Leaf water potential of field crops estimated using NDVI in ground-based remote sensing-opportunities to increase prediction precision

Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. However, this detection has typically been accomplished only after the stress effect led to significant changes in crop green biomass, leaf area index, angle and position, and few studies have attempted to estimate the uncertainties of the regression models. These have limited the informed interpretation of NDVI data in agricultural applications. We built a ground-based sensing cart and used it to calibrate the relationships between NDVI and leaf water potential (LWP) for wheat, corn, and cotton growing under field conditions. Both the methods of ordinary least-squares (OLS) and weighted least-squares (WLS) were employed in data analysis, and measurement errors in both LWP and NDVI were considered. We also used statistical resampling to test the effect of measurement errors of LWP on the uncertainties of model coefficients. Our data showed that obtaining a high value of the coefficient of determination did not guarantee a high prediction precision in the obtained regression models. Large prediction uncertainties were estimated for all three crops, and the regressions obtained were not always significant. The best models were obtained for cotton with a prediction uncertainty of 27%. We found that considering measurement errors for both LWP and NDVI led to reduced uncertainties in model coefficients. Also, reducing the sample size of LWP measurement led to significantly increased uncertainties in the coefficients of the linear models describing the LWP-NDVI relationship. Finally, potential strategies for reducing the uncertainty relative to the range of NDVI measurement are discussed.

Hydration kinetics of little millet and proso millet grains: effect of soaking temperature

Hydration characteristics of little millet (Panicum sumatrense) and proso millet (Panicum miliaceum) were studied at different soaking temperatures (30, 40, 50, 60 and 70 °C) and fitted into hydration models. From the initial moisture contents of 11.02 (d.b.) and 10.45% (d.b), little millet and proso millet grains attained the equilibrium moisture content of 38-50.17% (d.b) and 39.11-47.15% (d.b.), respectively. Little millet took 18.5 h to reach equilibrium moisture content at soaking temperature of 30 °C and 3.5 h at 70 °C. For proso millet, it took 4 h at 70 °C and 19 h at 30 °C. The data of moisture content with time fitted to Lewis, Page, modified Page and Peleg models shown higher coefficient of determination values ranging from 0.92 to 0.99. Among the models, the Peleg model is more suitable for the little millet grains and both Page and Peleg models are more suitable for the proso millet grains, to represent the hydration kinetics in the soak water temperature range of 30-70 °C. The dependency of the coefficients of the hydration models with temperature of soaking was found to be linear for both little and proso millets with coefficient of determination values ranging from 0.88 to 0.97.