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Nature's best vs. bruised: A veggie edibility evaluation database.

Bidisha Samanta¹, Sriparna Banerjee¹, Ranadhir Das¹

¹Electronics and Telecommunication Engineering Department, Jadavpur University, Kolkata, India.

|April 11, 2025

Summary

Automated vegetable freshness evaluation uses deep learning for quality sorting. Developing suitable datasets is crucial for accurate classification and improved food industry revenue.

Keywords:

Automated vegetable edibility estimation Computer vision Database comprising vegetables having varied edibility

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Area of Science:

Agricultural technology
Computer vision
Food science

Background:

Automated methods for vegetable freshness assessment are vital for the global food industry.
These techniques evaluate external morphology, texture, and color for quality sorting.
High-quality sorting impacts worldwide food industry revenue.

Purpose of the Study:

To highlight the importance of automated vegetable freshness evaluation.
To discuss the role of deep learning in vegetable classification.
To identify challenges in current automated methods.

Main Methods:

Utilizing automated techniques for assessing vegetable external morphology, texture, and color.
Leveraging advanced deep learning technologies for vegetable categorization.
Training and validation of models using specific databases.

Main Results:

Deep learning enables efficient and cost-effective vegetable classification.
Automated methods are crucial for sorting high-quality produce.
Database limitations pose a significant challenge to method effectiveness.

Conclusions:

Automated vegetable freshness evaluation is essential for quality control.
Deep learning shows promise but requires robust datasets for optimal performance.
Addressing dataset scarcity is key to advancing automated vegetable sorting technology.