Dynamic optimization of watering Satsuma mandarin using neural networks and genetic algorithms

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