外文摘要：Fruits are an essential part of our diet, providing necessary nutrients that promote good health and proper functioning of our bodies. However, determining fruit quality can be complex due to numerous factors such as harmful insects, fungal diseases and damage caused during the harvesting and transport processes. Current methods employed by industries, such as sensory panels for categorising damage from healthy produce; are not as precise as needed. Therefore, there is a pressing need for a more simple and accurate way to assess the quality of fresh produce. An emerging technology, the electronic nose, presents a cost-efficient and precise solution to this problem. The electronic nose identifies various aromas which helps to evaluate fruit quality. In correlation with this, machine learning models classify fruits into their respective grades using the data collected by the electronic nose. In this review, we delve into the practicalities of using the electronic nose technology and machine learning algorithms to identify the quality of various fruits such as apples, bananas, peaches, litchis, strawberries, and pomegranates. In conclusion, the integration of the electronic nose technology and machine learning models could revolutionise the fruit industry by providing an efficient, precise, and cost-effective method for determining fruit quality.
外文关键词：machine learning;fruits;Diseases;Electronic nose;Quality.
作者：Anwar, H;Anwar, T
作者单位：Bahauddin Zakariya Univ;New Zealand Coll Chiropract
期刊名称：JOURNAL OF ANIMAL AND PLANT SCIENCES-JAPS
期刊影响因子：0.0
出版年份：2024
出版刊次：34（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台