Assessment of crop water status by means of crop reflectance
Aim of this work is to present the procedure for precise plant reflectance measurements in order to estimate the plant water and chlorophyll status in a controlled environment. The method could be applied to conventional or organic greenhouse conditions. A hyperspectral camera was used to provide remotely plant reflectance measurements during periods with normal or low substrate water content. The optic sensor was calibrated into a light-controlled growth chamber. Reflectance measurements were carried out in tomato plants (Solanum lycopersicum 'Elpida') grown on perlite slabs. Well-irrigated pants were used as a reference point during the experimental period, while water stress was applied by withholding irrigation. Radiometric calibration includes the elimination of a variety of noise sources, such as photon noise, thermal noise, read out noise and quantisation noise. The proper number of lens aperture (f/) and exposure time (ms) ranges of the camera for the specific light signal conditions were evaluated, in order to achieve the most suitable readout values. Different algorithms and statistical methods (spectral threshold methods, supervised classification algorithms, unsupervised clustering algorithms) were used to detect and classify the object and extract the suitable information from the plant. Crop reflectance tended to increase as the substrate moisture content decreased from the first hours of irrigation pause. The combination of more than one spectral regions led to reflectance index estimations. The best indices for plant water stress detection were the mrNDVI and mrSRI values as they had the higher correlation with substrate water content. VOGREI and TCARI gave good correlation with plant chlorophyll and nitrogen content, with correlation coefficients up to 0.70.
Elvanidi, A., Katsoulas, N., Bartzanas, T., Ferentinos, K.P. and Kittas, C. 2017. Assessment of crop water status by means of crop reflectance. Acta Hort. (ISHS) 1164:297-304
water stress detection, hyperspectral camera, image classification methods, mrNDVI and mrSRI, chlorophyll content