Adversarial Training Collaborating Multi-Path Context Feature Aggregation Network for Maize Disease Density Prediction

Abstract

Maize is one of the world’s major food crops, and its yields are closely related to the sustenance of people. However, its cultivation is hampered by various diseases. Meanwhile, maize diseases are characterized by spots of varying and irregular shapes, which makes identifying them with current methods challenging. Therefore, we propose an adversarial training collaborating multi-path context feature aggregation network for maize disease density prediction. Specifically, our multi-scale patch-embedding module uses multi-scale convolution to extract feature maps of different sizes from maize images and performs a patch-embedding operation. Then, we adopt the multi-path context-feature aggregation module, which is divided into four paths to further extract detailed features and long-range information. As part of the aggregation module, the multi-scale feature-interaction operation will skillfully integrate rough and detailed features at the same feature level, thereby improving prediction accuracy. By adding noise interference to the input maize image, our adversarial training method can produce adversarial samples. These samples will interfere with the normal training of the network—thus improving its robustness. We tested our proposed method on the Plant Village dataset, which contains three types of diseased and healthy maize leaves. Our method achieved an average accuracy of 99.50%, surpassing seven mainstream models and showing its effectiveness in maize disease density prediction. This research has theoretical and applied significance for the intelligent and accurate detection of corn leaf diseases.