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Application of Sensors Technologies in Agricultural Engineering
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Application of Sensors Technologies in Agricultural Engineering

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 4303

Special Issue Editors

Prof. Dr. Sirio Cividino

E-Mail Website
Guest Editor
Department of Human Science and Quality of Life Promotion, Università Telematica San Raffaele Roma, Via di Val Cannuta 247, 00166 Rome, Italy
Interests: agriculture; sensor technologies; wine sector; design of wineries with a focus on energy efficiency and sensor integration; design of machinery and equipment for the agroforestry sector; application of wearable sensors for risk assessment in the agricultural and forestry sectors
Dr. Veronica Redaelli

E-Mail Website
Guest Editor
Department of Human Science and Quality of Life Promotion, Università Telematica San Raffaele Roma, 00166 Rome, Italy
Interests: infrared images; thermography; assessing animal welfare; veterinary medicine; zoocultures
Prof. Dr. Mauro Zaninelli

E-Mail Website
Guest Editor
Department of Human Science and Quality of Life Promotion, Università Telematica San Raffaele Roma, Via di Val Cannuta 247, 00166 Rome, Italy
Interests: development of electronic and computer technologies for agricultural applications; development of sensors and models for the livestock industry; automation of agricultural, agri-food and livestock sectors; agricultural machineries

Special Issue Information

Dear Colleagues,

The use of sensors in agricultural engineering is one of the most fascinating innovations in the modernization of agricultural practices. These technological devices, capable of collecting and analyzing data in real time, are revolutionizing the management of crops, water, soil, and overall environmental resources, enhancing the sustainability and efficiency of agriculture. In the realm of agricultural engineering, sensors enable a deeper understanding and more accurate control over the various factors that influence crop growth and resource management. The precision and reliability of the information gathered by sensors provide the opportunity to optimize agricultural practices, reduce waste, improve both the quantity and quality of production, and enhance workplace safety. This Special Issue aims to highlight the transformative impact of sensor technology in agricultural engineering, providing insights into current applications and future directions. In particular, this Special Issue focuses on the following main topics:

  • Sensor technologies for water management in agriculture.
  • Sensors for soil and crop health management.
  • Sensors to improve the quantity and quality of agricultural productions.
  • Sensor and precision agriculture.
  • Sensors, big data analysis, and AI to sustain agricultural decision making.
  • Sensors to increase the automation of agricultural operations.
  • Sensors for improving work processes and worker well-being, health, and safety in agricultural sectors.

Prof. Dr. Sirio Cividino
Dr. Veronica Redaelli
Prof. Dr. Mauro Zaninelli
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. Sensors 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 2600 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 technologies
  • quantity and quality of agri-food production
  • precision agriculture
  • automation
  • safety
  • big data and AI for agricultural decision making

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Published Papers (4 papers)

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Research

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20 pages, 67621 KB  
Article
Magnetic Induction Spectroscopy-Based Non-Contact Assessment of Avocado Fruit Condition
by Tianyang Lu, Adam D. Fletcher, Richard John Colgan and Michael D. O’Toole
Sensors 2025, 25(13), 4195; https://doi.org/10.3390/s25134195 - 5 Jul 2025
Viewed by 477
Abstract
This study demonstrates that the ripeness of avocado fruits can be analyzed using frequency-dependent electrical conductivity and permittivity through a non-invasive Magnetic Induction Spectroscopy (MIS) method. Utilizing an MIS system for conductivity and permittivity measurements of a large sample set ( [...] Read more.
This study demonstrates that the ripeness of avocado fruits can be analyzed using frequency-dependent electrical conductivity and permittivity through a non-invasive Magnetic Induction Spectroscopy (MIS) method. Utilizing an MIS system for conductivity and permittivity measurements of a large sample set (N=60) of avocado fruits across multiple frequencies from 100 kHz to 3 MHz enables clear observation of their dispersion behavior and the evolution of their spectra over ripening time in a completely non-contact manner. For the entire sample batch, the conductivity spectrum exhibits a general upward shift and spectral flattening over ripening time. To further quantify these features, normalized gradient analysis and equivalent circuit modeling were employed, and statistical analysis confirmed the correlations between electrical parameters and ripening stages. The trend characteristics of the normalized gradient parameter Py provide a basis for defining the three ripening stages within the 22-day period: early pre-ripe stage (0–5 days), ripe stage (5–15 days), and overripe stage (after 15 days). The equivalent circuit model, which is both physically interpretable and fitted to experimental data, revealed that the ripening process of avocado fruits is characterized by a weakening of capacitive structures and an increase in extracellular solution conductivity, suggesting changes in cellular integrity and extracellular composition, respectively. The results also highlight significant inter-sample variability, which is inherent to biological samples. To further investigate individual conductivity variation trends, Gaussian Mixture Model (GMM) clustering and Principal Component Analysis (PCA) was conducted for exploratory sample classification and visualization. Through this approach, the sample set was classified into three categories, each corresponding to distinct conductivity variation patterns. Full article
(This article belongs to the Special Issue Application of Sensors Technologies in Agricultural Engineering)
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Review

