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Updated: Mar 25, 2026

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
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A Novel Vision Sensing System for Tomato Quality Detection.

Satyam Srivastava1, Sachin Boyat2, Shashikant Sadistap3

  • 1Advanced Electronics Systems, ACSIR, CSIR-CEERI, Pilani, Jhunjhunu, Rajasthan 333031, India.

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|February 24, 2016
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Summary

This study introduces an automated vision system for detecting tomato diseases. The system achieves 92% accuracy in identifying and classifying various tomato ailments, aiding in quality inspection.

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Food Technology

Background:

  • Tomato production faces significant challenges due to microbial infections, leading to visible quality defects.
  • Accurate and early detection of tomato diseases is crucial for maintaining crop quality and marketability.

Purpose of the Study:

  • To develop and validate a vision sensing system for automated tomato fruit detection and disease identification.
  • To implement pattern recognition and soft computing techniques for comprehensive tomato quality assessment.

Main Methods:

  • A 12.0-megapixel USB camera interfaced with an ARM-9 processor was used for image acquisition.
  • A Zigbee module enabled wireless data transmission for remote processing and analysis.
  • The system employed preprocessing, classification, and recognition algorithms for disease detection.

Main Results:

  • The developed system successfully detected and classified various tomato diseases with an accuracy of approximately 92%.
  • The system predicted key quality parameters including shelf life, quality index, freshness, and maturity.
  • Results were validated using an aroma sensing technique (Alpha Mos 3000 system).

Conclusions:

  • The vision sensing system offers an effective solution for automated tomato quality inspection and disease management.
  • This technology can significantly improve the efficiency and accuracy of identifying tomato crop health.
  • The system's ability to predict quality parameters provides valuable insights for producers and consumers.