Predictive Analytics and Autonomous Control Systems for Sustainable Agro Facility Management

The challenges faced by agricultural facilities such as poor utilisation of resources, unstable supply of energy resources and intensive control processes that limit operational effectiveness highlight the necessity of an advanced and more automated management system that can handle the processes of predictive analytics and autonomous control and enhance overall performance. The study uses sensor-based data collection, machine-based learning models to predict operational and energy requirements, and a set of autonomous control algorithms implemented via a digital facility management platform. Simulation modelling and field tests were done to compare the system performance with traditional management methods. The findings show that there is significant energy savings, stability during operations and reaction to the real time facility conditions, the predictive models were highly accurate in predicting the resource needs, and the autonomous control layer facilitated timely changes that minimized downtime and enhanced process coordination. The results of the study verify the potential of intelligent automation to reshape the management of agro facilities and offer resilient sustainability benefits to sector wide digitalisation. The results of the study conclude that predictive analytics, combined with autonomous control, reinforces sustainability benefits and improves the overall operational value.