Hybrid neurocontrol of irrigation of field agricultural crops

Abstract

This study investigates a conceptual framework for a hybrid intelligent control system designed to optimize the irrigation practice for field crops via fertigation technologies. This research is aimed at enhancing irrigation management through the improvement of the prediction, optimization, and regulation processes. This is achieved through the incorporation of modern computational intelligence with advanced deep learning based neural networks, evolutionary optimization algorithms, and the adaptive neuro-fuzzy technique. This hybrid control framework is made up of interconnected sets of monitoring and decision-making modules. These include subsystems for evaluation of soil conditions, monitoring of plant growth and physiological development, assessment of environmental and climatic conditions, and measurements of the intensity of solar radiation. Additional systems address the preparation of the fertigation mixture and control of intelligent decision-making processes. For this system, the overall control policy is rendered through a predictive neurocontrol approach with execution on a computer platform. A recurrent deep neural model, long short-term memory (LSTM) type, provides crop growth and development parameter predictions through the ability to explore temporal dependencies in agricultural processes. Optimization in the predictive control feedback is accomplished through genetic algorithms in an adaptive manner.