DECISION-MAKING MODEL FOR GREENHOUSE AGRICULTURAL ENTERPRISE OPERATIONS IN THE CASE OF CULTIVATING NEW AGRICULTURAL CROPS

Abstract

A decision-making model has been developed for greenhouse enterprise operations, which includes: a mathematical model for crop yield forecasting based on meteorological indicators and greenhouse microclimate; a greenhouse control model that optimizes temperature, humidity, CO₂ levels, and resource consumption; and an optimization model for crop selection that accounts for economic, environmental, and agronomic factors. Particular attention is given to the cultivation of new crops (specifically bananas), which represents a novel direction for the southern regions of the Republic of Kazakhstan. The models were verified using real data from a banana greenhouse. Simulation results demonstrate a 19 % increase in yield and over 21% growth in profit under optimized management compared to traditional strategies. The proposed approach involves the use of an IoT sensor network, digital monitoring platforms, and artificial intelligence algorithms for adaptive control of the greenhouse microclimate and resource usage. The developed model supports informed managerial decision-making, enhances the efficiency of water and energy use, minimizes environmental impact, and promotes the sustainable development of greenhouse agriculture in regions with extreme climatic conditions.