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27 pages, 3796 KB  
Review
A Review of Orchard Canopy Perception Technologies for Variable-Rate Spraying
by Yunfei Wang, Weidong Jia, Mingxiong Ou, Xuejun Wang and Xiang Dong
Sensors 2025, 25(16), 4898; https://doi.org/10.3390/s25164898 - 8 Aug 2025
Viewed by 470
Abstract
With the advancement of precision agriculture, variable-rate spraying (VRS) technology has demonstrated significant potential in enhancing pesticide utilization efficiency and promoting environmental sustainability, particularly in orchard applications. As a critical medium for pesticide transport, the dynamic structural characteristics of orchard canopies exert a [...] Read more.
With the advancement of precision agriculture, variable-rate spraying (VRS) technology has demonstrated significant potential in enhancing pesticide utilization efficiency and promoting environmental sustainability, particularly in orchard applications. As a critical medium for pesticide transport, the dynamic structural characteristics of orchard canopies exert a profound influence on spraying effectiveness. This review systematically summarizes recent progress in the dynamic perception and modeling of orchard canopies, with a particular focus on key sensing technologies such as LiDAR, Vision Sensor, multispectral/hyperspectral sensors, and point cloud processing techniques. Furthermore, it discusses the construction methodologies of static, quasi-dynamic, and fully dynamic canopy modeling frameworks. The integration of canopy sensing technologies into VRS systems is also analyzed, including their roles in spray path planning, nozzle control strategies, and precise droplet transport regulation. Finally, the review identifies key challenges—particularly the trade-offs between real-time performance, seasonal adaptability, and modeling accuracy—and outlines future research directions centered on multimodal perception, hybrid modeling approaches combining physics-based and data-driven methods, and intelligent control strategies. Full article
(This article belongs to the Special Issue Application of Sensors Technologies in Agricultural Engineering)
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30 pages, 8037 KB  
Review
A Review of Multiscale Interaction Mechanisms of Wind–Leaf–Droplet Systems in Orchard Spraying
by Yunfei Wang, Zhenlei Zhang, Ruohan Shi, Shiqun Dai, Weidong Jia, Mingxiong Ou, Xiang Dong and Mingde Yan
Sensors 2025, 25(15), 4729; https://doi.org/10.3390/s25154729 - 31 Jul 2025
Viewed by 412
Abstract
The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent [...] Read more.
The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems. Full article
(This article belongs to the Special Issue Application of Sensors Technologies in Agricultural Engineering)
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Other

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16 pages, 8192 KB  
Perspective
Embedding AI-Enabled Data Infrastructures for Sustainability in Agri-Food: Soft-Fruit and Brewery Use Case Perspectives
by Milan Markovic, Andy Li, Tewodros Alemu Ayall, Nicholas J. Watson, Alexander L. Bowler, Mel Woods, Peter Edwards, Rachael Ramsey, Matthew Beddows, Matthias Kuhnert and Georgios Leontidis
Sensors 2024, 24(22), 7327; https://doi.org/10.3390/s24227327 - 16 Nov 2024
Cited by 2 | Viewed by 2201
Abstract
The agri-food sector is undergoing a comprehensive transformation as it transitions towards net zero. To achieve this, fundamental changes and innovations are required, including changes in how food is produced and delivered to customers, new technologies, data and physical infrastructures, and algorithmic advancements. [...] Read more.
The agri-food sector is undergoing a comprehensive transformation as it transitions towards net zero. To achieve this, fundamental changes and innovations are required, including changes in how food is produced and delivered to customers, new technologies, data and physical infrastructures, and algorithmic advancements. In this paper, we explore the opportunities and challenges of deploying AI-based data infrastructures for sustainability in the agri-food sector by focusing on two case studies: soft-fruit production and brewery operations. We investigate the potential benefits of incorporating Internet of Things (IoT) sensors and AI technologies for improving the use of resources, reducing carbon footprints, and enhancing decision-making. We identify user engagement with new technologies as a key challenge, together with issues in data quality arising from environmental volatility, difficulties in generalising models, including those designed for carbon calculators, and socio-technical barriers to adoption. We highlight and advocate for user engagement, more granular availability of sensor, production, and emissions data, and more transparent carbon footprint calculations. Our proposed future directions include semantic data integration to enhance interoperability, the generation of synthetic data to overcome the lack of real-world farm data, and multi-objective optimisation systems to model the competing interests between yield and sustainability goals. In general, we argue that AI is not a silver bullet for net zero challenges in the agri-food industry, but at the same time, AI solutions, when appropriately designed and deployed, can be a useful tool when operating in synergy with other approaches. Full article
(This article belongs to the Special Issue Application of Sensors Technologies in Agricultural Engineering)
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