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Innovative Engineering Technologies for the Agri-Food Sector, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 1132

Editors


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Guest Editor
Department of Soil, Plant and Food Science (DISSPA), University of Bari Aldo Moro, Via Amendola 165/a, 70126 Bari, Italy
Interests: food plant; remote sensing; robotic
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Soil, Plant and Food Science (DISSPA), University of Bari Aldo Moro, Via Amendola 165/a, 70126 Bari, Italy
Interests: pre- and post-harvest processing; yield monitoring; energy consumption monitoring; food plant
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Soil, Plant and Food Science (DISSPA), University of Bari Aldo Moro, Via Amendola 165/a, 70126 Bari, Italy
Interests: pulsed electric field; microwaves; ultrasound; smart agricultural
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The agri-food sector requires innovative processing, which is the goal of continuous research studies in order to make this sector competitive and sustainable from both an economic and environmental point of view, as well as being in accordance with global policies.

This Special Issue welcomes scientists to share their experimental and theoretical research, alongside comprehensive schematics, source codes, and mechanical design models. This facilitates the swift and widespread dissemination of cutting-edge technologies emerging in agricultural, environmental, food processing, and industrial plant sectors for food production.

Among the main objectives of the articles are the following:

  • The innovative and sustainable production of agricultural products, including smart farming, remote sensing, automation and robotics in agriculture, innovative farming techniques;
  • The innovative and sustainable management of post-harvest agricultural products;
  • Innovative and sustainable processes for food production;
  • Food preservation with sustainable and innovative processes, including pest management;
  • Valorisation of process waste for the production of new products or for energy production;
  • New agricultural and food production processes with the aim of reducing energy and water consumption.

Prof. Dr. Alessandro Leone
Prof. Dr. Antonia Tamborrino
Dr. Antonio Berardi
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • competitive and sustainable technology
  • innovative food process
  • environmental sustainability
  • agri-food products
  • smart farming
  • remote sensing
  • automation and robotics in agriculture
  • pre- and post-harvest processing
  • pest management
  • waste product valorization
  • physical and biological processes
  • food safety
  • food quality
  • circular economy
  • renewable energy

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Published Papers (1 paper)

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Review

41 pages, 4043 KB  
Review
Emerging Technologies for Soil Evaluation Using Spectrometry Sensing, Internet of Things and Machine Learning
by Florin Nenciu, Mihai Gabriel Matache, Iuliana Gageanu, Ioan Catalin Persu, Florin Bogdan Marin and Iulian Florin Voicea
Appl. Sci. 2026, 16(9), 4559; https://doi.org/10.3390/app16094559 - 6 May 2026
Viewed by 816
Abstract
The transition from conventional laboratory-based soil analysis to real-time, data-driven evaluation has become essential for advancing precision agriculture and ensuring sustainable resource management. This review provides a comprehensive and structured synthesis of emerging technologies for soil evaluation, focusing on the integration of spectrometric [...] Read more.
The transition from conventional laboratory-based soil analysis to real-time, data-driven evaluation has become essential for advancing precision agriculture and ensuring sustainable resource management. This review provides a comprehensive and structured synthesis of emerging technologies for soil evaluation, focusing on the integration of spectrometric sensing, Internet of Things (IoT) systems, and machine learning approaches. A systematic analysis of peer-reviewed studies published between 2012 and 2026 was conducted to assess the performance of these technologies in terms of accuracy, robustness, and scalability under variable environmental conditions. Spectrometry techniques, including visible–near-infrared and mid-infrared sensing, enable rapid and non-destructive estimation of soil chemical properties, while IoT-based sensor networks facilitate continuous in situ monitoring of key parameters such as moisture, pH, and nutrient content. Machine learning models further enhance soil assessment by enabling predictive analytics, data fusion, and high-resolution mapping. Despite their significant potential, challenges related to data quality, model transferability, sensor calibration, and implementation costs remain critical barriers to large-scale adoption. The review highlights the need for standardized evaluation frameworks, improved multimodal data integration, and increased focus on model interpretability and real-world applicability. Overall, the synergistic use of these technologies supports more efficient input management, reduces environmental impact, and contributes to the development of resilient and sustainable agricultural systems. Full article
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