Date of Publication :7th May 2016
Abstract: Tomato is one of the most popular fruit in the world. Day by day tomato consumption is increasing. Evaluating tomato maturity is to decide the ripeness and expiry of the tomato fruit. Color in tomato is the most important visible characteristic used to assess ripeness. The main factor of consumer intake is based on the color of the tomato. The image of tomato should undergo process like pre-processing, segmentation and feature extraction. After feature extraction process, feature training and feature matching is done. In feature matching comparison of data images take place in order to get a ripened or a raw tomato. The proposed method gives structure feature as well as texture feature of the input image of tomato. The extracted features are compared by using Artificial Neural Network (ANN) and K-means clustering algorithm.
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