Fruit quality evaluation using image processing: External perspectives

Abstract

Agriculture is one of the most significant industries in the world. It feeds over one billion individuals and produces over $1.3 trillion in food annually. Pastures and crops cover more than half of habitable land, supporting diverse animal life with food and habitat. Sustainable agricultural practices can help maintain and restore key ecosystems, promote sustainable growth, protect watersheds, and improve soil and water quality. Agriculture's deep connections to the global economy, human communities, and biodiversity make it one of the most critical conservation frontiers on the planet. This project aims to develop agricultural innovations using machine learning to perform quality inspections of fruit, enabling users to detect defects reliably and effectively. Machine learning can analyze vast amounts of data to identify trends and patterns that humans might overlook. In this project, a dataset containing images of both fresh and rotten fruits will be used to evaluate their quality. The assessment will focus on key visual attributes such as color, texture, size, shape, and the presence of defects. Consequently, the system is designed to deliver accurate fruit quality inspections with minimal reliance on human expertise or prior knowledge of fruit quality.