Mango quality assessment is important in meeting market requirements. The quality of the mango can be judge by its length, thickness, width, area, etc. In this paper on the basis of simple mathematical calculations different parameters of a number of mango are calculated. The present paper focused on the classification of mangoes using morphological Operations. A video containing mangoes hanging from the trees is made and used as the input to this algorithm. The video is read frame by frame and the within one frame morphological operations, watershed algorithm and analysis and segmentation are applied. The mango types used in this study were Ripe Mango, Unripe Mango. In this paper the application of neural network is used for assessment of mango. The contours of ripe and unripe mangoes have been extracted, precisely normalised and then used as input data for the neural network. The network optimisation has been carried out and then the results have been analysed in the context of response values worked out by the output neurons.

1. Leta,F. R. et al. , 2005, "Discussing Accuracy in an Automatic Measurement System using Computer Vision Techniques", COBEM 2005.
2. M. Rohs, "Real-World Interaction with Camera-Phones".
3. Fundamenta Informaticae 41 (2001) 187{228 1IOS] Press The Watershed Transform: Definitions, Algorithms and Parallelization Strategies by Jos B. T. M. Roerdink and Arnold Meijster.
4. (2007) Matlab neural network toolbox documentation. MathWorks. Inc. [Online]. Available: http://www. mathworks. com/access/helpdesk/help/toolbox/nnet/radial10. html#8378 Image Processing Toolbox.
5. Kenneth Spring, John Russ and Michael Davidson, Basic Properties of Digital Images, Olympus America Inc.
6. The Scientist and Engineer's Guide to Digital Signal Processing chapter 15 pp. 277.
7. J. Serra (1982): Image Analysis and Mathematical Morphology, Academic Press, London.
8. R. C. Gonzalez and R. E. Woods (1992): Digital Image Processing, Addison-Wesley, New York.
9. Digabel, H. , and Lantuéjoul, C. "Iterative algorithms". In Actes du Second Symposium Européen d'Analyse Quantitative des Microstructur es en Sciences des Matériaux , Biologie et Médecine, Caen, 4-7 October
10. Beucher, S. , and Lantuéjoul, C. "Use of watersheds in contour detection. " In Proc. International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, Rennes, September (1979).
11. Bieniek, A. , & Moga, A. (2000). An efficient watershed algorithm based on connected components. Pattern Recognition, 33, 907-916.
12. Malik Sikandar Hayat Khiyal, Aihab Khan, and Amna Bibi, Modified Watershed Algorithm for Segmentation of 2D Images, Issues in Informing Science and Information Technology Volume 6, 2009.
13. R. Haralick and L. Shapiro Computer and Robot Vision, Vol. 1, Addison-Wesley Publishing Company, 1992, pp 346 – 351.
14. Minsky E. Forsythe. Neural Network. Prentice-Hall, New York, 1983.
15. D. F. Specht, "Probabilistic neural networks for classification mapping, or associative memory," in Proceedings of IEEE International Conferenceon Neural Networks, vol. 1, 1988.
16. Color tracking turret project page. http://sites. google. com/site/colortrackingturret/software.

Mango Quality Assessment Image Recognition Feature Extraction Image Segmentation Neural Network Watershed Algorithm Matlab Gui