基于人工智能和深度学习的农产品质量安全检测系统

Habib Shah, Harish Kumar, Ali Akgül
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摘要

深度学习(DL)已经成为一种高效的技术,用于分析各种领域的大量数据,包括图像处理、语音识别和模式识别。最近,深度学习在食品科学与工程领域也得到了应用,这是一个相对较新的研究领域。本文简要介绍了深度学习,并深入研究了典型卷积神经网络(CNN)结构的架构,以及AI和IoT(物联网)数据训练方法。我们的研究涉及广泛的研究回顾,利用深度学习作为一种计算方法来解决与食品相关的挑战,如食品识别,卡路里计算,各种食品类型(如水果,土豆,肉类和水产品)的安全检测,以及食品供应链管理和食源性疾病检测。每项研究都考察了不同的问题、数据集、预处理技术、网络架构和评估指标,并将其结果与替代解决方案进行了比较。此外,我们还探讨了大数据在食品质量保证领域的作用,揭示了引人注目的趋势。根据我们的分析,深度学习一直优于其他方法,包括手动特征提取器和传统的机器学习算法。研究结果突出了DL作为食品安全检查和食品工业相关应用的一项有前途的技术的巨大潜力
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence and Deep Learning-Based System for Agri-Food Quality and Safety Detection
Deep Learning (DL) has emerged as a highly effective technique for analyzing large volumes of data across various domains, including image processing, speech recognition, and pattern recognition. Recently, DL has also found applications in the field of food science and engineering, a relatively novel area of research. This paper provides a concise introduction to DL and delves into the architecture of a typical Convolution Neural Network (CNN) structure, as well as AI and IoT (Internet of Things) data training methodologies. Our research involved an extensive review of studies that utilized DL as a computational approach to address food-related challenges, such as food recognition, calorie computation, and safety detection of various food types like fruits, potatoes, meats, and aquatic products, as well as food supply chain management and food borne illness detection. Each study examined different problems, datasets, preprocessing techniques, network architectures, and evaluation metrics, comparing their results with alternative solutions. Furthermore, we explored the role of big data in the field of food quality assurance, uncovering compelling trends. Based on our analysis, DL consistently outperforms other approaches, including manual feature extractors and traditional machine learning algorithms. The findings highlight the tremendous potential of DL as a promising technology for food safety inspections and related applications in the food industry
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