Abstract
This scientific article presents the results of research focused on developing a method for predicting the Normalized Difference Vegetation Index (NDVI) based on soil chemical composition using a multilayer artificial intelligence (AI) model. This method aims to improve the accuracy and predictive capability of land resource assessment, as well as the impact of chemical factors on vegetation. The study involved collecting soil chemical composition data in various conditions, providing a wide range of information for analysis. For NDVI assessment, a key indicator of vegetation condition, data from modern Earth observation satellite systems were used. The central aspect of the research is the multilayer AI model based on the Rosenblatt perceptron, capable of detecting complex nonlinear relationships between soil chemical parameters and NDVI. The training algorithm was tuned for maximum accuracy and generalization of results. The results show that the developed model provides high accuracy in NDVI predictions, making it an important tool for agriculture, ecology, and sustainable land use. These findings highlight the potential of using AI and soil data to optimize agricultural production, monitor ecosystems, and manage land resources.
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