Applications of Artificial Intelligence in Food Industry

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Engineering and Technology".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 20947

Special Issue Editors


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Guest Editor
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Science, Beijing 100190, China
Interests: food computing; food image analysis; food recommendation; multimodal food learning; multimedia content analysis and applications

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Guest Editor
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: nondestructive detection of food quality and safety; optical sensing and automation for food quality evaluation; advanced chemometrics methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With breakthroughs of artificial intelligence (AI) under the fourth industrial revolution, intelligent applications are providing innovative solutions to the food industry. AI is able to support the positive transformation and upgrading of food technologies alongside the digitization, automatization, and intelligence of the food industry in light of its powerful perceiving, reasoning, and decision-making capabilities. Novel applications based on AI are changing all activities in the food industry, ranging from production, processing and packaging, transporting and storing, consuming and disposing, and benefiting all actors in food industry. Combined with technologies such as the Internet of Things (IoT) and Big Data, AI is facilitating the development of new recipes and food, food process simulation and optimization, food quality and safety monitoring, etc. This Special Issue, “Applications of Artificial Intelligence in Food Industry”, focuses on advancing novel methods and applications in the food industry at the crossing of food and AI.

Dr. Weiqing Min
Prof. Dr. Zhiming Guo
Guest Editors

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Keywords

  • artificial intelligence
  • visual food analysis and understanding
  • generative AI methods
  • recipe generation
  • food image/video generation
  • knowledge graphs in food industry
  • multimodal food learning
  • multisensorial food perception and experience
  • AI-enabled decision support systems
  • intelligent systems for health and well-being
  • food quality detection
  • food safety monitoring
  • the combination of AI, IoT, and Big Data

Published Papers (12 papers)

