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.

翻译：水果是全球农业中的关键作物，为数百万人提供食物。水果产品的标准供应链涉及质量检查，以确保新鲜度、口感，最重要的是安全性。决定水果质量的一个重要因素是其成熟阶段。这一过程通常由现场专家手动分类，因此劳动密集型且容易出错。因此，水果成熟度分类的自动化需求日益凸显。许多自动方法已被提出，这些方法利用各种特征描述符对待分级食品项目进行评分。机器学习与深度学习技术主导了性能最佳的方法。此外，深度学习能够直接处理原始数据，从而使用户无需计算复杂的人工设计特征（这些特征通常针对特定作物）。在本综述中，我们回顾了文献中提出的最新自动化水果成熟度分类方法，重点介绍了它们所使用的最常见特征描述符。