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AgriEngineering
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Innovations, Engineering, Technologies and Best Practices for Ensuring Work Safety...
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Innovations, Engineering, Technologies and Best Practices for Ensuring Work Safety in Agriculture

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Agricultural Mechanization and Machinery".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 5393

Special Issue Editors

Dr. Marcello Biocca

E-Mail Website
Guest Editor
Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria—CREA, Centro di Ricerca In-Gegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, 00015 Monterotondo, Italy
Interests: agricultural engineering; safety, health and safety in agro-food systems; crop protection technology; mechanization in urban forestry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Massimo Cecchini

E-Mail Website
Guest Editor
Department of Agricultural and Forestry sciences (DAFNE), University of Tuscia, Via S. Camillo de Lellis, SNC, 01100 Viterbo, Italy
Interests: agricultural machinery; occupational safety and health; ergonomics; agriculture; energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue (SI) of AgriEngineering, which collects the latest research in the agricultural engineering sector related to safety, health and welfare in agriculture. Agricultural workers are exposed to a number of specific risks that lead to accidents and professional diseases, particularly related (but not only) to the use of machinery and equipment. In recent years, many innovative technologies and solutions have become available, and they can be conveniently applied to reduce these risks, maintaining the economic and environmental sustainability of crops. The SI explores the most important challenges faced by the agricultural engineering sector and offer innovative solutions to promote efficient agricultural practices and social sustainability.

Potential topics include a wide area of subjects, such as assistive technologies, work related musculo-skeletal disorders, work organisation, exposure to physical agents, microclimate safety, and vehicles stability and navigation research.

In this SI, we seek to demonstrate that adopting the latest technologies will allow producers to increase safety, reducing health and social costs and maintaining the productivity.

The SI will be a valuable resource for researchers, policymakers and stakeholders, inspiring them to work together to create a more sustainable future for agriculture.

The SI focuses primarily on original research papers across its whole scope, but also welcomes state-of-the-art review papers and first-hand case histories.

Dr. Marcello Biocca
Prof. Dr. Massimo Cecchini
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. 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

  • assistive technologies
  • safety for emerging robotics and autonomous agriculture
  • WMSDs work related musculo-skeletal disorders
  • work organisation
  • safety health and welfare in mechanization
  • innovative safe vehicles and machinery
  • noise, vibration, dust
  • environment and microclimate safety
  • ROPS and vehicles stability.

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

Published Papers (5 papers)

