Cotton phenotyping and physiology monitoring with a proximal remote sensing system

Abstract

Substantial progress has been made in developing sensor-based proximal phenotyping systems for cotton (Gossypium hirsutum L.), but research is needed to improve in-season prediction of lint yield and to improve accuracy in monitoring crop water stress using such a system. Here, we report on results of a 2-yr field study in which a proximal remote sensing system (measuring canopy height, spectral indices [normalized difference vegetation index, NDVI], and canopy temperature) was deployed every 2 wk over plots of eight cotton varieties at three rates of ET replacement (0, 45, and 90%). As expected, NDVI was an excellent predictor of canopy and biomass traits, including canopy height and leaf area index (LAI). The strength of correlations between in-season sensor measurements (NDVI and the canopy-to-air temperature difference [T c-T a ]) and lint yield ranged from poor to fair when analyzed by irrigation rate and excellent when analyzed across all rates. Correlations were weaker in the drier of the two years tested and NDVI was a better and more consistent predictor of yield than T c − T a , though multiple linear regression integrating both variables improved results by up to 9%. Combining the T c − T a data with onsite atmospheric weather station data allowed calculation of empirical crop water stress index (CWSI) values. Using the CWSI metric, relative differences in crop water stress were clear among ET replacement levels once the canopy width reached the threshold for focusing the infrared temperature sensor on the canopy. This indicated that cotton water stress can be successfully monitored using a proximal phenotyping system, which can be quickly and easily deployed across many plots in research or production settings.

Restructuring plant types for developing tailor‐made crops

“…Along with restructuring the crop architecture, good agricultural practices are crucial. Modernization of agricultural practices through advanced satellite imagery and remote sensing‐based information on environmental conditions such as soil moisture and nutrient properties, temperature variations during flowering and seed filling stages, and rainfall and storm updates can minimize yield loss in major crops globally (Adams et al ., 2021; De Swaef et al ., 2021; Pineda et al ., 2021). Artificial intelligence could be efficiently utilized to predict yield in crops and even minimize use of fertilizers according to soil nutrient profile.…”

Estimating yield-contributing physiological parameters of cotton using UAV-based imagery

“…In this study, models based on raw bands of near-infrared and red reflectance were also able to moderately estimate IPAR f throughout the growing season, further highlighting the importance of these two reflectance bands in explaining the variability in IPAR f . NDVI, which is also a function of near-infrared and red reflectance bands, has been used to predict cotton LAI and canopy cover (Ritchie et al, 2010;Adams et al, 2021). Guillen-Climent et al ( 2012) also reported that out of several VIs, NDVI was best correlated with IPAR f for crops with homogenous canopies such as wheat, maize, and soybean.…”

Rethinking plant breeding and seed systems in the era of exponential changes

“…This involves identifying and enhancing traits related to nutrient use efficiency, water stress tolerance, disease resistance, and response to environmental cues. By incorporating these traits into improved seeds, breeders can provide farmers with crops that perform optimally in precision agriculture systems (Basu;Parida, 2023;Abichou;Solan;Andrieu, 2019;Adams;Ritchie;Rajan, 2021).…”