Applications of Artificial Intelligence in Food Processing and Food Industries

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Food Process Engineering".

Deadline for manuscript submissions: 25 July 2024 | Viewed by 6089

Special Issue Editors


E-Mail Website
Guest Editor
Food Science and Biotechnology, Kangwon National University, Chuncheon 24341, Republic of Korea
Interests: mathematical modelling in food and bioprocesses; computer simulations; biopolymer rheology
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Food Science, National Chiayi University, Chiayi City 60004, Taiwan
Interests: food engineering simulation; food texture modification; dysphagia-friendly formulation; sustainable processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is playing a major role in the fourth industrial revolution. AI techniques are widely used by practicing engineers to solve intractable problems in food processing, resulting in significant success in a various domain, including advanced image processing and pattern detection, signal processing or recognition, sorting fresh produce, food safety compliance monitoring, effective cleaning in place systems, anticipating consumer preference and new product development with greater efficiency. Multinational corporations are increasingly investing in IoT-driven food processing industries. Recent advancements in computing capacity have allowed realizing complex AI techniques for the monitoring and automation of food processes. The rate of progress in the fields of IoT, big data, and AI is incredible, and activities that were unachievable just a few years ago have now been successfully implemented. Embracing such technological innovations and putting them to work is important for the success of the modern food processing techniques and food industry.

This special issue on “The applications of Artificial Intelligence in Food Processing and Food Industries” aims to provides a collection of high-quality manuscripts related to the implementation of AI or any of its subdivisions in food processing and food industries such as, but not limited to:

  • computer vision
  • machine learning
  • deep learning and real-world applications
  • sensor technology
  • big data analytics, and internet of things (IoT)
  • integrating explainability into existing AI systems
  • aspects of software engineering, such as intelligent programming environments, verification and validation of AI-based software, and hardware architectures enabling real-time AI approaches, safety, and reliability
  • industrial experiences in the application of the above techniques, e.g., case studies or benchmarking exercises

Dr. Won Byong Yoon
Dr. Huai-Wen Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • modelling
  • food
  • dimension reduction analysis
  • decision making algorithm
  • smart packages
  • smart factory

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 3246 KiB  
Article
Automatic Detection of Banana Maturity—Application of Image Recognition in Agricultural Production
by Liu Yang, Bo Cui, Junfeng Wu, Xuan Xiao, Yang Luo, Qianmai Peng and Yonglin Zhang
Processes 2024, 12(4), 799; https://doi.org/10.3390/pr12040799 - 16 Apr 2024
Viewed by 873
Abstract
With the development of machine vision technology, deep learning and image recognition technology has become a research focus for agricultural product non-destructive inspection. During the ripening process, banana appearance and nutrients clearly change, causing damage and unjustified economic loss. A high-efficiency banana ripeness [...] Read more.
With the development of machine vision technology, deep learning and image recognition technology has become a research focus for agricultural product non-destructive inspection. During the ripening process, banana appearance and nutrients clearly change, causing damage and unjustified economic loss. A high-efficiency banana ripeness recognition model was proposed based on a convolutional neural network and transfer learning. Banana photos at different ripening stages were collected as a dataset, and data augmentation was applied. Then, weights and parameters of four models trained on the original ImageNet dataset were loaded and fine-tuned to fit our banana dataset. To investigate the learning rate’s effect on model performance, fixed and updating learning rate strategies are analyzed. In addition, four CNN models, ResNet 34, ResNet 101, VGG 16, and VGG 19, are trained based on transfer learning. Results show that a slower learning rate causes the model to converge slowly, and the training loss function oscillates drastically. With different learning rate updating strategies, MultiStepLR performs the best and achieves a better accuracy of 98.8%. Among the four models, ResNet 101 performs the best with the highest accuracy of 99.2%. This research provides a direct effective model and reference for intelligent fruit classification. Full article
Show Figures

