An Art of Review: Fruit Ripeness and Quality Grading Detection Using Deep Learning

Abstract

Accurate detection of fruit ripeness and quality grading are crucial for modern agriculture, as it significantly influences market value, consumer satisfaction and postharvest losses. This overview of the paper examines the latest progress in ripeness detection and quality assessment by deep learning. Although traditional techniques were once dominant, they demonstrate limitations on scalability and accuracy under diverse environmental conditions. Besides machine learning and deep learning, especially with convolutional neural networks (CNN) and models based on YOLO, there’s a lot of potential for better performance. This paper focuses on using multiple labels to sort fruits by how ripe they are, their size, and their quality level, following international standards like USDA and APEDA. This review also points out some important datasets and talks about the challenges, like how changes in the environment can affect things. In future, it proposes the development of generalized, multi-fruit detection models for real-time deployment, with potential applications in smart farming and automated fruit pricing systems.