Forecasting crop yields based on fuzzy analysis of the dynamics of remote sensing multispectral data

Modern technologies for satellite monitoring of the Earth's surface provide agricultural producers with useful information about the health status of crops. The remote sensor's ability to detect subtle differences in vegetation makes it a useful tool for quantifying variability within a given field, estimating crop growth, and managing land based on current conditions. Remote sensing data, collected on a regular basis, allows producers and agronomists to draw up a current vegetation map that reflects the condition and strength of crops, analyze the dynamics of changes in plant condition, and predict yields in a particular area under crops. To interpret these data, the most effective means are various vegetation indices calculated empirically, that is, by operations with different spectral ranges of satellite monitoring multispectral data. Based on the time series of one of these vegetation indices, the paper considers the annual dynamics of the development of a plant culture in a particular field. The possibility of predicting the yield of the given crop is considered based on fuzzy modeling of time series for the corresponding spectral ranges of vegetation reflection obtained from satellite monitoring images. The proposed fuzzy models of time series are investigated for adequacy and suitability in terms of analyzing the features of the intra-annual of average long-term dynamics of the vegetation index, typical for the given area under crop.