Detection of citrus black spot fungi Phyllosticta citricarpa and Phyllosticta capitalensis on UV-C fluorescence images using YOLOv8

Authors: YADAV, PAPPU - University Of Florida; BURKS, THOMAS - University Of Florida; Qin, Jianwei; Kim, Moon; DEWDNEY, MEGAN - University Of Florida; VASEFI, FARTASH - Safetyspect Inc

Submitted to: Smart Agricultural Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/20/2024
Publication Date: 10/26/2024
Citation: Yadav, P., Burks, T., Qin, J., Kim, M.S., Dewdney, M., Vasefi, F. 2024. Detection of citrus black spot fungi Phyllosticta citricarpa and Phyllosticta capitalensis on UV-C fluorescence images using YOLOv8. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2024.100615.
DOI: https://doi.org/10.1016/j.atech.2024.100615

Interpretive Summary: Citrus black spot (CBS) is a quarantine fungal disease that can limit market access and thus cause economic losses for citrus growers. Early detection of CBS would enable control for the spread of the disease and prevent infected fruits from entering the packing stream. This study developed a method for detecting CBS fungus using a contamination, sanitization inspection and disinfection (CSI-D+) handheld fluorescence imaging device, which was commercialized based on an ARS patented technology. UV-C fluorescence images were acquired from both CBS fungus Phyllosticta citricarpa and Phyllosticta capitalensis at six concentrations. Based on a YOLOv8 deep learning framework, average accuracies were achieved at 96.97% and 96.17% to classify the concentration levels of Phyllosticta citricarpa and Phyllosticta capitalensis, respectively. The CSI-D+ handheld UV-C fluorescence imaging and deep learning techniques would benefit citrus industry and regulatory agencies (e.g., FDA and USDA APHIS) in ensuring and enforcing the quality and safety standards for the citrus-related food and beverage products.

Technical Abstract: Citrus Black Spot (CBS) disease, caused by the pathogenic fungus Phyllosticta citricarpa, poses a significant and far-reaching threat, particularly to regions such as Florida and beyond. The early detection of this disease assumes paramount importance, especially during the asymptomatic phase of tree infection. This critical stage offers a window of opportunity for grove managers to implement preemptive measures, thereby mitigating the potential dissemination of the infection within orchards. In the present study, we elucidate the robust capabilities of the CSI-D+ system, which integrates state-of-the-art fluorescence imaging technology, in tandem with the YOLOv8 deep learning framework. Our investigation is centered on the direct detection of CBS fungus Phyllosticta citricarpa (GC12) and its non-pathogenic counterpart Phyllosticta capitalensis (GM33), both prevalent on infected citrus leaves across varying concentration gradients. Impressively, the CSI-D+ system exhibits remarkable discriminatory acumen, achieving a noteworthy mean classification accuracy of 96.97% for GC12 fungus classification. This precision is complemented by an impressive F1-score of 96.35%, coupled with a commendable mAP@50 score of 97.82%. Furthermore, our inquiry extends to encompass the GM33 variant, wherein the system maintains a commendable average classification accuracy of 96.17%, alongside an F1-score of 88.76%, and a mAP@50 of 91.64%. Such pioneering systems bear substantial promise, serving as a rapid, non-invasive instrument for the early identification of incipient CBS infestations within citrus arboreal landscapes. In equipping grove managers with timely insights, these advancements stand to empower effective and timely intervention strategies, fortifying orchard resilience against the progression of this pathogenic menace.