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Article

Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach

by
Daniele Puri
1,
Leonardo Vita
1,*,
Davide Gattamelata
1 and
Valerio Tulliani
2
1
National Institute for Insurance Against Accidents at Work (INAIL), Via Fontana Candida 1, Monte Porzio Catone, 00078 Rome, Italy
2
Independent Researcher, 00100 Rome, Italy
*
Author to whom correspondence should be addressed.
Machines 2025, 13(5), 377; https://doi.org/10.3390/machines13050377
Submission received: 25 March 2025 / Revised: 28 April 2025 / Accepted: 29 April 2025 / Published: 30 April 2025
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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Abstract

:
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.

1. Introduction

Occupational Health and Safety (OHS) in agriculture represents a significant global concern, despite increasingly stringent regulations in this sector. Notably, the majority of accidents are linked to the use of self-propelled machinery, such as tractors and harvesters. The improper operation of this equipment is responsible for numerous serious and fatal accidents in many countries [1,2,3,4]. This is especially relevant regarding the risk of roll-over incidents [5,6,7].
In Italy, although recent years have seen a decline in accidents, primarily due to reduced operations during the COVID-19 pandemic, the number of injuries and fatalities remains high compared to other sectors [8,9]. According to data collected by the Italian Compensation Authority (INAIL), in the five-year period of 2018–2022, deaths averaged 150 per year, with a peak of 171 in 2019 and a minimum of 137 in 2022, where the leading cause of death was the total or partial loss of control of the means of transport or moving equipment [10]. More specifically, the latter is the cause of more than 50% of fatal accidents that occurred in 2022 as per the concluded investigations carried out by INAIL. Similar statistical incidences were observed in other countries [11,12,13,14], and in the European Union, agriculture represents one of the most impacted economic activities in terms of the number of fatal work accidents [15]. The causes of this phenomenon can be attributed to different roots [16,17]. First of all, the nature of agricultural activities, which are characterized by seasonality, outdoor work, overtime, etc., can be considered a risk factor that distinguishes them from other working contexts [18]. Further, the features of agricultural companies, which, in Italy, are mainly made of small-sized or family enterprises and part-time workers [19,20], should be considered. This aspect can be related to a lower level of safety education and training of farmers, leading to a low-risk perception and the tendency to neglect safety regulations and procedures [21,22]. When it comes to machinery safety, numerous studies have brought to light that all of the above factors contribute to the unsafe use of work equipment and, in particular, to that of self-propelled machinery, such as tractors and harvesters [23,24].
Another aspect that should be considered is related to the morphology of the territory: in Italy, only 23% of the territory is flat, while about 42% of the territory is made up of hills, and the remaining 35% is mountainous [25]. These features have a negative influence on agricultural activities as they make it difficult to use self-propelled equipment for fieldwork and the transport of products, leading to a higher probability of machinery roll-over events [26]. As noted by Facchinetti et al. [16], there is a strict correlation between the cases of tractor roll-over accidents and the territory where they happened: actually, the higher frequency of this type of accident occurred in mountainous and hilly areas, i.e., work environments characterized by slopes. Indeed, the presence of slopes causing an inadequate dynamic stability condition is considered the most significant factor contributing to overturning [27,28]. The majority of interventions aimed at dealing with this problem are focused on passive protection, i.e., the use of roll-over-protective structures (ROPSs), which, depending on the case, can be fixed or foldable/telescopic structures [29,30,31]. The use of ROPSs (as standalone structures or integrated into the machinery cabin) together with seatbelts can certainly help mitigate the consequences of accidents [32,33], but does not contribute to preventing roll-overs. To reduce the probability of occurrence, recent research has proposed proactive technical solutions, i.e., control systems that alert the driver when he/she is in a dangerous situation leading to the loss of stability or control loss of the vehicle [34]. For example, Casazza et al. [35] proposed a driver-assistance device that can signal critical stability conditions. Similarly, other researchers have developed alert systems in case of a loss of stability [36,37].
