Recognizing the ripeness of bananas using artificial neural network based on histogram approach / Nur Azam Ahmad

• Main Author: Ahmad, Nur Azam
• Format: Thesis
• Language: English
• Published: 2009
• Subjects: Neural networks (Computer science). Data processing
• Online Access: https://ir.uitm.edu.my/id/eprint/68989/1/68989.pdf; https://ir.uitm.edu.my/id/eprint/68989/
• Institution: Universiti Teknologi Mara

This paper presents about recognizing the ripeness of bananas using artificial neural network based on histogram approach. Neural networks are composed of simple elements operating in parallel. These elements are inspired by biological nervous systems. As in nature, the network function is determined largely by the connections between elements. Neural network were train to perform a particular function by adjusting the values of the connections between elements. Commonly neural networks are adjusted, or trained, so that a particular input leads to a specific target output. There, the network is adjusted, based on a comparison of the output and the target, until the network output matches the target. The main objective of this project is developing the technique to classify the ripeness of bananas into 3 categories, which is unripe, ripe and overripe systematically based on their histogram RGB value components. This system involved the process of collecting samples with different level of ripeness, image processing and image classification by using artificial neural network. Collecting bananas sample is done by using Microsoft NX6000 webcam with 2 mega pixels. Image processing stage involves procedure such as image resizing and RGB histogram. 32 samples were used as training for artificial neural network. In order to see whether the method mention above can classify the image correctly, another 28 images was used as testing. From the result obtained, it was shown that the artificial neural network can generally classify the ripeness of bananas. This is because it can classify up to 25 samples correctly out of 28 samples. Developing a program totally by using Matlab version 7.0 can help classification process successfully.