Abstract
Remote sensing has been known as a robust technique in precision farming over the last quarter of the 20th century. It has been successfully used to asses many biophysical and biochemical properties of various crops. Detecting stress in crops at an early growth stage is important to limit crop reductions and therefore increasing productivity. Thus, remote sensing may be a valuable tool for precision farming in cereal production. The present study was conducted to investigate the effectiveness of broad band and hyperspectral remotely sensed data to quantify maize (Zea maize L.) grain yield under moisture and nitrogen deficiency stresses. The results demonstrated strong significant correlations between various crop properties and some vegetation indices. RVI, SAVI, OSAVI and R750/R550 were found to be sensitive to maize grain yield (r > 0.80). The correlations with grain yield were found to be strongest at the grain filling stage. Penalized Linear Discrimnant Analysis (PLDA) and Principle Component Analysis (PCA) demonstrated the possibility to distinguish between moisture and nitrogen deficiency stress.
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