A third proposal to deal with this problem is related to farmers’ information and training initiatives. This preventive approach was investigated, mainly focusing on two different issues. On the one hand, campaigns were promoted to spread the proper use of ROPSs (and seatbelts) and their use in old machinery (i.e., retrofitting initiatives) [38,39]. On the other hand, the proper use of machinery and the comprehension of safety instructions were also investigated to reduce farmers’ risk-taking behaviour and attitudes [40]. As observed by O’ Connor et al. [20], the lack of safety information is particularly significant for small-sized companies, which pose a regulatory challenge because assessing their occupational safety and health (OSH) standards on a regular basis demands significant resources. Moreover, it must be noted that although environmental factors affecting tractor safety (e.g., slopes, poor ground conditions, wet surfaces, and obstacles) increase the risk of roll-overs, most farmers tend to underestimate the risks posed by slopes, often believing their tractors can handle them [41]. This suggests a lack of awareness or a wrong perception that the risks are minimal, based on poor information and training [42]. To improve this situation, Qi et al. [43] underlined that it is crucial to develop and enhance national and community information systems to inform farmers about natural hazards, with the goal of anticipating potential risks in farming activities. More specifically, safety information should be designed for specific areas to enhance awareness of potential risks in agriculture [44]. However, a few studies addressed this issue. Vigoroso et al. [45] examined the understanding of signals indicating hazardous slopes. Their study revealed that while farmers adopt protective behaviours when properly informed, they often struggle to process the information provided in machinery user manuals and road signs in practice.
In particular, to the authors’ knowledge, there is a scarcity of research merging information on the slope steepness of fields with the characteristics of self-propelled machinery, i.e., the maximum slope angle at which the machinery can be used safely. Actually, different from the roads where signals can indicate the steepness, farmers have no precise information on the field slopes (e.g., a vineyard) where they have to carry out field works. Therefore, although they should be aware of the maximum operating angle of their machinery as specified by the manufacturer, they lack practical reference information when working in mountainous or hilly terrain. As stressed by Görücü et al. [46], farmers must accurately perceive tilt angles to prevent overturns, but research shows they struggle due to the complexity of factors involved and the lack of information provided. Recognizing potential instability is crucial for corrective action, yet sensing roll-over risk is challenging, especially with attached implements [47].
Following these research hints, the Italian Compensation Authority (INAIL) promoted a research programme aimed at providing specific information and thus training material on the characteristics of the slope steepness of agricultural fields in mountainous and hilly areas in Italy. These data were merged with information related to tilt angles provided by manufacturers of the most common self-propelled machinery, which is defined in accordance with the EN ISO 16231-2:2015 standard [48]. The final goal of this analysis is to provide practical guidelines for the safe use of self-propelled machinery for each specific area of a certain territory and made at the disposal of farmers operating in that area. This allows farmers to recognize the risks associated with the field’s slope steepness, enabling them to make informed decisions about which equipment can be used safely and which should be avoided. It also helps them determine the appropriate maneuvers to perform and those that should be avoided to prevent roll-overs.
The current study presents the first phase of this project, which regarded the alpine region of the Autonomous Province of Trento (PAT) in the northern part of Italy, where vineyard cultivations were investigated and data provided by terrestrial LiDAR technology [49] allowed for 3D georeferencing and slope steepness definition for cultivated areas. Accordingly, this study addresses the absence of technical references to slope steepness for field works in mountainous and hilly areas, offering guidance not only to users and entrepreneurs conducting risk assessments of their equipment, but also to machinery manufacturers, which can provide more specific equipment. Occupational health and safety (OHS) considerations must be approached from both perspectives, focusing on developing effective strategies and tools to minimize accidents and injuries related to hazardous machinery. This aligns with Kogler et al. [6], who emphasized the importance of providing informational resources, such as guidelines, to enhance farmers’ safety. Consequently, the research presented in this paper can contribute to expanding knowledge on the safe selection and use of self-propelled machinery in mountainous and hilly areas. The remainder of the paper is organized as follows: in the next sections, the research approach is described. Section 3 proposes the outcomes of the case study, while in Section 4, the achieved results are discussed. Section 5 concludes this paper by addressing further work.

