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

Abstract

The traditional sugar-cane breeding process depends on the determination of experienced breeding researcher. It's well known that computer-aided methodology is very useful in many fields today, such as CAD, CAM, and CAI. Since the sugar-cane breeding problem in agriculture field is complex, it seems that using 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 which established by Taiwan Sugar Research Institute (TSRI) since 1990. The experimental results show that the correct percentage for testing is about 70%.