Citrus Huanglongbing detection based on polyphasic chlorophyll a fluorescence coupled with machine learning and model transfer in two citrus cultivars

Abstract

Citrus Huanglongbing (HLB) is regarded as the most severe disease threading the citrus industry. The basic aim of this study was to assess the changes of structure and functionality of the photosynthetic machinery induced by HLB and establish a discriminant model for rapid HLB detection using structural and functional features of photosynthesis system. Polyphasic chlorophyll a fluorescence transient was measured from healthy, asymptomatic and symptomatic HLB-infected leaves of two different cultivars namely (Navel orange and Satsuma). According to the polyphasic chlorophyll a fluorescence transient, HLB induced a positive L-band, K-band and J-band; a significant reduction in Fv/Fo, ΦEo, ψEo, ΦRo, ΦPo, PIₐbₛ, PIₜₒₜₐₗ, Fm, REo/RC and ETo/RC, and a significant increase in TRo/RC, Fo, ABS/RC and DIo/RC. The results suggested that the main disturbances of photosynthetic structure and function in HLB-infected leaves were associated with impairment of energetic connectivity of antennae in photosystem II (PSII), dysfunction of oxygen-evolving complex and inhibition of QA⁻ reoxidation. This phenomenon was similar in two different cultivars. Despite the fact that HLB infected symptomatic and magnesium deficient leaves can be often mistaken for each other by human eyes in the field condition, our results showed that some photosynthetic parameters could be considered as a potential proxy for distinguishing them from each other according to the results from principal component analysis (PCA). Moreover, the least squares support vector machine (LS-SVM) established based on the selected JIP-test parameters achieved overall detection accuracies of 95.0% for Navel orange and 96% for Satsuma using model transfer strategy. These results of OJIP records provided valuable information about the structure and function of photosynthetic apparatus in HLB infected leaves, and the photosynthetic fingerprints can be used for high-throughput HLB detection combining with advanced machine learning.

Plant phenotyping relevance

HLB感染葉の光合成生理状態をクロロフィル蛍光で測定し、機械学習による迅速な病徴検出モデルを構築・評価しており、植物表現型の取得と抽出が中心的です。

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