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International Journal of Food Science
Volume 2014 (2014), Article ID 184894, 11 pages
http://dx.doi.org/10.1155/2014/184894
Research Article

A Novel Vision Sensing System for Tomato Quality Detection

1Advanced Electronics Systems, ACSIR, CSIR-CEERI, Pilani, Jhunjhunu, Rajasthan 333031, India
2Karnataka State Open University (KSOU), Jhunjhunu, Rajasthan 333031, India
3Agri-Electronics Group, CSIR-CEERI, Pilani, Jhunjhunu, Rajasthan 333031, India

Received 13 April 2014; Revised 30 July 2014; Accepted 19 August 2014; Published 4 September 2014

Academic Editor: Alejandro Castillo

Copyright © 2014 Satyam Srivastava et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Producing tomato is a daunting task as the crop of tomato is exposed to attacks from various microorganisms. The symptoms of the attacks are usually changed in color, bacterial spots, special kind of specks, and sunken areas with concentric rings having different colors on the tomato outer surface. This paper addresses a vision sensing based system for tomato quality inspection. A novel approach has been developed for tomato fruit detection and disease detection. Developed system consists of USB based camera module having 12.0 megapixel interfaced with ARM-9 processor. Zigbee module has been interfaced with developed system for wireless transmission from host system to PC based server for further processing. Algorithm development consists of three major steps, preprocessing steps like noise rejection, segmentation and scaling, classification and recognition, and automatic disease detection and classification. Tomato samples have been collected from local market and data acquisition has been performed for data base preparation and various processing steps. Developed system can detect as well as classify the various diseases in tomato samples. Various pattern recognition and soft computing techniques have been implemented for data analysis as well as different parameters prediction like shelf life of the tomato, quality index based on disease detection and classification, freshness detection, maturity index detection, and different suggestions for detected diseases. Results are validated with aroma sensing technique using commercial Alpha Mos 3000 system. Accuracy has been calculated from extracted results, which is around 92%.