Classifying adaxial and abaxial sides of diseased citrus leaves with selected hyperspectral bands and YOLOv8

Abstract

Huanglongbing (HLB; citrus greening) and citrus canker are invasive diseases afflicting Florida citrus groves, causing financial losses through yield loss, smaller fruit, blemishes, premature fruit drop and/or eventual tree death. Often, symptoms of these diseases resemble those of other disease or disorders. Early detection of HLB and canker via in-grove leaf inspection can permit more effective mitigation tactics and management of groves. Autonomous, vision-based disease scouting in a grove offers a financial benefit to the Florida citrus industry. This study investigates the potential of hyperspectral reflectance imagery (HSI) for detecting and classifying these conditions in the presence of other, less consequential leaf defects. Leaves with visible symptoms of HLB, canker, zinc deficiency, scab, melanose, greasy spot, and a control set were collected and both sides were imaged with a line-scan hyperspectral camera. Spectral bands from this imagery were selected using two methods: an unsupervised method based on principal component analysis (PCA), a supervised method based on linear discriminant analysis (LDA), which were compared with randomly selected bands as a control. The YOLOv8 network architecture was trained to classify each side of these leaves with each band combination. LDA-selected bands from the front of the leaves yielded an overall classification accuracy of 87.09 %, with higher recall of melanose and precision of control than any other model tested. Leaves with HLB and zinc deficiency were classified most accurately, with both band selection methods yielding F1 scores of at least 0.955 and 0.934, respectively. These findings favor the use of supervised band selection for HSI-based in-grove disease detection.

Keywords

• LDA
• PCA
• HLB
• PC
• DL
• CNN