INTELLIGENT TECHNOLOGY FOR SITE-SPECIFIC FORECASTING OF AGRICULTURAL CROP YIELDS
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DOI:
https://doi.org/10.17721/AIT.2025.2.02Keywords:
crop production, yield, vegetation index, site-specific forecasting, models, factors, experimental verification.Abstract
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I n t r o d u c t i o n. Forecasting the yield of agricultural crops plays an important role in ensuring the sustainable development of the global economy. Predicting overproduction of agricultural products or food crises in certain countries makes it possible to carry out timely redistribution of food flows and reservation of appropriate resources. At the level of individual countries, decision-making processes are objectified and measures are taken to increase crop yields and prevent possible losses. The paper proposes a crop yield forecasting technology based on models in which the input factors are indicators of soil quality, the type and quantity of pests, and plant condition. Their optimization is determined by taking into account the characteristics of the external environment and human influence.
M e t h o d s. The research is based on models for identifying the dependence of crop yield on certain factors and on methods of their structural and parametric identification and optimization.
R e s u l t s. A set of models has been developed to determine crop yields as dependencies on factors that are decisive at the sowing stage; on the type and amount of applied fertilizers; on plant treatment technologies and environmental conditions; on irrigation technologies and volumes. Models for determining the vegetation index for each stage of plant impact have been proposed as dependencies on the influences of previous periods. An integrated model for site-specific crop yield forecasting has been developed as a weighted sum of dependency models reflecting the influence on plants during the vegetation period, which made it possible to significantly reduce the average statistical forecasting error.
C o n c l u s i o n s. The results obtained in the paper form a methodological basis for the technology of site-specific forecasting of agricultural crop yields. The necessity of this research is determined by the low accuracy of crop yield forecasting and generalized recommendations regarding agricultural impacts on plants. The application of the proposed models will make it possible to diversify crop production risks and increase agricultural output.
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