Identification and counting of mature apple fruit based on BP feed forward neural network

Abstract

Classification of fruits is an onerous and tedious task because of countless number of fruits. The traditional approach for detection and classification of fruit and its maturity level is based on the naked eye observation by the experts, which is both time consuming and causes eye fatigue. Advance techniques in image processing and machine learning helps to automatic classify and count the fruits, accurately, quickly and non-destructively. A method to automatic detect and classify apple fruit maturity level, whether it is mature or immature based on its color features has been proposed. Images of the apple are resized and Image Processing Techniques are applied for the extraction of apple color components (R, G, B). Artificial Neural Network is used as a classifier to identify and count the mature and immature applesusingcolor components. The proposed model has an accuracy of 98.1%, when all the three attributes are used as an input.