Developing a sugar-cane breeding assistant system by a hybrid adaptive learning technique

The traditional sugar-cane breeding process depends on the determination of an experienced breeding researcher. Since the sugar-cane breeding problem in agriculture field is a complex one, the use of computer-aided methodology is very suitable to solve this problem. In this paper, we use the techniques of neural networks and genetic algorithms to construct a method in order to induce the sugar-cane cross model from the sugar-cane parent database established by the Taiwan Sugar Research Institute since 1990. The experimental results show that the correct percentage for testing is about 70%