Multi-Sensor Data Fusion and Analytics for Predictive Quality Evaluation of Fresh Produce
A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Pre and Post-Harvest Engineering in Agriculture".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 21
Special Issue Editors
Interests: food science; food engineering; emerging technologies; smart sensors; artificial intelligence; food quality and control
Special Issues, Collections and Topics in MDPI journals
2. The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Interests: computer vision; artificial intelligence; machine learning; spectroscopy & hyperspectral imaging; spectroscopic software design; image reconstruction; remote sensing; NLP & LLM; bioinformatics
Special Issue Information
Dear Colleagues,
The quality and safety of agricultural products have become major challenges across the global food supply chain. Providing reliable and rapid quality assessment is essential to meet regulatory requirements and consumer expectations. Sensors, including optical systems (RGB, near-infrared, and hyperspectral imaging), physicochemical and electrochemical sensors, as well as olfactory (e-nose) and gustatory (e-tongue) devices, have emerged as powerful tools for data-driven agriculture. These technologies provide fast, accurate, non-destructive, and scalable solutions for characterizing the physical, chemical, and biological properties of agricultural commodities.
Recent advancements in machine learning (ML), deep learning (DL), and broader data-analysis frameworks have significantly enhanced sensor-based systems. These artificial intelligence algorithms have accelerated progress across many sensing domains, including e-noses, e-tongues, and spectroscopic and optical devices—many of which are increasingly integrated with computer vision methods. Together, these advances have enhanced sensing mechanisms and introduced sophisticated analytical approaches that are transforming how agricultural data is captured, interpreted, and applied in real-world field conditions for predictive quality assessment.
This Special Issue aims to present recent innovations in sensors and data-driven analytical methodologies for assessing the quality of fresh agricultural products. The proposal aligns with the journal’s scope by integrating engineering, agricultural technology, and intelligent systems applied to real-world problems in agriculture.
We welcome contributions that explore sensor design, hardware optimization, new signal-processing approaches, ML/DL model development, data-fusion strategies, and field applications. The scope is intentionally inclusive, yet focused, to encourage both methodological advances and applied studies.
In this Special Issue, original research articles and review papers are welcome. Research areas may include, but are not limited to, the following:
- Sensor design, development, and hardware optimization;
- Optical, spectroscopic, electrochemical, gustatory, and olfactory sensing systems;
- Signal preprocessing and feature-extraction methods;
- Machine learning and deep learning models for agricultural quality assessment;
- Field validation, industrial applications, and intelligent automation;
- Data fusion, multimodal sensing, and predictive analytics;
- Real-time decision-support systems.
We look forward to receiving your contributions.
Dr. Marcus Vinicius da Silva Ferreira
Dr. Toukir Ahmed
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 250 words) can be sent to the Editorial Office for assessment.
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. AgriEngineering 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 1600 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
- sensor design
- optical sensors
- electrochemical sensing
- electronic nose/tongue
- signal preprocessing
- feature extraction
- machine learning
- deep learning
- field validation
- data fusion
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