Fruit Quality Detection with Deep Learning and IoT: A Comprehensive Review

Fruit quality detection is a crucial task in the agricultural industry. It impacts the overall market value and consumer satisfaction. Traditional methods for assessing fruit quality are labor-intensive and subjective, leading to inconsistencies and inaccuracies. This paper presents the comprehensive review of fruit quality detection methods such as conventional methods, IoT based methods and machine learning methods. It presents the statistical analysis of usage of these fruit quality detection methods in the related literature. Further, it discusses about the publically available benchmark datasets and their analysis. Additionally, challenges and future research directions are presented to the researchers.