Dynamic Optimization of Watering in Satsuma Mandarin using Neural Networks and Genetic Algorithms

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In this study, an optimal watering scheduling that improves the quality of Satsuma mandarin grown in the field was investigated using an intelligent control technique combined with neural networks and genetic algorithms. The monthly data on fruit responses and climate factors were collected from August to November over 1996-2004. Dynamic changes in the sugar and citric acid contents of Satsuma mandarin, as affected by rainfall and sunshine duration, was first identified using neural networks, and then an optimal watering scheduling (rainfall management) that maximizes the sugar content and that minimizes the citric acid of Satsuma mandarin was determined through simulation of the identified neural-network model using genetic algorithms. The optimal value obtained was a combination of the marked increase in watering during the fruit-developmental stage (August and September) and a significant decrease in watering during the fruit-maturing stage (October and November) . From model simulation, a marked increase in watering during the former stage induced a lower citric acid content, and a significant decrease in watering during the latter stage induced an increase in sugar content. Drip irrigation is commonly used for increasing watering whereas plastic-film mulching is used for reducing it.

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