One of the most relevant and highly demanded directions in modern precision agriculture is the assessment of vegetation conditions. An accurate assessment of the state of agricultural plants during the growing season is necessary for the effective use of fertilizers, profitable yields and high quality products. The method for solving this problem is based on an analysis of the color characteristics of plants from digital images. In this paper methods of the analysis of color characteristics of plants in aerial photos with various factors of qualitative indicators are examined. In addition, an example of analysis of experimental data is presented using the programming language R. The initial data of the problem are plants’ color parameters L, a, b in special test areas. The test area is a small region of the field where the qualitative indices of plants are already known. In this paper, the following example is considered: there are test areas of wheat with known doses of nitrogen (0, 60, 90, 120 kg of active substance per 1 ha). In addition, certain quality indicators of plants are formed at each site: grain size (large, small), plant protection (weeds, no weeds), seeding rates (6 mmillion per ha, 5 mmillion per ha). The existence of a linear relationship between the color of plants and the dose of nitrogen must be analysed on the basis of various qualitative factors. In the course of solving the problem, algorithms were developed and tested to implement the methods presented. As a result of a preliminary analysis in the example described, the distribution of samples of color characteristics for each pair of factors turned out to be different. In the course of the experiment 8 linear regressions were developed and the regression equations turned out to be statistically significant as a whole. Nevertheless, it should be noted that the coefficient α for the color component of L turned out to be 0. Presumably, this is due to errors during the experiment’s stowage (some of the test areas were laid out later than others). Refs 5. Table 1.