2. Materials and Methods

The study aimed at combining data from the LiDAR system owned by the Autonomous Province of Trento (PAT) [49] and those related to tilt angles for self-propelled machinery provided by the EN ISO 16231-2 standard [48] in order to classify and categorize cultivated areas based on roll-over risk. Moreover, details of the scheme followed are illustrated in Figure 1.
The sample area is located in the northern part of Italy: as illustrated in Figure 2, the Autonomous Province of Trento (PAT) is an alpine region covering an area of 6210 km2. The region’s morphology features a diverse landscape, with approximately 70% of the total area situated above 1000 m above sea level (a.s.l.) and an average elevation of 1400 m a.s.l. The land use in the region reflects a typical mountain landscape, with forests—predominantly coniferous stands—covering approximately 70% of the total area. Rocky outcrops and bare ground account for 11.5% of the territory, while urbanized and agricultural areas are primarily concentrated in the valley floors, occupying around 19% of the province. Table 1 summarizes main agricultural activities in terms of: (1) utilized agricultural area (UAA), which takes into account the sum of the farm areas intended for agricultural production; (2) the number of enterprises involved in agricultural activities (including forestry), which are mainly small-sized and family-run companies [50].
Due to the relevance of viticulture, this study focused on the characterization of vineyards.

2.1. Slope Steepness of Cultivated Areas

LiDAR data show great potential for agricultural applications, as demonstrated in the literature [51,52,53].
The Autonomous Province of Trento is covered by an HR-DTM (High-Resolution Digital Terrain Model) derived from airborne LiDAR (Light or Laser Detection and Ranging) data, which is freely available for download [49]. LIDAR is an active remote sensing technique used for topographic surveys by equipping an aerial vehicle with a laser scanner that captures data at extremely high speeds and resolutions. This system generates digital terrain models (DTMs) and digital surface models (DSMs) with a geometric resolution of 1 × 1 m. Specifically, the mapping process of the entire PAT was performed over different flights that occurred from 2013 to 2015, and subsequently in 2018, to remedy the lack of ground points in the most vegetated areas, and 46 flights were carried out. The 0.5 m × 0.5 m mesh grating was derived by the interpolation of the original data detected using the sensor (first pulse only), and is available in ASC and XYZ formats. The processed survey points have an altimetric accuracy corresponding to +/−25 cm with a confidence level of 95% (2 sigma) and a planimetric accuracy of +/−50 cm. The HR-DTM is composed of two distinct datasets, consisting of 25,201 and 400 blocks, respectively, each with a side length of 0.5 km and a pixel size of 0.5 m. The LIDAR data were merged with those related to the land use provided by the reports of PAT to obtain information related to the morphological characteristics of the cultivated areas.

2.2. Areas Classification

Through the use of ESRI ArcGIS Pro 3.3.0 software [54], the next step consisted in the classification of the different areas depending on two parameters: the type of cultivation and the slope steepness. This allowed us to generate a slope dataset.

2.3. Areas Categorization

Based on the information previously collected, each area was categorized based on the type of machinery that can be used. Specifically, in this phase, a new classification of the slope dataset was performed with respect to the level of roll-over risk for each type of self-propelled machinery using the Reference Stability Static Angle (RSSA) Machine Operating Slope (MOS) values provided by the EN ISO 16231-2 standard. In Table 2, the values of MOS and RSSA for the most common self-propelled agricultural machinery are shown.
It must be noted that RSSA is computed by multiplying the MOS values per safety factor, which allows engineers to consider a safety margin. In accordance with the EN ISO 16231-2 standard, this factor is equal to 1.5.
These values (expressed in percentages) are provided for both lateral and front roll-over/tip-over by the EN ISO 16231-2 standard. To be more precise, EN ISO 16231-2 defines MOS as the “value indicating, for each type of self-propelled machine and each direction, the maximum slope on which the machine is intended to work according to good agricultural practice”, while the RSSA “provides the calculated slope on which the machine is required to be stable” [48].

2.4. Risk Assessment Data

The final step of the process consists of the definition of risk assessment data, which can be distinguished into two main outputs:
  • Zonal histograms were developed on the vector parcels to associate the amount of the area potentially exposed to roll-over/tip-over risk when using specific agricultural machines according to the cultivation type of each area.
  • Safety procedures were developed for the proper use of machinery in each area, including information concerning allowed maneuvers, allowed implements, etc., In other words, combining data provided by the EN ISO 16231-2 standard and the steepness of the field, it is possible to identify for each type of machinery the slope areas where the machine can work according to good agricultural practice in any direction, those where only certain maneuvers can be carried out safely, and those where the machine cannot be used.

