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The growing competition of water available from Nile fresh water, coupled with laws limiting ground water pumping, has led to utilization of different water qualities in irrigation proposals. In this study, the effect of mixed water on yield, biomass and relative chlorophyll meter was estimated. The performance of ground-based remote sensing of hyperspectral passive reflectance sensor and digital image analysis as precision agriculture tools was tested at tubers bulking growth stage to assess their relationship to detractively measured parameters via simple linear regression and three models based on partial least square regression (PLSR) analysis. The results showed that the newly developed indices (R970 - R730)/(R970 + R730) and (R990 - R730)/(R990 + R730) showed close and highly significant associations with yield, biomass and relative chlorophyll meter, with R2 values reach to 0.87, 0.80, and 0.86, respectively, while the green cover (%) was highly significantly related the same parameters, with R2 values reach to of 0.82, 0.73 and 0.70, respectively. Three models of PLSR based on (i) the spectral reflectance from 302 to 1148 nm, (ii) selected nine spectral indices and (iii) selected eight red–blue–green (RBG) indices of the images analysis for the measured parameters were apparently useful for estimating the yield, aerial biomass and relative chlorophyll meter of potato cultivar under fresh and mixed water treatments. In conclusion, the assessment of measured parameters was improved and more robust when using the multivariate analysis of PLSR models than with previously assayed normalized difference spectral indices and RGB indices from digital image analysis.

GROUND-BASED REMOTE SENSING FOR ESTIMATING THE CUMULATIVE EFFECT OF DIFFERENT IRRIGATION WATER QUALITIES ON THE PRODUCTIVITY AND GROWTH OF POTATO