Assessment of state-of-the-art deep learning based citrus disease detection techniques using annotated optical leaf images

Abstract

Citrus (Citrus reticulata) plants are affected by several diseases and require keen attention to detect and cure the diseases in time; otherwise, significant financial loss is incurred. With the advancement of computer vision and deep learning techniques, identifying various diseases is becoming simpler. However, this process requires a proper dataset of infected leaves and a suitable detector to recognise the diseases. Because the publicly available citrus leaf datasets are not annotated, they are not suited for disease detection tasks. Therefore, a new dataset (called CCL’20) comprising images of infected citrus leaves with multiple classes of diseases, including precise annotations, is developed. Primarily, machine learning models are used in plant disease detection, and only limited deep learning models are utilised in agricultural applications. This paper has identified the CNN based detectors best suited for agricultural engineering, such as CenterNet, YOLOv4, Faster-RCNN, DetectoRS, Cascade-RCNN, Foveabox and Deformabe Detr, implemented and fine-tuned them to detect citrus leaf diseases using our CCL’20 dataset. Extensive performance and computational analysis is carried out to determine how effectively these models diagnose different stages of citrus leaf diseases. This paper presents the state-of-the-art CNN detectors for citrus leaf disease detection, evaluated based on their precision, recall, and other valuable parameters such as training parameters, inference time, memory usage, speed and accuracy trade-off for each model. The results show that the Scaled YOLOv4 P7 achieves fast and early prediction of the diseases, and CenterNet2 with Res2Net 101 DCN-BiFPN predicts the early stage of citrus leaf diseases with high accuracy to other recent and efficient detecting models.