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

IF 6.3 Q1 AGRICULTURAL ENGINEERING
Pappu Kumar Yadav , Thomas Burks , Jianwei Qin , Moon Kim , Megan M. Dewdney , Fartash Vasefi
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引用次数: 0

Abstract

Citrus Black Spot (CBS), caused by the pathogenic fungus Phyllosticta citricarpa, is a quarantine citrus disease, that has the potential to spread on nursery tress especially in regions like 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 Contamination Sanitization Inspection-Disinfection Plus (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 conidia of the CBS-causing fungus P. citricarpa (Gc12) and its non-pathogenic counterpart P. capitalensis (Gm33), both prevalent on surfaces of infected citrus leaves across varying concentration gradients. Impressively, the CSI-D+ system (which is a new, handheld fluorescence-based imaging device developed to detect microbial contamination and disinfect surfaces rapidly) 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.
利用 YOLOv8 在 UV-C 荧光图像上检测柑橘黑斑病真菌 Phyllosticta citricarpa 和 Phyllosticta capitalensis
柑橘黑斑病(CBS)由致病真菌 Phyllosticta citricarpa 引起,是一种检疫性柑橘病害,有可能在苗圃树上传播,尤其是在佛罗里达等地区。这种疾病的早期发现至关重要,尤其是在树木感染的无症状阶段。这一关键阶段为果林管理者提供了采取预防措施的机会,从而减轻了感染在果园内传播的可能性。在本研究中,我们阐释了污染消毒检测-消毒强化(CSI-D+)系统的强大功能,该系统集成了最先进的荧光成像技术和 YOLOv8 深度学习框架。我们的研究重点是直接检测 CBS 致病真菌 P. citricarpa(Gc12)和非致病真菌 P. capitalensis(Gm33)的分生孢子,这两种真菌都普遍存在于不同浓度梯度的受感染柑橘叶片表面。令人印象深刻的是,CSI-D+ 系统(这是一种新型的手持荧光成像设备,用于快速检测微生物污染和表面消毒)表现出了非凡的辨别能力,Gc12 真菌分类的平均准确率高达 96.97%。此外,其 F1 分数高达 96.35 %,mAP@50 分数高达 97.82 %。此外,我们的研究还包括 Gm33 变体,该系统的平均分类准确率为 96.17%,F1 分数为 88.76%,mAP@50 为 91.64%。这种开创性的系统前景广阔,可作为一种快速、非侵入性的工具,用于早期识别柑橘树木景观中的初期 CBS 侵害。这些进步为果园管理者提供了及时的洞察力,有助于制定有效、及时的干预策略,增强果园抵御病原威胁的能力。
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