Figure 1

19 pages, 6544 KiB  
Article
A Study of the Physical Characteristics and Defects of Green Coffee Beans That Influence the Sensory Notes Using Machine Learning Models
by Blanca Gonzalez-Sanchez, Oscar Sandoval-Gonzalez, Jose de Jesus Agustin Flores-Cuautle, Ofelia Landeta-Escamilla, Otniel Portillo-Rodriguez and Gerardo Aguila-Rodriguez
Processes 2024, 12(1), 18; https://doi.org/10.3390/pr12010018 - 20 Dec 2023
Viewed by 1340
Abstract
This paper presents a detailed analysis of the relation between physical characteristics and defects of green coffee beans and the sensory profile that influence the sensory notes of fragrance, aroma, flavor, and aftertaste of coffee. Machine learning models were used to identify the [...] Read more.
This paper presents a detailed analysis of the relation between physical characteristics and defects of green coffee beans and the sensory profile that influence the sensory notes of fragrance, aroma, flavor, and aftertaste of coffee. Machine learning models were used to identify the variables of importance and identify the ways in which these variables affect the sensory note of coffee, to determine which algorithm and its hyperparameters have greater precision in determining the sensory values of coffee such as floral, fruity, herbal, nutty, caramel, chocolate, spicy, resinous, pyrolytic, earthy, fermented, and phenolic. The result indicates the relationship and importance that exist between the physical variables, defects, and size of the green coffee bean, with respect to their respective sensory notes. The data of the proposed system demonstrate that by combining the scores of several experts, a precision can be achieved analogously to that obtained by cupping experts; therefore, the possibility of errors induced by human concerns such as fatigue or subjectivity is reduced. Full article
Show Figures

Figure 1

16 pages, 3541 KiB  
Article
Classification of Quality Characteristics of Surimi Gels from Different Species Using Images and Convolutional Neural Network
by Won Byong Yoon, Timilehin Martins Oyinloye and Jinho Kim
Processes 2023, 11(10), 2864; https://doi.org/10.3390/pr11102864 - 28 Sep 2023
Cited by 1 | Viewed by 1097
Abstract
In the aspect of food quality measurement, the application of image analysis has emerged as a powerful and versatile tool, enabling a highly accurate and efficient automated recognition and the quality classification of visual data. This study examines the feasibility of employing an [...] Read more.
In the aspect of food quality measurement, the application of image analysis has emerged as a powerful and versatile tool, enabling a highly accurate and efficient automated recognition and the quality classification of visual data. This study examines the feasibility of employing an AI algorithm on labeled images as a non-destructive method to classify surimi gels. Gels were made with different moisture (76–82%) and corn starch (5–16%) levels from Alaska pollock and Threadfin breams. In surimi gelation, interactions among surimi, starch, and moisture caused color and quality shifts. Color changes are indicative of structural and quality variations in surimi. Traditional color measuring techniques using colorimeter showed insignificant differences (p < 0.05) in color values and whiteness among treatments. This complexity hindered effective grading, especially in intricate formulations. Despite insignificant color differences, they signify structural changes. The Convolutional Neural Network (CNN) predicts the visual impact of moisture and starch on gel attributes prepared with different surimi species. Automated machine learning assesses AI algorithms; and CNN’s 70:30 training/validation ratio involves 400–700 images per category. CNN’s architecture, including input, convolutional, normalization, Rectified Linear Unit (ReLU) activation, and max-pooling layers, detects subtle structural changes in treated images. Model test accuracies exceed 95%, validating CNN’s precision in species and moisture classification. It excels in starch concentrations, yielding > 90% accuracy. Average precision (>0.9395), recall (>0.8738), and F1-score (>0.8731) highlight CNN’s high performance. This study demonstrates CNN’s value in non-destructively classifying surimi gels with varying moisture and starch contents across species, and it provides a solid foundation for advancing our understanding of surimi production processes and their optimization in the pursuit of high-quality surimi products. Full article
Show Figures

Figure 1

17 pages, 2365 KiB  
Article
Prediction of Winter Wheat Harvest Based on Back Propagation Neural Network Algorithm and Multiple Remote Sensing Indices
by Hong Ji, Xun He, Wanzhang Wang and Hongmei Zhang
Processes 2023, 11(1), 293; https://doi.org/10.3390/pr11010293 - 16 Jan 2023
Cited by 4 | Viewed by 1759
Abstract
Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation [...] Read more.
Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation points, and the main producing areas were from south to north. Based on Landsat 8 satellite remote sensing images, the changes in NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and NDWI (Normalized Difference Water Index) were analyzed at different growth stages of winter wheat in 2020. Multiple regression analysis and Back Propagation (BP) neural network machine learning methods were used to establish prediction models for the harvest time of winter wheat at different growth stages. The results showed that the prediction model based on a BP neural network had high accuracy. The RMSE, MAE and MAPE of the training set and the test set were 0.531 and 0.5947, 0.3001 and 0.3104, 0.0114% and 0.0119%, respectively. The prediction model of winter wheat harvest date based on BP neural network was verified in the main winter wheat producing areas of Henan province in 2020 and 2021. The average errors were 1.67 days and 2.13 days, which were less than 3 days, meeting the needs for winter wheat production and harvest. The grain water content of winter wheat at harvest time calculated by the prediction model reached the grain water standard of the wheat combine harvester. Therefore, the prediction of the winter wheat harvest time can be realized based on multiple remote sensing indicators. Full article
Show Figures

Figure 1

Back to TopTop