Research on food safety sampling inspection system based on deep learning

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.

An apple leaf disease identification model for safeguarding apple food safety

“…In 2021, Song et al (2021) implemented an apple disease recognition model based on a small-scale dataset and achieved an accuracy of 98.5% on the dataset. In 2021, Chen & Yu (2022) conducted research on food safety sampling inspection system based on deep learning. According to their study results, deep learning outperforms other approaches.…”

Emerging trends in the agri-food sector: Digitalisation and shift to plant-based diets

“…There are also some applications of BD focused on driving demand and meeting consumer needs for plant-based foods, such as developing new smart fruit marketing models in e-commerce ( Ma and Zhang, 2022 ), satisfying date consumers through an automatic image classification system based on 5G technology and deep learning ( Hossain et al, 2018 ), and generating a healthy food recommendation for the end-user in a nutrition-based vegetable system ( Ludena and Ahrary, 2016 ). There is another group of BD applications that cut across the entire agri-food chain, including certifying the quality of olive oil, one of the star products of the Mediterranean diet, using DNA traceability techniques in combination with 4.0 technologies including BD ( Ben Ayed et al, 2022 ), improving dynamic risk management associated to food-borne pathogens leafy vegetables ( Donaghy et al, 2021 ), improving food quality and safety inspection (including fruit) through deep learning ( Chen and Yu, 2022 ; Zhou et al, 2019 ), improving the taste of vegetables by integrating metabolic profiling with other omics methodologies derived from BD ( Zhu et al, 2019 ), and assessing the quality of fruits and vegetables using techniques such as computer vision, image processing and hyperspectral imaging ( Bandal and Thirugnanam, 2016 ).…”

Research on identification method of tangerine peel year based on deep learning

“…In recent years, the application of deep learning technology in agriculture, food, traditional Chinese medicine and other fields has developed rapidly (Chen & Yu, 2022), such as crop diseases and insect pests identification, fruit and vegetable varieties identification, and traditional Chinese medicine decoction pieces identification. Multi-task learning and attention mechanism are also widely used, and new algorithms with high accuracy have been proposed.…”