Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3835

Special Issue Editors


E-Mail Website
Guest Editor
National Engineering Research Center of Biomaterials, Nanjing Forestry University, Nanjing 210037, China
Interests: artificial intelligence; machine vision; robotics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China
Interests: pattern recognition and intelligent systems; artificial intelligence; data mining and big data analytics

E-Mail Website
Guest Editor
School of Science, Jiangsu Ocean University, Lianyungang 222005, China
Interests: spectral detection; intelligent manufacturing; intelligent decision-making systems

Special Issue Information

Dear Colleagues,

The rise in artificial intelligence (AI) has revolutionized data analytics. This Special Issue aims to present cutting-edge research and methodologies that leverage AI for data-driven decision-making.

We welcome original research, comprehensive reviews, and innovative methodologies that demonstrate how AI can address complex domain-specific challenges and facilitate practical data-driven solutions. Submissions may encompass both theoretical contributions and empirical studies, emphasizing robust model architectures, efficient training strategies, and interpretable results for informed decision-making.

We welcome contributions that cover a wide range of topics, including, but not limited to, the following:

  • AI architectures for object detection and classification;
  • AI techniques for anomaly recognition;
  • Intelligent monitoring systems;
  • Resource optimization for responsible AI development.

We look forward to receiving contributions that push the boundaries of AI-based analytics and bridge the gap between state-of-the-art research and practical decision-making.

Dr. Xiaojun Jin
Prof. Dr. Jian Zhang
Prof. Dr. Dong-Qing Yuan
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

  • data analytics
  • decision-making
  • anomaly recognition
  • Intelligent monitoring systems
  • artificial intelligence

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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

Review

19 pages, 441 KB  
Review
Recent Advances and Applications of Nondestructive Testing in Agricultural Products: A Review
by Mian Li, Honglian Yin, Fei Gu, Yanjun Duan, Wenxu Zhuang, Kang Han and Xiaojun Jin
Processes 2025, 13(9), 2674; https://doi.org/10.3390/pr13092674 - 22 Aug 2025
Viewed by 421
Abstract
With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming [...] Read more.
With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming the products. These inherent advantages have promoted the increasing adoption of NDT technologies in agriculture. Meanwhile, rising quality standards for agricultural products have intensified the demand for more efficient and reliable detection methods, accelerating the replacement of conventional techniques by advanced NDT approaches. Nevertheless, selecting the most appropriate NDT method for a given agricultural inspection task remains challenging, due to the wide diversity in product structures, compositions, and inspection requirements. To address this challenge, this paper presents a review of recent advancements and applications of several widely adopted NDT techniques, including computer vision, near-infrared spectroscopy, hyperspectral imaging, computed tomography, and electronic noses, focusing specifically on their application in agricultural product evaluation. Furthermore, the strengths and limitations of each technology are discussed comprehensively, quantitative performance indicators and adoption trends are summarized, and practical recommendations are provided for selecting suitable NDT techniques according to various agricultural inspection tasks. By highlighting both technical progress and persisting challenges, this review provides actionable theoretical and technical guidance, aiming to support researchers and practitioners in advancing the effective and sustainable application of cutting-edge NDT methods in agriculture. Full article
Show Figures

Figure 1

31 pages, 3016 KB  
Review
Image Recognition Technology in Smart Agriculture: A Review of Current Applications Challenges and Future Prospects
by Chunxia Jiang, Kangshu Miao, Zhichao Hu, Fengwei Gu and Kechuan Yi
Processes 2025, 13(5), 1402; https://doi.org/10.3390/pr13051402 - 4 May 2025
Viewed by 2973
Abstract
The implementation of image recognition technology can significantly enhance the levels of automation and intelligence in smart agriculture. However, most researchers focused on its applications in medical imaging, industry, and transportation, while fewer focused on smart agriculture. Based on this, this study aims [...] Read more.
The implementation of image recognition technology can significantly enhance the levels of automation and intelligence in smart agriculture. However, most researchers focused on its applications in medical imaging, industry, and transportation, while fewer focused on smart agriculture. Based on this, this study aims to contribute to the comprehensive understanding of the application of image recognition technology in smart agriculture by investigating the scientific literature related to this technology in the last few years. We discussed and analyzed the applications of plant disease and pest detection, crop species identification, crop yield prediction, and quality assessment. Then, we made a brief introduction to its applications in soil testing and nutrient management, as well as in agricultural machinery operation quality assessment and agricultural product grading. At last, the challenges and the emerging trends of image recognition technology were summarized. The results indicated that the models used in image recognition technology face challenges such as limited generalization, real-time processing, and insufficient dataset diversity. Transfer learning and green Artificial Intelligence (AI) offer promising solutions to these issues by reducing the reliance on large datasets and minimizing computational resource consumption. Advanced technologies like transformers further enhance the adaptability and accuracy of image recognition in smart agriculture. This comprehensive review provides valuable information on the current state of image recognition technology in smart agriculture and prospective future opportunities. Full article
Show Figures

Figure 1

Back to TopTop