Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach
Occupational Health and Safety (OHS) in agriculture is a critical concern worldwide, with self-propelled machinery accidents, particularly tip/roll-overs, being a leading cause of injuries and fatalities. In such a context, while great attention has been paid to machinery safety improvement, a major...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/5/377 |
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| author | Daniele Puri Leonardo Vita Davide Gattamelata Valerio Tulliani |
| author_facet | Daniele Puri Leonardo Vita Davide Gattamelata Valerio Tulliani |
| author_sort | Daniele Puri |
| collection | DOAJ |
| description | Occupational Health and Safety (OHS) in agriculture is a critical concern worldwide, with self-propelled machinery accidents, particularly tip/roll-overs, being a leading cause of injuries and fatalities. In such a context, while great attention has been paid to machinery safety improvement, a major challenge is the lack of studies addressing the analysis of the work environment to provide farmers with precise information on field slope steepness. This information, merged with an awareness of machinery performance, such as tilt angles, can facilitate farmers in making decisions about machinery operations in hilly and mountainous areas. To address this gap, the Italian Compensation Authority (INAIL) launched a research programme to integrate georeferenced slope data with the tilt angle specifications of common self-propelled machinery, following EN ISO 16231-2:2015 standards. This study presents the first results of this research project, which was focused on vineyards in the alpine region of the Autonomous Province of Trento, where terrestrial LiDAR technology was used to analyze slope steepness. The findings aim to provide practical guidelines for safer machinery operation, benefiting farmers, risk assessors, and manufacturers. By enhancing awareness of tip/roll-over risks and promoting informed decision-making, this research aims to contribute to improving OHS in agriculture, particularly in challenging terrains. |
| format | Article |
| id | doaj-art-998fefe9179742c18bf82a032dc09795 |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-998fefe9179742c18bf82a032dc097952025-08-20T03:14:43ZengMDPI AGMachines2075-17022025-04-0113537710.3390/machines13050377Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based ApproachDaniele Puri0Leonardo Vita1Davide Gattamelata2Valerio Tulliani3National Institute for Insurance Against Accidents at Work (INAIL), Via Fontana Candida 1, Monte Porzio Catone, 00078 Rome, ItalyNational Institute for Insurance Against Accidents at Work (INAIL), Via Fontana Candida 1, Monte Porzio Catone, 00078 Rome, ItalyNational Institute for Insurance Against Accidents at Work (INAIL), Via Fontana Candida 1, Monte Porzio Catone, 00078 Rome, ItalyIndependent Researcher, 00100 Rome, ItalyOccupational Health and Safety (OHS) in agriculture is a critical concern worldwide, with self-propelled machinery accidents, particularly tip/roll-overs, being a leading cause of injuries and fatalities. In such a context, while great attention has been paid to machinery safety improvement, a major challenge is the lack of studies addressing the analysis of the work environment to provide farmers with precise information on field slope steepness. This information, merged with an awareness of machinery performance, such as tilt angles, can facilitate farmers in making decisions about machinery operations in hilly and mountainous areas. To address this gap, the Italian Compensation Authority (INAIL) launched a research programme to integrate georeferenced slope data with the tilt angle specifications of common self-propelled machinery, following EN ISO 16231-2:2015 standards. This study presents the first results of this research project, which was focused on vineyards in the alpine region of the Autonomous Province of Trento, where terrestrial LiDAR technology was used to analyze slope steepness. The findings aim to provide practical guidelines for safer machinery operation, benefiting farmers, risk assessors, and manufacturers. By enhancing awareness of tip/roll-over risks and promoting informed decision-making, this research aims to contribute to improving OHS in agriculture, particularly in challenging terrains.https://www.mdpi.com/2075-1702/13/5/377roll-over/tip-over riskself-propelled machinesstatic stabilitydigital terrain model (DTM)geographic information system (GIS)farmer behaviour |
| spellingShingle | Daniele Puri Leonardo Vita Davide Gattamelata Valerio Tulliani Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach Machines roll-over/tip-over risk self-propelled machines static stability digital terrain model (DTM) geographic information system (GIS) farmer behaviour |
| title | Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach |
| title_full | Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach |
| title_fullStr | Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach |
| title_full_unstemmed | Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach |
| title_short | Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach |
| title_sort | roll tip over risk analysis of agricultural self propelled machines using airborne lidar data gis based approach |
| topic | roll-over/tip-over risk self-propelled machines static stability digital terrain model (DTM) geographic information system (GIS) farmer behaviour |
| url | https://www.mdpi.com/2075-1702/13/5/377 |
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