Monitoring of spatial heterogeneity of chlorophyll fluorescence of cotton leaves at the early stage of Verticillium wilt based on spectral imaging

Abstract

Verticillium wilt (VW) is one of the most common and devastating diseases in cotton production, and early diagnosis is very important to alleviate the damage of VW. Recent studies have shown that early diagnosis and prevention of soil-borne diseases can be achieved by detecting spectral changes related to chlorophyll fluorescence and transpiration. However, there are no systematic studies to report the heterogeneity of photosynthetic characteristics and their spectral responses of plant leaves at the early stage of VW. In this study, the spatial heterogeneity characteristics in chlorophyll fluorescence of cotton leaves during the incubation period of VW were discussed, and the pixel-level inversion of the heterogeneity characteristics of leaf chlorophyll fluorescence was realized with hyperspectral imaging information, aiming to realize the early diagnosis of VW of cotton. The results showed that the chlorophyll fluorescence parameters Y(NPQ) (quantum yield of regulated energy dissipation) and NPQ/4 (non-photochemical quenching/4) values of cotton increased and the Y(II) (effective quantum yield of photosystem II) decreased significantly during the asymptomatic period of VW, indicating heterogeneity in photosynthetic capacity of leaves in the early stage of VW, i.e., VW developed from leaf margins to leaf center, and leaf margin was the area where chlorophyll fluorescence changed firstly. Furthermore, the multi-task learning model constructed with vegetation index and wavelet features accurately inversed the pixel-level heterogeneous characteristics of leaf Y(NPQ) and Y(II). The spectral information had the best inversion performance for the local heterogeneous regions of Y(II), with a classification accuracy of 85.6 %, a Kappa coefficient of 0.71, an r² (coefficient of determination) of 0.66, and a RMSE (root mean square error) of 0.06. According to the inversion results of the local heterogeneous region of Y(II), the accurate diagnosis of early-stage VW was realized, with an accuracy of 87.4 % and a Kappa coefficient of 0.75. This study will provide a new method for the early prevention and control of VW.

Plant phenotyping relevance

綿花葉のクロロフィル蛍光という植物生理形質を、ハイパースペクトル画像と機械学習で画素レベル推定し、萎凋病の早期診断へ応用しており、形質取得・抽出手法が中心的である。

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