Fruit Ripeness Classification: a Survey

• URL: http://arxiv.org/abs/2212.14441v1
• Date: Thu, 29 Dec 2022 19:32:20 GMT
• Title: Fruit Ripeness Classification: a Survey
• Authors: Matteo Rizzo, Matteo Marcuzzo, Alessandro Zangari, Andrea Gasparetto, Andrea Albarelli
• Abstract summary: 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. Deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features.
• Score: 59.11160990637616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 experts in the field, which makes it a labor-intensive and error-prone process. Thus, there is an arising need for automation in the process of 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.

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