Abstract
Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. 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 by conducting a greenhouse experiment. To this end, photosynthetic CO2 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 six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses 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. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards.
Highlights
Introduction distributed under the terms andAgriculture worldwide accounts for up to 70% of the total consumption of water.Water demand for agricultural use in irrigation remains the largest source of consumption, and it will increase by 60% in 2025 [1]
The sensitivity of photosynthetic CO2 assimilation rate (Pn), Cond, and Trmmol to water stress of the upper layer, middle layer, and lower layer were compared in drought treatment period and rewatering period (Figure 3, Tables S2 and S3)
Cond, and Trmmol of normal water supply for the upper layer was significantly higher than other water stresses in the entire drought treatment time, whereas these values of severe drought were significantly lower than other water stresses
Summary
Introduction distributed under the terms andAgriculture worldwide accounts for up to 70% of the total consumption of water.Water demand for agricultural use in irrigation remains the largest source of consumption, and it will increase by 60% in 2025 [1]. Climate change may exacerbate droughts, which may set in more quickly, be more intense, and last longer [2,3]. Fruit trees, such as citrus, which are sensitive to droughts, are already showing decreased yields and poor tolerance to pests and stresses [4]. Photosynthetic efficiency is not just connected with potential yield increases, but it influences efficiency of the use of resources such as water [6]. An important reason for the insufficient exploration of the potential for changes to water use and photosynthesis for fruit yield forecast and quality improvements is the lack of appropriate high-throughput screening methods
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