A Decision and Control System Mimicking a Skilled Grower's Thinking Process for Dynamic Optimization of the Storage Environment

Abstract

A decision and control system mimicking a skilled grower's thinking process is proposed which was then applied to dynamic optimization of the relative humidity that minimizes both the water loss and the fungal development of fruits during storage. A skilled grower's thinking process consists mainly of two steps: (1) “learning”through experience and (2) “selection and decision”through simulation of a mental model developed in his brain by the learning. After a decision, (3) “action”in a loose and/or sophisticated manner is taken. The decision and control system proposed here consists of two decision systems (I and II) and a fuzzy on-off controller for ventilation. The optimal set points of the relative humidity are first determined using decision system I, and then the relative humidity is controlled based on these set points using a fuzzy on-off controller. In decision system I, neural networks identify the water loss and fungal development of the fruit as affected by the relative humidity (“learning”), and genetic algorithms search for the optimal set points of the relative humidity that minimize the water loss and fungal development of the fruit through simulation of the identified model (“selection and decision”) . The optimal set points obtained here involved a slightly low humidity during the first few days after storage and then higher humidity. The fuzzy on-off controller tuned optimally by decision system II showed good control of the relative humidity. This control technique is widely applicable to dynamic optimizations of complex agricultural systems.