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Research

23 pages, 6190 KiB  
Article
Exposure to Noise from Agricultural Machinery: Risk Assessment of Agricultural Workers in Italy
by Valerio Di Stefano, Massimo Cecchini, Simone Riccioni, Giorgia Di Domenico and Leonardo Bianchini
AgriEngineering 2025, 7(3), 87; https://doi.org/10.3390/agriengineering7030087 - 19 Mar 2025
Viewed by 288
Abstract
Accidents and deaths at work are a persistent problem, with numbers still worrying. The agricultural and forestry sector is among the most exposed to work risks, with particular attention to noise risk from the use of agricultural machinery and operators. This study aims [...] Read more.
Accidents and deaths at work are a persistent problem, with numbers still worrying. The agricultural and forestry sector is among the most exposed to work risks, with particular attention to noise risk from the use of agricultural machinery and operators. This study aims to analyze the exposure to noise risk during use of wheeled and tracked tractors, with or without a cab, as well as other operating machines. The analysis takes into account the parameters Lpeak (peak sound pressure values), LAeq.T (time-weighted equivalent noise exposure levels) and LAS (maximum and minimum values weighted according to the Slow time constant) in order to assess the noise impact and define strategies for improving the safety and health of workers. This study demonstrates that in multiple cases, the regulatory thresholds for the examined variables are exceeded, regardless of the presence of a cabin. Specifically, Lpeak values approach 140 dB, dangerous to human health, while LAeq.T levels are close to or, in some instances, exceed 87 dB. It is also verified that agricultural and forestry operators who mainly use crawler tractors have greater and constant exposure to noise compared to those who use tractors with a cabin. Full article
(This article belongs to the Special Issue Innovations, Engineering, Technologies and Best Practices for Ensuring Work Safety in Agriculture)
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15 pages, 1695 KiB  
Article
Biofilter, Ventilation, and Bedding Effects on Air Quality in Swine Confinement Systems
by Hong-Lim Choi, Andi Febrisiantosa, Anriansyah Renggaman, Sartika Indah Amalia Sudiarto, Chan Nyeong Yun and Arumuganainar Suresh
AgriEngineering 2025, 7(3), 73; https://doi.org/10.3390/agriengineering7030073 - 7 Mar 2025
Viewed by 636
Abstract
This study evaluated housing designs and bedding systems to improve air quality in swine facilities, focusing on odor and particulate matter (PM) reduction. Three experimental animal house designs (M1, M2, M3) were tested: M1 used circulating airflow with negative pressure, M2 featured a [...] Read more.
This study evaluated housing designs and bedding systems to improve air quality in swine facilities, focusing on odor and particulate matter (PM) reduction. Three experimental animal house designs (M1, M2, M3) were tested: M1 used circulating airflow with negative pressure, M2 featured a plug flow air pattern with a perforated plastic bed, and M3 employed a sawdust bedding system with recirculating ventilation. Nine fattening swine were housed in each 12 m2 house over 110 days (6 May to 26 August 2018). Appropriate air samples were collected, and odorous compounds, volatile organic acids (VOA), PM, and bacterial concentrations measured. Results showed that M3 had the lowest ammonia (NH3) levels (5.9 ± 1.5 ppm) and undetectable hydrogen sulfide (H2S), while M1 recorded the highest NH3 (9.1 ± 2.2 ppm). VOA concentrations were significantly lower in M3 (75 ± 1.3 ppbv) compared to M1 (884 ± 15 ppbv) and M2 (605 ± 10.3 ppbv). PM10 levels were highest in M3 (312 ± 11 μg/m3) and lowest in M1 (115 ± 3 μg/m3), and thus bacterial counts were elevated in M3 (2117 ± 411 cfu/min), whereas M1 showed the lowest bacterial count of 1029 ± 297 cfu/min. The sawdust bedding system effectively reduced odorous compounds, highlighting its potential for odor control. However, higher PM levels in M3 emphasize the need to balance environmental management with animal welfare. These findings suggest that optimizing housing designs and bedding systems can enhance air quality in swine facilities while addressing sustainability and welfare concerns. Full article
(This article belongs to the Special Issue Innovations, Engineering, Technologies and Best Practices for Ensuring Work Safety in Agriculture)
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19 pages, 20092 KiB  
Article
Comparative Analysis of Vibration Impact on Operator Safety for Diesel and Electric Agricultural Tractors
by Teofil-Alin Oncescu, Ioan Catalin Persu, Stefan Bostina, Sorin Stefan Biris, Marius-Valentin Vilceleanu, Florin Nenciu, Mihai-Gabriel Matache and Daniela Tarnita
AgriEngineering 2025, 7(2), 40; https://doi.org/10.3390/agriengineering7020040 - 7 Feb 2025
Viewed by 937
Abstract
The present paper investigates the comparative impact of vibrations on operator safety for two diesel and electric agricultural tractors under real operating conditions. Vibrations were measured using four triaxial accelerometers installed at critical points, including the seat base, backrest, floor, and operator’s head. [...] Read more.
The present paper investigates the comparative impact of vibrations on operator safety for two diesel and electric agricultural tractors under real operating conditions. Vibrations were measured using four triaxial accelerometers installed at critical points, including the seat base, backrest, floor, and operator’s head. Tests were conducted on two comparable tractor models, a diesel New Holland TCE 50 and an electric prototype TE-0, across four types of terrains (concrete, grass, uneven agricultural road, and plowed land) and at two working speeds (5 km/h and 10 km/h). The root mean square (RMS) accelerations, seat-to-head transmissibility, and isolation efficiency were calculated in compliance with ISO 2631 standards to evaluate the effects on operator health and comfort. The results showed superior vibration isolation efficiency for the electric tractor, particularly within the critical frequency range of 4–12 Hz, where human health risks are most significant and a better isolation efficiency of 98%, significantly reducing operator exposure to harmful vibrations. These findings highlight the potential of electric tractors to improve operator comfort, safety, and long-term health in agricultural applications. Full article
(This article belongs to the Special Issue Innovations, Engineering, Technologies and Best Practices for Ensuring Work Safety in Agriculture)
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19 pages, 5492 KiB  
Article
Effects of Noise and Vibration Changes from Agricultural Machinery on Brain Stress Using EEG Measurement
by Seok-Joon Hwang and Ju-Seok Nam
AgriEngineering 2024, 6(4), 4248-4266; https://doi.org/10.3390/agriengineering6040239 - 12 Nov 2024
Cited by 1 | Viewed by 1035
Abstract
In this study, the agricultural work stress induced by the noise and vibration of some agricultural machinery was analyzed through electroencephalogram (EEG) measurements. The values of spectral edge frequency (SEF) 95%, relative gamma power (RGP), and EEG-based working index (EWI), utilized as stress [...] Read more.
In this study, the agricultural work stress induced by the noise and vibration of some agricultural machinery was analyzed through electroencephalogram (EEG) measurements. The values of spectral edge frequency (SEF) 95%, relative gamma power (RGP), and EEG-based working index (EWI), utilized as stress indicators, were derived by analyzing the EEG data collected. The EEG analysis revealed that agricultural work stress manifested when participants engaged in agricultural tasks following a period of rest. Additionally, the right prefrontal cortex was identified where the values of SEF95% and RGP increased concurrently with the rise in noise (61.42–88.39 dBA) and vibration (0.332–1.598 m/s2). This study’s results are expected to be utilized as foundational data to determine the agricultural work stress felt by farmers during work through EEG analysis in response to changes in noise and vibration. Full article
(This article belongs to the Special Issue Innovations, Engineering, Technologies and Best Practices for Ensuring Work Safety in Agriculture)
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19 pages, 6180 KiB  
Article
Human–Robot Interaction through Dynamic Movement Recognition for Agricultural Environments
by Vasileios Moysiadis, Lefteris Benos, George Karras, Dimitrios Kateris, Andrea Peruzzi, Remigio Berruto, Elpiniki Papageorgiou and Dionysis Bochtis
AgriEngineering 2024, 6(3), 2494-2512; https://doi.org/10.3390/agriengineering6030146 - 1 Aug 2024
Cited by 4 | Viewed by 1842
Abstract
In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific [...] Read more.
In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture “come” alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety. Full article
(This article belongs to the Special Issue Innovations, Engineering, Technologies and Best Practices for Ensuring Work Safety in Agriculture)
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