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：水果是全世界数百万人赖以生存的农业的主要作物。水果产品的标准供应链涉及质量检查,以保证新鲜、口味和最重要的是安全。决定水果质量的一个重要因素是其成熟阶段。这通常是由实地专家手工分类的,因此它是一个劳动密集型和容易出错的过程。因此,在水果成熟程度分类方面出现了自动化的需要。许多自动方法已经提出要对食品品品进行分级使用各种特征描述。机械学习和深层学习技术主导着最高性能的方法。此外,深层学习可以依靠原始数据操作,从而让用户不必计算复杂的工程特征,而这些特征往往是作物特有的。在这次调查中,我们审查了文献中为水果成熟程度分类自动化而提议的最新方法,突出了它们操作的最常见特征描述符。</s>