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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.
Data in Brief
|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.
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.
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