Automated Risk Assessment and Control Framework for Feed Production in Digital Agroengineering Systems

Digital transformation in agricultural production requires automated approaches for monitoring, risk assessment, and decision support under uncertainty. This study proposes an automated risk assessment and control framework for feed production in digital agroengineering systems. The framework is designed for the quantitative evaluation of producer and consumer risks arising during the control of key feed-quality parameters, using the case of feed production for cattle in the OHMK agricultural holding. The proposed approach integrates probabilistic modeling, simulation-based risk estimation, fuzzy logic, expert evaluation, and a multi-agent representation of agroengineering processes. A three-dimensional risk model is developed to represent producer risk, consumer risk, and actuarial risk as interconnected components of a digital control environment. In addition, a fuzzy model is introduced to assess the robustness and digital maturity of management functions, including organization, planning, motivation, and control. Computer experiments based on statistical data for crude protein content in silage demonstrate that control risks depend nonlinearly on measurement uncertainty, parameter variability, and normative thresholds. In the analyzed single-indicator case study, the arithmetic mean of crude protein content in silage was 7.5% of dry matter, the standard deviation was 0.5, and the Weibull approximation parameters were α = 1.0, β = 2.5, and γ = 6.0. Under the most sensitive normative threshold scenario, producer risk increased to approximately 25%, while consumer risk showed a lower but nonlinear increase with measurement uncertainty. The results show that producer risk may reach significant levels when measurement uncertainty becomes comparable with the variability of the controlled parameter. The proposed framework can serve as a computational basis for future automated monitoring, risk-aware control, and decision-support systems in Industry 4.0-oriented agricultural production.