A Hyperspectral-Physiological Phenomics System: Measuring Diurnal Transpiration Rates and Diurnal Reflectance

Abstract

A novel hyperspectral-physiological system that monitors plants dynamic response to abiotic alterations was developed. The system is a sensor-to-plant platform which can determine the optimal time of day during which physiological traits can be successfully identified via spectral means. The directly measured traits include momentary and daily transpiration rates throughout the daytime and daily and periodical plant weight loss and gain. The system monitored and evaluated pepper plants response to varying levels of potassium fertilization. Significant momentary transpiration rates differences were found between the treatments during 07:00–10:00 and 14:00–17:00. The simultaneous frequently measured high-resolution spectral data provided the means to correlate the two measured data sets. Significant correlation coefficients between the spectra and momentary transpiration rates resulted with a selection of three bands (ρ523, ρ697 and ρ818nm) that were used to capture transpiration rate differences using a normalized difference formula during the morning, noon and the afternoon. These differences also indicated that the best results are not always obtained when spectral (remote or proximal) measurements are typically preformed around noon (when solar illumination is the highest). Valuable information can be obtained when the spectral measurements are timed according to the plants’ dynamic physiological status throughout the day, which may vary among plant species and should be considered when planning remote sensing data acquisition.

Detection of Potassium Deficiency and Momentary Transpiration Rate Estimation at Early Growth Stages Using Proximal Hyperspectral Imaging and Extreme Gradient Boosting

“…However, the ability to create different models for the different parts of the day was used to develop different TR models that took advantage of the high temporal and spectral resolution database along with the corresponding PA attributes. The best performing model was for the afternoon, similar to our previous findings using a different approach [ 28 ]. Whereas the previous results were obtained by comparing TR variance between parts of the day, and the correlation to spectral bands to capture those differences, this work used a more general approach to capture and model the differences.…”

“…The use of an imaging chamber in a greenhouse is faster, although it creates a queue of plants, each being imaged at a different time and affected by changing ambient conditions. Different ambient conditions have already been proven to affect the usefulness of hyperspectral image analysis [ 28 ]. When phenomics systems are used to study plant function through physiological trait measurements, they are termed functional phenomics systems [ 7 , 9 ].…”

Functional physiological phenotyping with functional mapping: A general framework to bridge the phenotype-genotype gap in plant physiology

“…More importantly, with the rapid deployment of phenotyping facilities all over the world along with their growing phenotyping capacity (e.g. more number of units, smaller size) and the successful integration of lysimeters and imaging system ( Weksler et al., 2020 ), FPP-FM will show its usefulness in serving the translation of physiological data into genetic gain. In principle, our method is extendable to morphological traits showing plasticity and dependence on treatment because the concept of functional mapping is general and the idea of integrating dynamic phenotypes into functional mapping is also general.…”

Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data

“…Water 2022, 14, x FOR PEER REVIEW 3 of 18 This research aimed to evaluate deep neural networks for gap-filling of EC measurements over crops, assuming they could explain the temporal structure of the data, thereby overcoming the obstacles arising from a limited amount of data with rapid temporal changes. Furthermore, specific objectives of this study included: (1) comparing the model performances on different gap durations; (2) testing the effect of adding a non-irrigated crop (wheat) to a training set based on irrigated crops and gap-filled data from irrigated crops (tomato and cotton); and (3) assessing the outcome of using four meteorological variables (net radiation, air temperature, relative humidity, and wind speed) and a subset of them as input values on the performance of the neural networks. The findings could help scientists using the EC system to measure fluxes of ET and may highlight the potential of using these deep learning techniques for interpolating other cyclic multivariate time series.…”

“…Most ET estimation methods are indirect and thus provide only an approximate estimation. Direct methods are expensive and technically complex [1] but provide a fairly accurate and reliable estimate of ET. The eddy covariance (EC) method is a direct approach for measuring field-scale ET over crops [2].…”