ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR CROP YIELD FORECASTING
DOI:
https://doi.org/10.26662/ijiert.v13i11.pp1-5Keywords:
Artificial intelligence, crop yield forecasting, machine learning, remote sensing, digitalization of agriculture, food security.Abstract
This paper examines the application of artificial intelligence (AI) technologies for forecasting crop yields within the context of the digital transformation of the agro-industrial complex. The relevance of this topic stems from the need to improve the accuracy and timeliness of forecasts amid climate instability—a situation where traditional expert-based methods no longer provide sufficient reliability. The objective of this study is to conduct a comprehensive analysis of machine learning methods used for yield forecasting and to evaluate their potential for enhancing the efficiency of crop production. The paper explores the implementation of AI solutions, drawing on the experience of the Republic of Uzbekistan (e.g., the CropAgro platform and various aerospace monitoring projects). The methodological framework incorporates general scientific methods—such as analysis, synthesis, and a systems approach—combined with a comparative analysis of machine learning models and the case study method.
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