Neural network classifier of jalapeno chile using imaging

Automatic sorting in the canning industry involves searching for simple methods for classifying automatically the products. Reliable classifiers can use discriminant analysis but the actual technology available with neural networks is growing due to its advantages of experience-based learning, generalization, graceful degradation and fault tolerance. In this paper we present a neural network classifier of jalapeno chile based on chile images obtained with a CCD camera. After being singulated, the image is processed and the noise outside its perimeter eliminated before calculating features such as area, length and angle. The area and length under different angles are introduced as training data to a propagation algorithm which classifies chiles in three different categories.