Automated detection of fully and partially riped mango by machine vision

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.