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Application of Deep Learning in Food: A Review

深度学习 人工智能 计算机科学 机器学习 深信不疑网络 特征工程 人工神经网络 深层神经网络 领域(数学分析) 预处理器 质量(理念) 数学 认识论 数学分析 哲学
作者
Lei Zhou,Chu Zhang,Fei Liu,Zhengjun Qiu,Yong He
出处
期刊:Comprehensive Reviews in Food Science and Food Safety [Wiley]
卷期号:18 (6): 1793-1811 被引量:384
标识
DOI:10.1111/1541-4337.12492
摘要

Abstract Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.
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