3. Results

In this section, the experimental results are summarized as follows: in the first part, information related to the categorization procedure is provided, while in the second part of the section, the outcomes of a specific case study concerning an area dedicated to viticulture are reported.

3.1. Categorization

In the first phase of the analysis, data related to the morphology of the territory were acquired. In Figure 3, mountainous and hilly areas of the region were identified.
Then, the slope classification was carried out: as shown in Figure 4, the lighter areas are those that have a lower slope (the white areas are those that have a slope less than or equal to 3%), while for greater slopes, the colour progressively darkens.
Then, using the MOS values provided by EN ISO 16231-2, the different areas of the territory were categorized into three different categories:
  • C1 (where there is no roll-over risk due to the slope steepness);
  • C2 (where there is the risk of lateral roll-over);
  • C3 (where there is a risk of both lateral and front/rear roll-over).
These data were merged with the different types of self-propelled machines reported in the EN ISO 16231-2: in Figure 5, a synthesis of this elaboration is reported considering the total surfaces expressed in hectares (e.g., for Type 1 machinery (Combine harvester without slope compensation system), the majority of the territory (525.735 hectares) belong to category C3, i.e., the most dangerous one).
To put this information into practice, risk maps were derived as shown in Figure 6 and Figure 7 which represent the false colour diagrams for the combine harvester without a slope compensation system (type 1 machine) and for the combine harvester with a body levelling system (type 2 machine), respectively.
Comparing the maps shown for type 1 and type 2 machines, it emerges that the roll-over risk is much higher for type 1: the red and yellow surfaces are significantly more widespread for type 1 machines than for type 2, while in the latter case the green areas are more widespread.
Thus, the final step of the categorization consisted in adding information on the type of cultivation by developing risk maps that consider three variables for each type of machinery: MOS, slope steepness, and cultivation type. In other words, using ArcGIS software, the information related to cultivation type and steepness is merged to create specific roll/tip-over risk maps.

3.2. Case Study

To delve deeper into this aspect of the study, a specific analysis was carried out that took into consideration the grape harvester and slope of vineyards, focusing on a restricted area delimited by the Municipality of Trento: in Figure 8, the positioning of this area (identified by the white boundary) into the PAT region (identified by the red boundary) is shown.
In this area, grape cultivation is very diffused, covering 1262.4916 hectares, i.e., almost 50% of the whole cultivated area of the municipality of Trento. In Figure 9, the map of the vineyards’ location is represented: purple-coloured areas represent the vineyards, while the white border indicates the limits of the municipality.
For this case study, the following machinery was chosen: (1) a grape harvester without a body levelling system (NO_BLS); and (2) a grape harvester with a body levelling system (W_BLS). The above-mentioned machines are most likely used in areas intended for grape farming. The total area dedicated to the cultivation of grapes was then analyzed to verify the slope of each field: these data were merged with the information related to MOS and RAAS of the machinery to determine the roll/tip-over risk. In Figure 10, data related to the risk level areas are reported for both kinds of machinery (NO_BLS and W_BLS).
Regarding grape harvesters equipped with an automatic levelling system (BLS), one can see that 1010 hectares represents the overall area where there is no roll-over risk due to the slope steepness. This also means that the harvester equipped with a body levelling system is not stable enough for safely working on 15% of the area, which is classified as C.3.
In more detail, the whole territory was categorized by taking into account the single plots of the municipality, as shown in Figure 11 (risk categories when using the harvester without a body levelling system (NO_BLS)) and Figure 12 (risk categories when using the harvester without a body levelling system (W_BLS)).
Looking at these figures, one can note how in mountainous and hilly areas, the slope rapidly changes, and when using machinery not equipped with the levelling system, the risk level changes in a few metres. (Figure 11). Furthermore, working in the same parcel with different versions of the same type of machinery implies a significant variation in the roll/tip-over risk.
Thus, the proper choice of work equipment is paramount, as well as working procedures: for instance, in yellow areas of Figure 11, which indicate risk category C.2, horizontal maneuvers (such as turning around) should be avoided. Alternatively, in the red zones, which indicate risk category C.3, harvesting should only be carried out manually.

