Hyperspectral sensing of photosynthesis, stomatal conductance, and transpiration for citrus tree under drought condition

Abstract

Abstract Obtaining variation in water use and photosynthetic capacity is a promising route toward yield increases, but it is still too laborious for large-scale rapid monitoring and prediction. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees. To this end, photosynthetic CO 2 assimilation rate ( Pn ), stomatal conductance ( Cond ) and transpiration rate ( Trmmol ) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels. Water stress caused Pn, Cond and Trmmol rapid and continuous decreases in whole drought period. Upper layer was more sensitive to drought than middle and lower layers. Original reflectance spectra of three drought treatments were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. Performance of four machine learning algorithms were assessed and random forest (RF) algorithm yielded the highest predictive power for predicting photosynthetic parameters. Our results indicated that leaf hyperspectral reflectance was a reliable and stable method for monitoring water stress and yield increasing in large-scale orchards. Highlight An efficient and stable methods using hyperspectral features for early and pre-visual identification of drought and machine learning techniques for predicting photosynthetic capacity.

Plant phenotyping relevance

柑橘の光合成・気孔コンダクタンス・蒸散をハイパースペクトル反射と機械学習で推定する手法の開発・評価が中心であり、植物生理形質のハイスループット表現型計測に該当する。

Code and data availability

The paper reports hyperspectral and gas-exchange measurements of lemon trees under drought, but no public dataset, code, or model deposit is provided. The data availability statement explicitly states all data are contained within the article or supplementary material, and no author URL or repository is given.

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