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The study is devoted to the construction of a mathematical model of the yield of agricultural crops, which includes three components: trend, seasonal, and random. The developed model shows the dependence of yield on phenological indicators, the quality of land resources, management efficiency, and other random factors. The trend and seasonal components of the yield model do not depend on random factors and can therefore be used to predict yield. It is proposed to consider the trend component of the model as a piecewise linear function, and the seasonal component of the model as a linear harmonic regression. A method for analyzing multispectral images with consideration of geoinformation data has been developed to assess phenological indicators. This method includes determining the threshold value using Otsu method to find the density of the agricultural crop in the field. Data on crop density, supplemented with geodata about the plot boundaries, are used to calculate the yield. A comparison of yield forecasts for three crops in the Chernihiv region was made using observations of the phenological indicators of crops throughout the year and over 3 months. It was found that yield is significantly determined by plant development in the first months after germination. The comparison of yield forecasts was made with data from the State Statistics Service of Ukraine and forecasts made using the WOFOST simulation model. It was established that the average relative error of yield prediction using the developed model is 2.96% when using observations of the phenological indicators of crops throughout the year and 4.51% when observing over 3 months. This accuracy is sufficient and comparable to the average accuracy of predictions based on the WOFOST model, which is 3.62%).
Mingxin Huang (Fri,) studied this question.