Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case

Abstract

Plants transpire water through their tissues in order to move nutrients and water to the cells. Transpiration includes various mechanisms, primarily stomata movement, which controls the rate of CO2 and water vapor exchange between the tissues and the atmosphere. Assessment of stomatal conductance is available for gas exchange techniques at leaf level, yet these techniques are not scalable to the whole plant let alone a large vegetation area. Hyperspectral reflectance spectroscopy, which acquires hundreds of bands in a single scan, may capture a glimpse of the crop’s physiological activity and therefore meet the scalability challenge. In this study, classic chemometric analyses are used alongside advanced statistical learning algorithms in order to identify stomatal conductance cues in hyperspectral measurements of cotton plants experiencing a gradient of irrigation. Random forest of regression trees identified 23 wavelengths related to both structural properties of the plant as well as water content. Partial least squares regression succeeded in relating these wavelengths to stomatal conductance, but only partially (R2 < 0.2). An artificial neural network algorithm reported an R2 = 0.54 with an 89% error-free performance on the same data subset. This study discusses implementation of machine learning methodologies as a benchmark for deeper analysis of spectral information, such as required when searching for plant physiology-related attenuations embedded within reflectance spectra.

Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models

“…Due to environmental variability, few reports have estimated mid-day photosynthesis. However, our PLSR estimation was better than the observations of Vitrack-Tamam et al ( 2020 ) for cotton stomatal conductance ( R 2 = 0.23); this was likely due to the lower range spectral reflectance device used in their experiment (633–1659 nm). Similar estimations of net photosynthesis were accomplished using the scaled photochemical reflectance index and a FieldSpec Hi-Res Device (Kumari et al 2012 ).…”

Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach

“…The use of other TIR based indices such as the crop water stress index (CWSI) might be a useful addition to the training data set. TIR data might not be the ideal source for stomatal conductance predictions, since other studies were able to predict stomatal conductance from hyperspectral reflectance spectroscopy data using RF and ANN algorithms [84]. Further, the sample size for sap flux was much larger than for stomatal conductance due to a limited amount of porometry devices and the non-automated nature of the measurements.…”

Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application

“…(2) Machine learning (ML) techniques. ML is increasingly used to estimate land surface variables based on large field measurements, including for example partial least squared regression [14], artificial neural network [15], and random forest [16]. These empirical methods need site-specific land surface variables for model training and the trained model may not be suitable for other sites.…”