Multiple light sources excited fluorescence image-based non-destructive method for citrus Huanglongbing disease detection

Abstract

Citrus Huanglongbing (HLB) poses a significant threat to citrus orchards. Timely HLB screening of citrus trees is essential for citrus orchard management. This study developed a multi-excitation fluorescence imaging system based on the correlation between HLB stress and flavonoid fluorescence characteristics. The feasibility of using fluorescent images to classify healthy, macular (nutrient-deficient, not relate to HLB) and HLB-infected citrus fruits was explored. Initially, three-dimensional fluorescence spectra of citrus peels at maturity were scanned and the obtained Excitation-Emission Matrices (EEMs) were analyzed to screen four fluorescence characteristic regions (FCR1-FCR4) that were sensitive to HLB-infected citrus. Subsequently, four fluorescence imaging conditions (G1: EX = 365 ± 20 nm, EM = 525 ± 20 nm, G2: EX = 415 ± 20 nm, EM = 525 ± 20 nm, B1: EX = 308 ± 20 nm, EM = 450 ± 20 nm, and B2: EX = 365 ± 20 nm, EM = 450 ± 20 nm) were designed based on characteristic fluorescence bands. The imaging system primarily utilizes standard CMOS cameras and optical filters for image acquisition, offering significant advantages in terms of operational simplicity and cost-effectiveness. An HLB classification model was constructed using the Random Forest (RF) algorithm based on color feature parameters of fluorescence images, with a classification accuracy of up to 87.5 %. When the top 10 image feature parameters with the highest contribution rate were selected to construct the classification model considering the equipment cost, the accuracy is 83.33 %. This study demonstrated that fluorescence imaging utilizing flavonoid fluorescence characteristics enables non-destructive and rapid detection of citrus HLB. This approach provides valuable data and technical support for decision-making on spring orchard cleanup and control of HLB.

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