Detection of citrus black spot fungus using fluorescence imaging and deep learning on leaf surface

Abstract

Citrus Black Spot (CBS) disease, caused by the pathogenic fungus Phyllosticta citricarpa, presents a significant threat to citrus-growing regions, including Florida. Detecting CBS early is crucial, especially when trees don't yet show symptoms. This early stage provides an opportunity for orchard managers to take preventive measures and curb the disease's spread. In our study, we explore the CSI-D+ system, which combines cutting-edge fluorescence imaging technology with the YOLOv8 deep learning framework. We focus on identifying two CBS fungus variants, GC12 and GM33, commonly found on infected citrus leaves. Sample leaves were inoculated with varying concentration levels of the two variants and imaged by the CSI-D+ device. Impressively, the CSI-D+ system demonstrates exceptional discrimination abilities for discerning variant concentration levels. It achieves a notable mean accuracy of 96.97% for detecting the GC12 fungus, with an F1score of 96.35% and a mean average precision (mAP) of 97.82%. Similarly, for the GM33 variant, the system maintains an average accuracy of 96.17%, an F1-score of 88.76%, and a mAP of 91.64%. The system offers promise as rapid, noninvasive tool for early CBS fungus spore detection on citrus leaves. By providing timely insights, it could empower effective intervention strategies, bolstering orchard resilience against this harmful fungus.