AI approaches to identification and control of total plant production systems

Abstract Recent progress in intelligent control techniques has enabled complex systems such as cultivation and fruit-storage processes to be dealt with. This paper presents the application of a hierarchical intelligent control system, which consists of an expert system and an optimizer based on neural networks and genetic algorithms, for optimizing a total plant production process. Environmental factors in the cultivation and storage processes are optimally controlled, based on the physiological status of the plant (or fruit). The expert system determines suitable environmental setpoints throughout growth, and the optimizer determines optimal environmental setpoints during important growth stages and during storage, based on plant responses. In the optimizer, neural networks were used for the identification of plant responses to environmental factors, and genetic algorithms were used to search for the optimal environmental setpoints through the simulation of the identified models. Optimal setpoints of the nutrient concentration in hydroponic tomato cultivation and optimal setpoints of the temperature during tomato storage were determined using this control technique.

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