A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning
Yonasi Safari,
Joyce Nakatumba-Nabende,
Rose Nakasi
et al.
Abstract:Automation improves the quality of fruits through quick and accurate detection of pest and disease infections thus contributing to the country's economic growth and productivity. Although humans can identify the fruit damage caused by pests and diseases, methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits, affect their market value. The issue of pest and disease infections further deteriorates fruits' quality… Show more
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