AI Based Fruit Quality Detection Using Image Analysis

The increasing export market demand for good quality fruits has led to develop new techniques for quality assessment. Classical evaluation techniques of fruit quality are very laborious, time consuming and sensitive to human interpretation. In this work, an AI enabled system for analyzing the quality of the fruit based on image processing approaches is proposed to automate the process of fruit inspection. These systems use deep learning methodologies such as Convolutional Neural Networks (CNNs) to analyze fruit images and measure characteristics of fruit, such as color, texture, size, or defects. Comprehensive fruit photography (e.g., clustered or single fruit) can enable AI systems to effectively inspect fruit errors (including discoloration, bruising, deformation etc.) shape-wise in terms of freshness and ripening degree. Even more, the system can predict the quality of a generic fruit through visual properties related to the customer demand. The applied approach enables noninvasive and real-time measurements toward high efficiency and consistency and low human error. This AI-based approach offers successful solutions for automatic fruit-cultivation, sorting and packaging-quality checking, that in the end can result in improved supply chain management, and happy customers.