4. Discussion

Recent advancements in machinery safety norms and standards have underscored an increasing awareness of occupational health and safety (OHS) from the perspectives of both equipment manufacturers and end users. These developments reflect a growing recognition of the hazardous nature of using agricultural self-propelled machinery when workers are frequently exposed to risks such as machinery roll/tip-overs [55]. Thus, significant challenges remain in the practical implementation of safety standards and working procedures [56]. This is particularly true concerning research integrating field slope steepness data with the operational characteristics of self-propelled agricultural machinery. Thus, a general contribution of this study relies on dealing with this gap in knowledge by presenting a methodology to provide farmers with specific information on the maximum safe working slope angles related to the parcels they farm depending on the machinery they use. Although this study presents the first results of a more thorough project, some practical findings can be outlined.
First, to effectively assess the roll-over risk of self-propelled agricultural machines, the proposed approach proves to be a valuable tool to enable the prediction of slope gradients in specific areas concerning plantation layouts and correlate them with the static stability angle of the agricultural machinery used on that terrain. By doing so, it becomes possible to generate a slope map that is tailored to both the specific topography and the types of machines operating in that environment.
In the literature, several studies have proposed advanced solutions integrating real-time detection mechanisms to enhance situational awareness and facilitate prompt communication of emergencies. For example, Bertacchini et al. [57] focused on advancements in driver assistance technologies: their research highlighted solutions that alert users in hazardous situations, such as instances where the vehicle experiences a loss of stability. Similarly, Liu and Koc [58] developed a detection and emergency notification system. These systems play a crucial role in accident prevention by providing timely notifications that enable drivers to take corrective actions before a critical situation escalates.
However, one of the key advantages of the proposed approach is its ability to provide a preliminary evaluation of whether a particular machine can safely operate on a given slope before reaching the area. This proactive assessment minimizes the risk of accidents by preventing machines from entering hazardous inclinations where stability could be compromised, thus avoiding risky situations.
Consequently, this tool offers a significant added value in enhancing safety measures, reducing the likelihood of roll/tip-overs, and preventing potential injuries to operators.
By integrating this predictive capability into agricultural planning and machine operation, farmers and agricultural professionals can make more informed decisions, ultimately improving both safety and efficiency in field operations. This output is in line with Cutini et al. [59], who pointed out that when using a self-propelled fruit harvester (SPFH) equipped with a limiting device on the pivoting axle, the vehicle maintains roll-over stability, even if one wheel becomes detached from the ground, thus fostering the proper choice of work equipment.
Moreover, when dealing with implements, substantial shifts in the centre of gravity occur, greatly increasing the risk of tipping, as stressed by several researchers [60,61,62] who observed that this risk is particularly pronounced on sloped terrain, where stability and traction may be further compromised. These findings highlight the importance of operational adjustments, especially in scenarios involving steep slopes and fluid-carrying equipment.
The importance of correct machine maneuvering is also a relevant issue that has emerged from the analysis, as in mountainous and hilly areas, the slope rapidly changes. In fields classified as C.2 (yellow areas), the risk of lateral overturns is high, and farmers must follow paths parallel to the slope lines (red dotted line in Figure 13a) only. At the same time, this information can also be useful in the case of planning row alignment in novel plant cultivation (Figure 13b). Accordingly, proper training and information are needed to avoid risk-taking behaviours. This aligns with research findings of several studies underlining the fact that farmers are prone to incorrect maneuvering due to a mismatch between perceived and actual risk [63,64,65,66,67]. Moreover, it confirms the research findings by Cogato et al. [68], who emphasized that GIS mapping offers the ability to determine the potential mechanizability of a block in advance, thereby supporting operative and management decisions.
This information can be made available to farmers both through traditional means, such as farmers’ associations and cooperatives, as well as by means of specific software tools that can be used individually before starting work activities. This type of information can be used to create practical, territory-specific guidelines to enhance safety awareness and accident prevention. These guidelines could help farmers determine which machinery can be used safely in specific areas, assess risks associated with slope steepness, and adopt appropriate operational solutions to minimize roll-over/tip-over risks. This output can reduce the lack of safety instructions when using self-propelled machinery, outlined by several studies [69,70,71]. This is consistent with Vigoroso et al. [45], who outlined the fact that when certain risks cannot be entirely eliminated, such as the risk of tractor roll/tip-overs due to steep slopes, additional safety measures must be implemented to alert operators, ensuring they are aware of high-risk areas. These warnings, combined with proper training and operational guidelines, help operators develop safer behaviours, such as adjusting their driving techniques, avoiding dangerous inclines, and using specialized equipment.
Furthermore, the development of specific applications to be used by farmers individually is in line with previous studies, where the availability of an app for preventive risk assessment before starting work can improve farmers’ awareness of the correct use of machinery by means of more user-centred tools [72,73,74,75]. This issue represents a future advancement in current research, aligning with INAIL’s institutional objectives.
Further improvements are also needed to better integrate the EN ISO 16231-2 data with those related to both the field area to be cultivated and the machinery features, since several parameters of the latter can influence its operative behaviour, such as the presence of liquids, the condition of tyres, and the presence of ROPSs, altering the roll/tip-over risk level [76,77,78]. Overall, it must be noted that the proposed approach concerns the use of existing machinery, which, in most countries, such as those in the European Union, is designed in accordance with the slope limits indicated by the EN ISO 16231-2 standard. Nevertheless, the slope mapping obtained in this study could also be used as a reference to improve machinery design with the aim of augmenting its usability in mountainous areas. In addition, the proposed approach may also be used for assessing the stability of tractors combined with specific interchangeable equipment (e.g., flail mowers, sprayers, etc.). However, in this case, it is preliminarily necessary to define the reference stability angles for each combination of tractors with specific implements, considering that EN ISO 16231-2 does not apply.

