Application of Deep Learning in Food: A Review

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.

African Sorghum-Based Fermented Foods: Past, Current and Future Prospects

“…The authors were able to optimize bioethanol production from sorghum grains, and indicated the effectiveness of the approach in reducing cost, time and effort associated with experimental techniques [186]. Further detailed description, classification and use of these AI and ML techniques is available in the literature, and can be consulted for further reading [187][188][189][190][191]. The specific and potential immediate application of AI and ML to sorghum-based fermented foods include predictive product development and optimization of fermentation processes.…”

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“…As such, the development of datasets will be exponentially tedious with the increasing number of food types and cuisines considered. A recent review by Zhou explored a few key limitations on the current applications of deep learning in food [115]. For the technology to be applicable for general public use, thousands of foods will need to be photographed, annotated and consolidated into a central database.…”

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