A Back Propagation Neural Networks for Grading Jatropha curcas Fruits Maturity
Z. Effendi, R. Ramli and J. A. Ghani
DOI : 10.3844/ajassp.2010.390.394
American Journal of Applied Sciences
Volume 7, Issue 3
Problem statement: Jatropha curcas has the potential to become one of the world’s key energy crops. Crude vegetable oil, extracted from the seeds of the Jatropha plant, can be refined into high quality biodiesel. Traditional identification of Jatropha curcas fruits is performed by human experts. The Jatropha curcas fruit quality depends on type and size of defects as well as skin color and fruit size. Approach: This research develops a back propagation neural networks to identify the Jatropha curcas fruit maturity and grade the fruit into relevant quality category. The system is divided into two stages: The first stage is a training stage that is to extract the characteristics from the pattern. The second stages is to recognize the pattern by using the characteristics derived from the first task. Back propagation diagnosis model is used to recognition the Jatropha curcas fruits. It is ascertained for the developed system is used in recognizing the maturity of Jatropha curcas fruits. This study presents a pattern recognition system of Jatropha curcas using back propagation. Results: By using back propagation, it gave an accuracy of about 95% based on our samples which used the twenty-seven images. The results produced by neural network were found to be more accurate due to its capability to distinguished complex decision regions. Conclusion: The training data set for back propagation had 4 levels of grading i.e., raw, fruit-aged, ripe and over ripe with twenty-seven images of Jatropha curcas fruits. At the end of the training, the neural network achieved its performance function by testing with a selected set of different images. The performance of the back propagation was satisfactory when incorporated with the software tool, since there were number of errors arising in categorizing.
© 2010 Z. Effendi, R. Ramli and J. A. Ghani. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.