5. Conclusions

Working in hilly and mountainous areas always presents a roll/tip-over risk for farmers when using self-propelled machinery. An accurate mapping of the slope steepness can allow them to be more aware of the level of risk, and consequently of the correct behaviour to follow. Today’s information technology tools allow us to map cultivated areas by means of 3D georeferencing tools and classify them into different categories depending on slope steepness. The current study presents the first part of a national research project promoted by the Italian Compensation Authority (INAIL), and the findings showed that the proposed approach enables the prediction of terrain slopes within specific cultivated areas in relation to plantation layouts, and correlates them with the static stability angle of the machinery operating in those regions. In this way, it becomes possible to generate detailed slope maps tailored to particular areas and machine types, allowing operators to assess in advance whether a given machine can safely operate on a specific slope before physically reaching the location. Such preventive analysis cannot completely solve these problems, but can certainly serve to significantly enhance safety measures, reducing the likelihood of roll/tip-over accidents and minimizing the risk of injuries. However, this positive outcome is related to the case study context only, and further validation is necessary. In particular, additional effort is necessary to expand the database of mapped areas covered by high-resolution digital terrain models (HR-DTMs). Hence, further research is needed to improve the coverage degree of the terrain model, as well as to integrate an automated risk analysis procedure driven by artificial intelligence into user-centred tools. These advancements would streamline the mapping process, reducing the time required for preventive risk analysis and making the tool more practical for widespread adoption in agricultural safety management.