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Research

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16 pages, 1065 KiB  
Article
Disambiguity and Alignment: An Effective Multi-Modal Alignment Method for Cross-Modal Recipe Retrieval
by Zhuoyang Zou, Xinghui Zhu, Qinying Zhu, Hongyan Zhang and Lei Zhu
Foods 2024, 13(11), 1628; https://doi.org/10.3390/foods13111628 - 23 May 2024
Abstract
As a prominent topic in food computing, cross-modal recipe retrieval has garnered substantial attention. However, the semantic alignment across food images and recipes cannot be further enhanced due to the lack of intra-modal alignment in existing solutions. Additionally, a critical issue named food [...] Read more.
As a prominent topic in food computing, cross-modal recipe retrieval has garnered substantial attention. However, the semantic alignment across food images and recipes cannot be further enhanced due to the lack of intra-modal alignment in existing solutions. Additionally, a critical issue named food image ambiguity is overlooked, which disrupts the convergence of models. To these ends, we propose a novel Multi-Modal Alignment Method for Cross-Modal Recipe Retrieval (MMACMR). To consider inter-modal and intra-modal alignment together, this method measures the ambiguous food image similarity under the guidance of their corresponding recipes. Additionally, we enhance recipe semantic representation learning by involving a cross-attention module between ingredients and instructions, which is effective in supporting food image similarity measurement. We conduct experiments on the challenging public dataset Recipe1M; as a result, our method outperforms several state-of-the-art methods in commonly used evaluation criteria. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
13 pages, 2107 KiB  
Article
Exploration of Convective and Infrared Drying Effect on Image Texture Parameters of ‘Mejhoul’ and ‘Boufeggous’ Date Palm Fruit Using Machine Learning Models
by Younes Noutfia and Ewa Ropelewska
Foods 2024, 13(11), 1602; https://doi.org/10.3390/foods13111602 - 21 May 2024
Viewed by 308
Abstract
Date palm (Phoenix dactylifera L.) fruit samples belonging to the ‘Mejhoul’ and ‘Boufeggous’ cultivars were harvested at the Tamar stage and used in our experiments. Before scanning, date samples were dried using convective drying at 60 °C and infrared drying at 60 [...] Read more.
Date palm (Phoenix dactylifera L.) fruit samples belonging to the ‘Mejhoul’ and ‘Boufeggous’ cultivars were harvested at the Tamar stage and used in our experiments. Before scanning, date samples were dried using convective drying at 60 °C and infrared drying at 60 °C with a frequency of 50 Hz, and then they were scanned. The scanning trials were performed for two hundred date palm fruit in fresh, convective-dried, and infrared-dried forms of each cultivar using a flatbed scanner. The image-texture parameters of date fruit were extracted from images converted to individual color channels in RGB, Lab, XYZ, and UVS color models. The models to classify fresh and dried samples were developed based on selected image textures using machine learning algorithms belonging to the groups of Bayes, Trees, Lazy, Functions, and Meta. For both the ‘Mejhoul’ and ‘Boufeggous’ cultivars, models built using Random Forest from the group of Trees turned out to be accurate and successful. The average classification accuracy for fresh, convective-dried, and infrared-dried ‘Mejhoul’ reached 99.33%, whereas fresh, convective-dried, and infrared-dried samples of ‘Boufeggous’ were distinguished with an average accuracy of 94.33%. In the case of both cultivars and each model, the higher correctness of discrimination was between fresh and infrared-dried samples, whereas the highest number of misclassified cases occurred between fresh and convective-dried fruit. Thus, the developed procedure may be considered an innovative approach to the non-destructive assessment of drying impact on the external quality characteristics of date palm fruit. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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13 pages, 1891 KiB  
Article
Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning
by Yanqiu Zhu, Shuxiang Fan, Min Zuo, Baohua Zhang, Qingzhen Zhu and Jianlei Kong
Foods 2024, 13(10), 1570; https://doi.org/10.3390/foods13101570 - 17 May 2024
Viewed by 330
Abstract
The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize [...] Read more.
The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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17 pages, 2572 KiB  
Article
Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm
by Milana Pribić, Ilija Kamenko, Saša Despotović, Milan Mirosavljević and Jelena Pejin
Foods 2024, 13(2), 343; https://doi.org/10.3390/foods13020343 - 22 Jan 2024
Viewed by 863
Abstract
Triticale grain, a wheat–rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes [...] Read more.
Triticale grain, a wheat–rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes and concentrations of the commercial enzyme Shearzyme® 500 L with the aim of evaluating their impact on wort production. In order to capture the complex relationships between the input (triticale ratio, enzyme ratio, mashing regime, and triticale variety) and output variables (wort extract content, wort viscosity, and free amino nitrogen (FAN) content in wort), the study aimed to implement the use of artificial neural networks (ANNs) to model the mashing process. Also, a genetic algorithm (GA) was integrated to minimize a specified multi-objective function, optimizing the mashing process represented by the ANN model. Among the solutions on the Pareto front, one notable set of solutions was found with objective function values of 0.0949, 0.0131, and 1.6812 for the three conflicting objectives, respectively. These values represent a trade-off that optimally balances the different aspects of the optimization problem. The optimized input variables had values of 23%, 9%, 1, and 3 for the respective input variables of triticale ratio, enzyme ratio, mashing regime, and triticale variety. The results derived from the ANN model, applying the GA-optimized input values, were 8.65% w/w for wort extract content, 1.52 mPa·s for wort viscosity, and 148.32 mg/L for FAN content in wort. Comparatively, the results conducted from the real laboratory mashing were 8.63% w/w for wort extract content, 1.51 mPa·s for wort viscosity, and 148.88 mg/L for FAN content in wort applying same input values. The presented data from the optimization process using the GA and the subsequent experimental verification on the real mashing process have demonstrated the practical applicability of the proposed approach which confirms the potential to enhance the quality and efficiency of triticale wort production. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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13 pages, 2299 KiB  
