This paper discusses the development of a software prototype for cucumbers selection and grading by applying Standard Backpropagation Neural Network (SBPNN) and Principal Component Analysis (PCA). The prototypes has been tested to recognize cucumbers based on their shapes (i.e. straight or non-straight cucumbers). Cucumbers images data were expressed in eight position of rotational exes:0˚,45˚,90˚,135˚,180˚,225˚,270˚,315˚. The implemented system can recognized 100% of all tested straight cucumbe - Kudang B. Seminar; Marimin . (Jurnal Keteknikan Pertanian, 2003)
This paper discusses the development of a software prototype for cucumbers selection and grading by applying Standard Backpropagation Neural Network (SBPNN) and Principal Component Analysis (PCA). The prototypes has been tested to recognize cucumbers based on their shapes (i.e. straight or non-straight cucumbers). Cucumbers images data were expressed in eight position of rotational exes:0˚,45˚,90˚,135˚,180˚,225˚,270˚,315˚. The implemented system can recognized 100% of all tested straight cucumbers and 75%of all tested non-straight cucumbers. The performance implemented SBPNN was also compared to another system called Probabilistic Nural Network (PNN). The result shows that SBPNN in generalization or recognition accuracy.