Non-destructive Watermelon Ripeness Determination Using Image Processing and Artificial Neural Network (ANN)
Agriculture products are being more demanding in
market today. To increase its productivity, automation to produce
these products will be very helpful. The purpose of this work is to
measure and determine the ripeness and quality of watermelon. The
textures on watermelon skin will be captured using digital camera.
These images will be filtered using image processing technique. All
these information gathered will be trained using ANN to determine
the watermelon ripeness accuracy. Initial results showed that the best
model has produced percentage accuracy of 86.51%, when measured
at 32 hidden units with a balanced percentage rate of training dataset.
[1] S. A. R. A. B. M.M.Mokji, "Starfruit Grading on 2-Dimensional Color
Map," Regional Postgraduate Conference on Engineering and
Science,Johore, 2006.
[2] R. B. Paolo Gay, "Innovative Techniques for Fruit Color Grading,"
presented at Innovative Techniques for Fruit Color Grading, American
Society of Agricultural and Biological Engineers, St. Joseph,
Michigan,2002 ASAE Annual Meeting, 2002.
[3] E. L. H. Eduard Llobert, Julian W Gardner,Stefano Franco, Nondestructive
Banana Ripeness Determination Using a Neural Network-
Based Electronic Nose, 1999.
[4] E. L. J. Brezmes, X. Vilanova, G. Saiz. X. Correig, Fruit Ripeness
Monitoring Using an Electronic Nose, 2000.
[5] D. S. Perera, Backpropagation neural network based face detection in,
2005.
[6] J. M. J. M. B. M. L. Happel, Design and Evolution of Modular Neural
Network Architectures, vol. 7, pp. 985-1000, 1994.
[7] I. M. Yassin, "Face Detection Using Multilayer Perceptrons Trained on
Min-MAx Features and Optimized Using Perticle Swarm Optimization,"
in Faculty of Electrical Engineering: Universiti Teknologi Mara, 2008.
[8] P. E. H. Richard O. Duda, David G. Stork, Pattern Classification, 2 ed:
www.linkavailable, 2001.
[9] A. Y. M. S. Siti Nordiyana Md Salim, Mohd Noor Ahmad, Abdul Hamid
Adom, Zulkifli Husin, "Development of Electronic Nose for Fruits
Ripeness Determination," presented at 1st International Conference on
Sensing Technology, Palmerston North, New Zealand, 2005.
[1] S. A. R. A. B. M.M.Mokji, "Starfruit Grading on 2-Dimensional Color
Map," Regional Postgraduate Conference on Engineering and
Science,Johore, 2006.
[2] R. B. Paolo Gay, "Innovative Techniques for Fruit Color Grading,"
presented at Innovative Techniques for Fruit Color Grading, American
Society of Agricultural and Biological Engineers, St. Joseph,
Michigan,2002 ASAE Annual Meeting, 2002.
[3] E. L. H. Eduard Llobert, Julian W Gardner,Stefano Franco, Nondestructive
Banana Ripeness Determination Using a Neural Network-
Based Electronic Nose, 1999.
[4] E. L. J. Brezmes, X. Vilanova, G. Saiz. X. Correig, Fruit Ripeness
Monitoring Using an Electronic Nose, 2000.
[5] D. S. Perera, Backpropagation neural network based face detection in,
2005.
[6] J. M. J. M. B. M. L. Happel, Design and Evolution of Modular Neural
Network Architectures, vol. 7, pp. 985-1000, 1994.
[7] I. M. Yassin, "Face Detection Using Multilayer Perceptrons Trained on
Min-MAx Features and Optimized Using Perticle Swarm Optimization,"
in Faculty of Electrical Engineering: Universiti Teknologi Mara, 2008.
[8] P. E. H. Richard O. Duda, David G. Stork, Pattern Classification, 2 ed:
www.linkavailable, 2001.
[9] A. Y. M. S. Siti Nordiyana Md Salim, Mohd Noor Ahmad, Abdul Hamid
Adom, Zulkifli Husin, "Development of Electronic Nose for Fruits
Ripeness Determination," presented at 1st International Conference on
Sensing Technology, Palmerston North, New Zealand, 2005.
@article{"International Journal of Information, Control and Computer Sciences:58992", author = "Shah Rizam M. S. B. and Farah Yasmin A.R. and Ahmad Ihsan M. Y. and Shazana K.", title = "Non-destructive Watermelon Ripeness Determination Using Image Processing and Artificial Neural Network (ANN)", abstract = "Agriculture products are being more demanding in
market today. To increase its productivity, automation to produce
these products will be very helpful. The purpose of this work is to
measure and determine the ripeness and quality of watermelon. The
textures on watermelon skin will be captured using digital camera.
These images will be filtered using image processing technique. All
these information gathered will be trained using ANN to determine
the watermelon ripeness accuracy. Initial results showed that the best
model has produced percentage accuracy of 86.51%, when measured
at 32 hidden units with a balanced percentage rate of training dataset.", keywords = "Artificial Neural Network (ANN), Digital ImageProcessing, YCbCr Colour Space, Watermelon Ripeness.", volume = "3", number = "2", pages = "416-5", }