Article
Boosting Purnica granatum L. Seed Oil Yield: An Adaptive Neuro-Fuzzy Interference System Fuels SC-CO2 Extraction Breakthrough
by Padej Pao-la-or, Boonruang Marungsri, Pornariya Chirinang, Kakanang Posridee, Ratchadaporn Oonsivilai and Anant Oonsivilai
Foods 2024, 13(1), 161; https://doi.org/10.3390/foods13010161 - 2 Jan 2024
Viewed by 986
Abstract
This study used supercritical fluid extraction to successfully enhance the conditions for extracting oil from pomegranate seeds. To determine the optimal extraction conditions for maximizing pomegranate oil yield, the researchers employed a Box–Behnken design experimental strategy, involving three parameters with three levels each: [...] Read more.
This study used supercritical fluid extraction to successfully enhance the conditions for extracting oil from pomegranate seeds. To determine the optimal extraction conditions for maximizing pomegranate oil yield, the researchers employed a Box–Behnken design experimental strategy, involving three parameters with three levels each: extraction pressure, extraction temperature, and extraction time. To determine the optimal optimization conditions, the Response Surface Method (RSM) and the Artificial Neural Fuzzy Intelligent System (ANFIS) were also used. The results revealed a strong correlation with the experimental data, demonstrating that both strategies were helpful in optimizing the extraction process. The ideal extraction parameters, according to this study, were an extraction pressure of 40 MPa, an extraction temperature of 55 °C, and an extraction time of 120 min with a CO2 flow rate of 21.3 L/h. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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12 pages, 4583 KiB  
Article
Development of a Low-Cost Artificial Vision System as an Alternative for the Automatic Classification of Persian Lemon: Prototype Test Simulation
by Bridget V. Granados-Vega, Carlos Maldonado-Flores, Camila S. Gómez-Navarro, Walter M. Warren-Vega, Armando Campos-Rodríguez and Luis A. Romero-Cano
Foods 2023, 12(20), 3829; https://doi.org/10.3390/foods12203829 - 19 Oct 2023
Viewed by 1303
Abstract
In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, [...] Read more.
In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, thickness of the peel, diameter, length, and color) of 90 lemons selected from the company Esperanza de San José Ornelas SPR de RL (Jalisco, Mexico) were studied, which were divided into three groups (Category “extra”, Category I, and Category II) according to their characteristics. The parameters of weight (26.50 ± 3.00 g), diameter/length (0.92 ± 0.08) and thickness of the peel (1.50 ± 0.29 mm) did not present significant differences between groups. On the other hand, the color (determined by the RGB and HSV models) presents statistically significant changes between groups. Due to the above, the proposed ANN correctly classifies 96.60% of the data obtained for each of the groups studied. Once the ANN was trained, its application was tested in an automatic classification process. For this purpose, a prototype based on the operation of a stepper motor was simulated using Simulink from Matlab, which is connected to three ideal switches powered by three variable pulse generators that receive the information from an ANN and provide the corresponding signal for the motor to turn to a specific position. Manual classification is a process that requires expert personnel and is prone to human error. The scientific development presented shows an alternative for the automation of the process using low-cost computational tools as a potential alternative. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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15 pages, 1543 KiB  
Article
Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation
by Tianhao Li, Wensong Wei, Shujuan Xing, Weiqing Min, Chunjiang Zhang and Shuqiang Jiang
Foods 2023, 12(17), 3145; https://doi.org/10.3390/foods12173145 - 22 Aug 2023
Cited by 3 | Viewed by 1713
Abstract
The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis [...] Read more.
The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis methods may lack the capability of modeling complex nonlinear relations between spectral information and nutrition content. Therefore, we initiated this study to explore the feasibility of integrating deep learning with NIR-HSI for food nutrition estimation. Inspired by reinforcement learning, we proposed OptmWave, an approach that can perform modeling and wavelength selection simultaneously. It achieved the highest accuracy on our constructed scrambled eggs with tomatoes dataset, with a determination coefficient of 0.9913 and a root mean square error (RMSE) of 0.3548. The interpretability of our selection results was confirmed through spectral analysis, validating the feasibility of deep learning-based NIR-HSI in food nutrition estimation. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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15 pages, 3242 KiB  
Article
A Novel Foodborne Illness Detection and Web Application Tool Based on Social Media
by Dandan Tao, Ruofan Hu, Dongyu Zhang, Jasmine Laber, Anne Lapsley, Timothy Kwan, Liam Rathke, Elke Rundensteiner and Hao Feng
Foods 2023, 12(14), 2769; https://doi.org/10.3390/foods12142769 - 20 Jul 2023
Cited by 1 | Viewed by 1838
Abstract
Foodborne diseases and outbreaks are significant threats to public health, resulting in millions of illnesses and deaths worldwide each year. Traditional foodborne disease surveillance systems rely on data from healthcare facilities, laboratories, and government agencies to monitor and control outbreaks. Recently, there is [...] Read more.
