基于机器视觉的水果腐烂度检测系统设计

Design of a Fruit Decay Detection System Based on Machine Vision

摘要: 本文设计了基于机器视觉的水果腐烂度检测系统，借助人工智能中理论较为成熟的卷积神经网络进行水果腐烂度识别，卷积神经网络具有推理、规划、学习、感知、交流、操作等能力的智能行为，拥有强大的自适应性、学习性、全局最优等功能，在水果种类和品质识别中表现出较好的性能。本检测系统将深度学习应用于水果腐烂度检测，旨在为用户提供更好的体验，提升生活品质。用户可以将购买的水果放心存入冰箱、储物柜等，本检测系统基于YOLOv5目标检测算法设计对存放的水果进行智能识别、腐烂度检测，无需进行人工操作，检测准确率高达90%。一方面给予使用者舒适简单的体验，另一方面为商业上的快速复制创造了条件，为进一步建立智能家居生态链提供了方案。

Abstract: This article presents a fruit decay detection system based on machine vision, which utilises the mature convolutional neural network in artificial intelligence for fruit decay recognition. The convolutional neural network exhibits intelligent behaviours, including reasoning, planning, learning, perception, communication, and operation. It also demonstrates strong adaptability, learning, and global optimisation functions. Its performance in fruit variety and quality recognition has been shown to be effective. This detection system employs deep learning techniques for the purpose of fruit decay detection, with the objective of enhancing user experience and improving quality of life. Users are able to safely store purchased fruits in refrigerators and storage cabinets, among other suitable locations. The detection system is designed based on the YOLOv5 object detection algorithm, which enables the intelligent recognition and detection of the degree of decay of stored fruits, obviating the need for manual operation. The detection accuracy is up to 90%. On the one hand, the system provides users with a comfortable and simple experience. On the other hand, it creates conditions for rapid replication in business, thereby providing solutions for further establishing the smart home ecosystem.