Investigating the Association Between Citrus Huanglongbing and Chlorophyll Content Using Hyperspectral Detection.

Abstract

Huanglongbing (HLB) poses a severe threat to the sustainable citrus industry, causing significant alterations in the spectral reflectance and leaf chlorophyll content (LCC) of citrus leaves. This study investigates the quantitative relationship between spectral characteristics and LCC for the early detection of HLB in Mianju mandarin cultivars. We analyzed hyperspectral data from healthy and HLB-infected leaves, employing the least absolute shrinkage and selection operator (LASSO) method and spectral indices to select chlorophyll characteristic bands, and several machine learning models were used to estimate the LCC. The results indicate that: (1) HLB-infected leaves exhibit significantly different spectral reflectance, characterized by a distinct "blueshift of the red edge"; (2) a greater proportion of characteristic bands for HLB-infected leaves were located in the near-infrared region compared to healthy leaves; and (3) the LASSO-PLSR model demonstrated high predictive accuracy for LCC estimation-for healthy leaves (Rv 2 = 0.956, RMSEv = 0.675) and for HLB-infected leaves (Rv 2 = 0.816, RMSEv = 4.614)-with performance being notably superior for healthy leaves (Rv 2 difference of +0.146). This research establishes a systematic quantification between hyperspectral and chlorophyll content, suggesting that hyperspectral-based LCC estimation can serve as a reliable indirect indicator for the early detection of HLB, with substantial practical application potential.

Code and data availability

The supplied blocks describe hyperspectral/SPAD data collection and machine learning analysis for citrus HLB chlorophyll estimation, but contain no public data deposit, code repository, or availability statement. The only URL present is the CC BY license notice, which is not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.