Foodborne diseases and outbreaks are significant threats to public health, resulting in millions of illnesses and deaths worldwide each year. Traditional foodborne disease surveillance systems rely on data from healthcare facilities, laboratories, and government agencies to monitor and control outbreaks. Recently, there is a growing recognition of the potential value of incorporating social media data into surveillance systems. This paper explores the use of social media data as an alternative surveillance tool for foodborne diseases by collecting large-scale Twitter data, building food safety data storage models, and developing a novel frontend foodborne illness surveillance system. Descriptive and predictive analyses of the collected data were conducted in comparison with ground truth data reported by the U.S. Centers for Disease Control and Prevention (CDC). The results indicate that the most implicated food categories and the distributions from both Twitter and the CDC were similar. The system developed with Twitter data could complement traditional foodborne disease surveillance systems by providing near-real-time information on foodborne illnesses, implicated foods, symptoms, locations, and other information critical for detecting a potential foodborne outbreak. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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14 pages, 1461 KiB  
Article
The Estimation of Chemical Properties of Pepper Treated with Natural Fertilizers Based on Image Texture Parameters
by Ewa Ropelewska and Justyna Szwejda-Grzybowska
Foods 2023, 12(11), 2123; https://doi.org/10.3390/foods12112123 - 24 May 2023
Cited by 3 | Viewed by 1054
Abstract
The cultivar and fertilization can affect the physicochemical properties of pepper fruit. This study aimed at estimating the content of α-carotene, β-carotene, total carotenoids, and the total sugars of unfertilized pepper and samples treated with natural fertilizers based on texture parameters determined using [...] Read more.
The cultivar and fertilization can affect the physicochemical properties of pepper fruit. This study aimed at estimating the content of α-carotene, β-carotene, total carotenoids, and the total sugars of unfertilized pepper and samples treated with natural fertilizers based on texture parameters determined using image analysis. Pearson’s correlation coefficients, scatter plots, regression equations, and coefficients of determination were determined. For red pepper Sprinter F1, the correlation coefficient (R) reached 0.9999 for a texture from color channel B and −0.9999 for a texture from channel Y for the content of α-carotene, −0.9998 (channel a) for β-carotene, 0.9999 (channel a) and −0.9999 (channel L) for total carotenoids, as well as 0.9998 (channel R) and −0.9998 (channel a) for total sugars. The image textures of yellow pepper Devito F1 were correlated with the content of total carotenoids and total sugars with the correlation coefficient reaching −0.9993 (channel b) and 0.9999 (channel Y), respectively. The coefficient of determination (R2) of up to 0.9999 for α-carotene content and the texture from color channel Y for pepper Sprinter F1 and 0.9998 for total sugars and the texture from color channel Y for pepper Devito F1 were found. Furthermore, very high coefficients of correlation and determination, as well as successful regression equations regardless of the cultivar were determined. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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22 pages, 2606 KiB  
Article
Health-Aware Food Recommendation Based on Knowledge Graph and Multi-Task Learning
by Yi Chen, Yandi Guo, Qiuxu Fan, Qinghui Zhang and Yu Dong
Foods 2023, 12(10), 2079; https://doi.org/10.3390/foods12102079 - 22 May 2023
Cited by 4 | Viewed by 3288
Abstract
Current food recommender systems tend to prioritize either the user’s dietary preferences or the healthiness of the food, without considering the importance of personalized health requirements. To address this issue, we propose a novel approach to healthy food recommendations that takes into account [...] Read more.
Current food recommender systems tend to prioritize either the user’s dietary preferences or the healthiness of the food, without considering the importance of personalized health requirements. To address this issue, we propose a novel approach to healthy food recommendations that takes into account the user’s personalized health requirements, in addition to their dietary preferences. Our work comprises three perspectives. Firstly, we propose a collaborative recipe knowledge graph (CRKG) with millions of triplets, containing user–recipe interactions, recipe–ingredient associations, and other food-related information. Secondly, we define a score-based method for evaluating the healthiness match between recipes and user preferences. Based on these two prior perspectives, we develop a novel health-aware food recommendation model (FKGM) using knowledge graph embedding and multi-task learning. FKGM employs a knowledge-aware attention graph convolutional neural network to capture the semantic associations between users and recipes on the collaborative knowledge graph and learns the user’s requirements in both preference and health by fusing the losses of these two learning tasks. We conducted experiments to demonstrate that FKGM outperformed four competing baseline models in integrating users’ dietary preferences and personalized health requirements in food recommendations and performed best on the health task. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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Review

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32 pages, 2198 KiB  
Review
Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review
by Hind Raki, Yahya Aalaila, Ayoub Taktour and Diego H. Peluffo-Ordóñez
Foods 2024, 13(1), 11; https://doi.org/10.3390/foods13010011 - 19 Dec 2023
Cited by 3 | Viewed by 1316
Abstract
On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food’s supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food’s safety is [...] Read more.
On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food’s supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food’s safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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29 pages, 4609 KiB  
Review
The Application of Artificial Intelligence and Big Data in the Food Industry
by Haohan Ding, Jiawei Tian, Wei Yu, David I. Wilson, Brent R. Young, Xiaohui Cui, Xing Xin, Zhenyu Wang and Wei Li
Foods 2023, 12(24), 4511; https://doi.org/10.3390/foods12244511 - 18 Dec 2023
Cited by 3 | Viewed by 5538
Abstract
Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous [...] Read more.
Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)
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