NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions

Abstract

The application of spectroradiometric index such as the normalized difference vegetation index (NDVI) to assess green biomass or nitrogen (N) content has focused on the plant canopy in precision agriculture or breeding programs. However, little is known about the usefulness of these techniques in isolated plants. The few reports available propose the use of a spectroradiometer in combination with special adaptors that improve signal acquisition from plants, but this makes measurements relatively slow and unsuitable. Here we studied the direct use (i.e. without adaptors) of a commercial cost-effective spectroradiometer, GreenSeeker™ (NTech Industries Ins., Ukiah, California, USA) provided with an active sensor (i.e. equipped with its own source of radiation) for measuring NDVI in four genotypes of durum wheat (Triticum turgidum L. var. durum) grown in pots under a range of water and N regimes. Strong correlations were observed between NDVI measurements and dry aboveground biomass (AB), total green area (TGA), green area without spikes (GA) and aboveground N content (AN). To prove the predictive ability of NDVI measured under potted conditions, linear regression models for each growth trait and for plant N content were built with the data of two genotypes. The models accurately predicted growth traits and N content, confirming the direct relationship between total plant biomass and spectroradiometric readings

Bermudagrass, Wheat, and Tall Fescue Crude Protein Forage Estimation using Mobile‐Platform, Active‐Spectral and Canopy‐Height Data

“…Some explanation of this can be offered in considering the potential CP variability, which may occur as a result of mobilization of N in the wheat plant, as morphological changes occurred toward reproductive development in the spring. These findings are in agreement with Cabrera‐Bosquet et al (2011) for NDVI and aboveground N concentration in wheat ( R 2 = 0.47 to 0.71).…”

“…It is intuitive that spectral sensing instrumentation, which provides a light source and can acquire reflectance measurements, will potentially provide a robust method for leaf N concentration estimation. Erdle et al (2011) recorded R 2 values up to 0.96 for active spectral sensors acquiring reflectance measurements at 730 and 760 nm when regressed with wheat leaf N. Cabrera‐Bosquet et al (2011) reported R 2 from 0.47 to 0.71 between NDVI and aboveground N content in wheat. Fricke and Wachendorf (2013), Reddersen et al (2014), and Pittman et al (2015) examined the combination of sensors for biomass estimation.…”

Development of a Mobile Multispectral Imaging Platform for Precise Field Phenotyping

“…NDVI have proven valuable to relate biomass development with yield, light interception, evaluation of scenecense, nitrogen uptake and physiological responses related to chlorophyll contents [1,[22][23][24]. NDVI is to a great extend influenced by canopy spatial structure, growth stage, vegetation density, sensor/camera angle, soil reflectance properties and illumination [22,23,[25][26][27] which may induce much redundant variation.…”

High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling

“…These technologies are attractive alternatives to LiDAR, due to their high performance, flexibility, and relatively low cost (Díaz-Varela et al, 2015). For remote sensing of plants, near-infrared (NIR) cameras have been used in many studies (Lee and Searcy, 1999; Sugiura et al, 2005; Berger et al, 2010; Cabrera-Bosquet et al, 2011; van Maarschalkerweerd et al, 2013; Colomina and Molina, 2014; Díaz-Varela et al, 2015; Torres-Sánchez et al, 2015), because plant leaves (or chlorophylls) strongly reflect NIR light (Knipling, 1970; Tucker, 1979; Fahlgren et al, 2015) and some indices based on NIR reflectance rate, such as normalized difference vegetation index (NDVI; Rouse et al, 1974), are useful for identifying plants and assessing their growing conditions via remote sensing. Some studies have indicated that NIR sensors have advantages over standard RGB sensors in plant monitoring (Nijland et al, 2014; Zhang et al, 2016).…”