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

## TL;DR

This paper introduces a database for evaluating the edibility of vegetables using automated methods to assess freshness and quality.

## Contribution

The paper presents a new database specifically designed for training and validating automated vegetable edibility evaluation systems.

## Key findings

- Automated methods for assessing vegetable freshness are effective but require suitable datasets.
- Current techniques rely on deep learning to categorize vegetables based on morphology, texture, and color.
- A lack of appropriate databases limits the effectiveness of these automated methods.

## Abstract

In the realm of evaluating vegetable freshness, automated methods that assess external morphology, texture, and colour have emerged as efficient and cost-effective tools. These methods play a crucial role in sorting high-quality vegetables for both export and local consumption, significantly impacting the revenue of the food industry worldwide. Researchers have recognized the importance of this area, leading to the development of various automated techniques, particularly leveraging advanced deep learning technologies to categorize vegetables into specific classes. However, the effectiveness of these methods heavily relies on the databases used for training and validation, posing a challenge due to the lack of suitable datasets.