Fruit Ripeness Classification: a Survey

TL;DR

This survey reviews recent machine learning and deep learning methods for automating fruit ripeness classification, emphasizing the shift towards raw data processing and the variety of feature descriptors used.

Contribution

It provides a comprehensive overview of current techniques in fruit ripeness classification, highlighting the advantages of deep learning over traditional feature-based methods.

Findings

Deep learning methods outperform traditional feature descriptors.

Most recent approaches operate directly on raw data.

Automation reduces labor and errors in fruit quality assessment.

Abstract

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by field experts, making it a labor-intensive and error-prone process. Thus, there is an arising need for automation in fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on.