An intelligent control for greenhouse automation, oriented by the concepts of SPA and SFA — an application to a post-harvest process

• Published in: Computers and electronics in agriculture Vol. 29; no. 1; pp. 3 - 20
• Main Authors: Morimoto, Tetsuo, Hashimoto, Yasushi
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2000; Elsevier
• Subjects: Agricultural systems; Agronomy. Soil science and plant productions; Biological and medical sciences; Chaos; Fundamental and applied biological sciences. Psychology; Fuzzy logic control; General agronomy. Plant production; Genetic algorithms; Harvesting. Postharvest. Storage; Neural networks; Optimization; Vegetative propagation. Sowing and planting. Harvesting; Chaos; Fuzzy logic control; Agricultural systems; Neural networks; Optimization; Genetic algorithms; Relative humidity; Automation; Fruit; Citrus fruit; Optimization method; Postharvest; Stored food product; Protected cultivation; Neural network; Orange; Environmental control; Fuzzy logic; Postharvest physiology; Fuzzy control; Storage; Genetic algorithm; Optimal control; Ventilation; Objective function; Artificial intelligence; Intelligent control
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/S0168-1699(00)00133-2

For better quality of fruit during the storage process, it is essential to control the environment optimally, taking the physiological status of the fruit into consideration. This has been explored as a ‘speaking fruit approach (SFA)’. In this study, an intelligent control technique based on the SFA is proposed. It consists of a fuzzy controller and two optimizers. Optimizers I and II, which both comprise neural networks and genetic algorithms, respectively determine an optimal setpoint of the environment for the fuzzy controller based on fruit responses during storage and tune the fuzzy controller optimally. Neural networks are used for identifying fruit responses and the genetic algorithms for determining an optimal value through simulation of the identified model. The fuzzy controller controls the environment based on the setpoints determined by optimizer I. The control input is the relative humidity in the storage chamber, and the controlled output is the water loss of the fruit. This control technique is widely applicable to sophisticated control of complex agricultural systems.