Precision agriculture (PA) is the trend in modern agriculture where agricultural technologies are being adapted to the within-field spatial variability of the crop development and yield formation factors. Economic and environmental benefits of PA implementation in crop production depend on the possibility to evaluate the degree of within-field variability of those environmental parameters, which are important for crop development. In this context, the development of identification methods allowing to find the within-field variability of different parameters and to mark the borders between differently managed areas within each agricultural field is of great significance. Relevant basic algorithm for such a method and its implementation diagram are presented in the paper. The within-field variability detection and border marking is based on hyperspectral images and optical criteria (reflection indexes), characterizing specific and non-specific features of crop canopy spectral characteristics under the impact of various stress factors. To confirm the feasibility of the algorithm the changes of plant optical characteristics have been studied. A list of reflection indexes and a set of quantity indicators for each criterion have been obtained for spring wheat physiological condition under optimum environmental parameters and in the conditions of nitrogen and water deficiency. The experiment was conducted under controlled conditions to eliminate possible impacts of other environmental factors on the plants. Significant differences in optical characteristics of the plants affected by nitrogen and water deficiency compared to those with no stress confirmed the reliability of the offered algorithm. The obtained results open new possibilities to automate the process of satellite data interpretation for precision agriculture, initiate research projects where optical criteria for other environmental stress factors might be found. The algorithm realization and the information resource of the IKI-Monitoring center in the nearest future will significantly widen the application of the new method into practice of precision agriculture.