DECISION TREE METHODS IN GRAIN YIELD FORECASTING
DOI:
https://doi.org/10.17770/etr2024vol2.8023Keywords:
CHAID, decision analysis, decision tree, grain yield, neural networksAbstract
Traditional methods for forecasting yield, which rely on human judgment, often fall short of providing accurate and reliable forecasts. The application of artificial intelligence (AI) methods for predicting grain harvest is becoming increasingly relevant to balance performance indicators of companies in the grain-growing industry and forecast future results. It is important to consider the specific operations of companies in the industry and the factors influencing the harvest when using such methods, as these are essential for future decision-making. The main goal of the study is to explore the use of decision analysis methods in forecasting the yield of companies in the grain-growing industry. An analytical study has been conducted on the potential of using AI methods, including the analysis of decision tree-building methods and their application possibilities. In a practical study, a decision tree is constructed using CHAID, and the impact of various factors on decision-making quality in the grain-growing industry is analyzed. Subsequently, neural networks are used to predict potential yield based on the companies' historical data from previous periods.
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Copyright (c) 2024 Peter Grabusts, Oļegs Uzhga-Rebrov , Lasma Prizevoite, Inta Kotane
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