Growers and farm managers require well-timed information to find out the nutritional and vitality status of their crops (so as to apply appropriate fertiliser or to correct for crop nutrient deficiencies at identified hotspots, for example): Swiss chard (Beta vulgaris) is a leafy vegetable that is cooked before consumption that requires fairly frequent irrigation to keep the soil at more than 50% moisture level. Heavy nutrient levels in the plant could be a direct indicator of the crop having been polluted from the surroundings, which may be a result of heavy metals absorbed from, among others, contaminated soils and wastewater.
The article reports on use of Swiss chard canopy-level hyperspectral measurements and regression algorithms, to determine the concentrations of three foliar macronutrients and three micronutrients in Swiss chard subjected to three water treatments (rainwater + fertilizer, tap water + fertilizer, and treated wastewater). Among other findings, the investigators were able to accurately estimate Swiss chard foliar macronutrient concentrations in comparison to foliar micronutrient concentrations. The results pave the way for developing an effective routine for estimating foliar nutrients that is suitable for monitoring Swiss chard nutrient status under different treatments.
This work is partly funded by the UK Department for International Development, a core donor of the International Centre of Insect Physiology and Ecology.
Abdel-Rahman E.M., Mutanga O., Odindi J., Adam E.M.I., Odindo A. and Ismail R. (2017) Estimating Swiss chard foliar macro- and micronutrient concentrations under different irrigation water sources using ground-based hyperspectral data and four partial least squares (PLS)-based (PLS1, PLS2, SPLS1 and SPLS2) regression algorithms. Computers and Electronics in Agriculture 132, 21–33.
Estimating Swiss chard foliar macro- and micronutrient concentrations under different irrigation water sources using ground-based hyperspectral data and four partial least squares (PLS)-based (PLS1, PLS2, SPLS1 and SPLS2) regression algorithms
Published 28 February 2017