Automated identification of citrus diseases in orchards using deep learning

Abstract

Citrus disease identification is vital to ensure the quality and quantity of production and minimise the damage in orchards. This study proposed a deep learning-based algorithm to perform automated identification on five common types of citrus diseases in orchards. The proposed algorithm consisted of a detection network to detect the citrus fruit in the complicated background and a classification network to classify them into the corresponding types. Several state-of-the-art network architectures were studied in terms of their object detection and classification performance, and they were evaluated on a dataset of 1524 images taken in field conditions from different orchards in distinct time intervals, scales, angles, and lighting conditions. Based on the experimental results, the algorithm eventually adopted an optimised YOLO-V4 model for detection and the EfficientNet model for classification, and the overall algorithm obtained the accuracy and F1 score of 0.890 and 0.872, respectively. In conclusion, the proposed algorithm is capable of automated citrus disease identification in orchards featuring high efficiency and precision.