Research on food safety sampling inspection system based on deep learning

• Autor(a) principal: CHEN,Tzu-Chia
• Data de Publicação: 2022
• Outros Autores: YU,Shu-Yan
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Food Science and Technology (Campinas)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000100652
• topic: image processing; deep learning; network architectures; learning algorithms

Abstract With numerous promising cases in image processing, voice recognition, target detection, and other fields, deep learning (DL) have proven to be an advanced tool for big data analysis. It's been used in food science and engineering recently as well. This is the first food-related study that we are aware of. We gave a brief overview of DL in this paper, as well as comprehensive descriptions of the structure of some common deep neural network (DNN) architectures and training approaches. We looked at hundreds of publications that used DL as a data processing method to address problems and issues in the food domain, such as food identification, calorie estimating, fruit, potato, meat, and aquatic commodity quality detection, food supply chain, and food pollution. Each study looked at the particular challenges, datasets, preprocessing techniques, networks and systems used, the efficiency achieved, and comparisons with other common solutions. We examined the degree to which big data is being used in the food safety domain and found some positive developments in this article. According to our study results, DL outperforms other approaches such as manual attribute extractors, traditional machine learning algorithms, and DL as a promising technique in food quality and safety inspection.