Author Contributions

Conceptualization, methodology, software, validation, formal analysis, investigation; resources; data curation; writing—original draft preparation; writing—review and editing; visualization, supervision; project administration D.P., L.V., D.G. and V.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Additional data can be provided upon request to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Scheme of the proposed approach.
Figure 1. Scheme of the proposed approach.
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Figure 2. Study area—the Autonomous Province of Trento.
Figure 2. Study area—the Autonomous Province of Trento.
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Figure 3. Autonomous Province of Trento digital elevation map to identify mountainous and hilly areas (dark grey) and flat ground (white/light grey).
Figure 3. Autonomous Province of Trento digital elevation map to identify mountainous and hilly areas (dark grey) and flat ground (white/light grey).
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Figure 4. Definitive study area and relative slope (the slope steepness is expressed as a percentage).
Figure 4. Definitive study area and relative slope (the slope steepness is expressed as a percentage).
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Figure 5. Categorization of the territory in areas potentially exposed to different types of roll-over/tip-over risk (n/a = not applicable).
Figure 5. Categorization of the territory in areas potentially exposed to different types of roll-over/tip-over risk (n/a = not applicable).
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Figure 6. Map of roll-over risk level for combine harvester without slope compensation system (type 1 machine): green (category C1 area), yellow (category C2 area), and red (category C3 area).
Figure 6. Map of roll-over risk level for combine harvester without slope compensation system (type 1 machine): green (category C1 area), yellow (category C2 area), and red (category C3 area).
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Figure 7. Map of roll-over risk level for combine harvester with body levelling system (type 3 machine): green (category C.1 area), yellow (category C.2 area), and red (category C.3 area).
Figure 7. Map of roll-over risk level for combine harvester with body levelling system (type 3 machine): green (category C.1 area), yellow (category C.2 area), and red (category C.3 area).
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Figure 8. The municipality of Trento.
Figure 8. The municipality of Trento.
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Figure 9. Vineyards in the municipality of Trento.
Figure 9. Vineyards in the municipality of Trento.
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Figure 10. Area in hectares of the municipality of Trento where the use of grape harvesters with body levelling systems (orange) and without (blue) may expose farmers to roll-over risk according to risk category (C.1, C.2, and C.3).
Figure 10. Area in hectares of the municipality of Trento where the use of grape harvesters with body levelling systems (orange) and without (blue) may expose farmers to roll-over risk according to risk category (C.1, C.2, and C.3).
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Figure 11. Details of categorization when using a grape harvester without a body levelling system (NO_BLS).
Figure 11. Details of categorization when using a grape harvester without a body levelling system (NO_BLS).
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Figure 12. Details of categorization when using a grape harvester with a body levelling system (W_BLS).
Figure 12. Details of categorization when using a grape harvester with a body levelling system (W_BLS).
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Figure 13. Slope lines for safe work activities in C.2 areas: (a) correct (OK) and incorrect (NO) working paths; (b) safe directions for planting new cultivations.
Figure 13. Slope lines for safe work activities in C.2 areas: (a) correct (OK) and incorrect (NO) working paths; (b) safe directions for planting new cultivations.
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Table 1. Most relevant agricultural activities in the Autonomous Province of Trento (PAT).
Table 1. Most relevant agricultural activities in the Autonomous Province of Trento (PAT).
Type of ActivityUtilized Agricultural Area (%)Number of Companies
(%)
Viticulture5.836.3
Apple8.128.0
Pasture56.28.7
Table 2. MOS and RSSA for the most common self-propelled agricultural machinery (adapted from [EN ISO 16231-2]).
Table 2. MOS and RSSA for the most common self-propelled agricultural machinery (adapted from [EN ISO 16231-2]).
Machine TypeLateral Roll/Tip-OverFront/Rear Tip-Over
MOS (%)RSSA (%)MOS (%)RSSA (%)
Combine harvester without slope compensation system12181827
Combine harvester with slope compensation system20302030
Combine harvester with body levelling system30453045
Forage harvester2537.52537.5
Field crop sprayer1522.52537.5
Root crop harvester10151522.5
Grape harvester without body levelling system20303045
Grape harvester with body levelling system30453045
Combine harvester without slope compensation system12181827
Combine harvester with slope compensation system20302030
Combine harvester with body levelling system30453045
Forage harvester2537.52537.5
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MDPI and ACS Style

Puri, D.; Vita, L.; Gattamelata, D.; Tulliani, V. Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach. Machines 2025, 13, 377. https://doi.org/10.3390/machines13050377

AMA Style

Puri D, Vita L, Gattamelata D, Tulliani V. Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach. Machines. 2025; 13(5):377. https://doi.org/10.3390/machines13050377

Chicago/Turabian Style

Puri, Daniele, Leonardo Vita, Davide Gattamelata, and Valerio Tulliani. 2025. "Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach" Machines 13, no. 5: 377. https://doi.org/10.3390/machines13050377

APA Style

Puri, D., Vita, L., Gattamelata, D., & Tulliani, V. (2025). Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach. Machines, 13(5), 377. https://doi.org/10.3390/machines13050377

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