Research on Automatic Detection of Citrus Pests and Diseases Using Deep Neural Network Model

Citrus fruit diseases are a major cause of citrus yield decline. Deep learning methods have achieved promising results in many artificial intelligence problems, which motivates us to apply them to the challenge of recognizing citrus pests and diseases. In this paper, two progressive convolutional neural network (CNN) models are proposed. The proposed CNN models are designed to distinguish between healthy fruits and common citrus pest fruits. The first model is a four-branch parallel CNN based on a feature fusion (ODC-FFN) structure for the automatic detection of citrus diseases. The model takes the size, color, shape, and texture images of citrus surface diseases as inputs and uses a fusion feature extractor to design the four-branch network structure. Through comparison experiments with other models, its recognition accuracy reaches 94.68%. The second model is based on the first model, the multiple attention-based citrus disease classification model (ODC-MAN) is constructed by embedding spatial attention and channel attention mechanisms as feature extractors, and the convolutional layer fusion is changed to classification layer fusion, and the integrated network model is further constructed through integrated learning. The recognition accuracy is further improved to 98.21% by comparison experiments, which indicates that the integrated model using classification layer fusion is effective.