Abstract
Moisture and nitrogen deficiency are major limiting factors for cereal production in many regions worldwide. Detecting stress in crops at an early growth stage is important if significant reductions in yield are to be averted. In this context, remote sensing has the potential of providing a rapid and accurate tool for precision farming in cereal production. This research was undertaken to investigate the potential of broad band and hyperspectral remote sensing for predicting grain yield of wheat (Triticum aestivum L.) under moisture and nitrogen deficiency stress. A controlled greenhouse experiment was conducted to (i) investigate the influence of moisture and nitrogen induced stress on wheat and the resulting spectral reflectance characteristics at the leaf and canopy scale (ii) assess the effectiveness of different vegetation indices to predict wheat grain yield and (iii) assess the possibility of distinguishing between moisture and nitrogen deficiency stressors. Strong significant correlations between crop grain yield and some vegetation indices were observed. Ratio Vegetation Index (RVI) and Simple Ratio (SR) were found to be sensitive to wheat grain yield (r > 0.80). The correlations with grain yield were found to be strongest at the grain filling stage. Principle Component Analysis (PCA) demonstrated low ability to distinguish between moisture and nitrogen deficiency stress.
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