residual blocks, a multiscale feature extraction module for extracting multiscale lesions, and an attention module that captures cross-dimension interaction and extracts lesion features better from complex backgrounds. Extensive experimental results show that the proposed network achieves the average identification accuracy of 88.64% on the dataset with the complex backdrop. It also proves that the method has significant advantages in terms of network complexity and realtime performance compared with other models. Moreover, on the other public dataset, the proposed method achieves brilliant results. It is certified that the attention module recognizes disease spots more accurately and has few parameters relative to another state-of-the-art method. The results demonstrate that the proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.