Noise is a major physical hazard in agricultural activities, and numerous research activities have managed to detect its effects, resulting in surveys and measurements which help to define exposure limits, prevention methods, and control strategies. This review aims to collect and analyse the data from research studies and to provide a comprehensive overview on the subject. Thus, a set of 81 papers, gathered from the Scopus and PubMed scientific databases, has been analysed to provide information regarding the evolution of noise exposure levels over time, to highlight findings on noise-induced hearing loss (NIHL), and to list strategies for noise prevention and control in agriculture. Bibliographic research showed that noise measurements between 1991 and 2022, included in scientific research on farming, forestry, and animal husbandry, mainly reported values beyond the threshold of 85 dB(A); furthermore, several research activities on NIHL showed that farmers’ family members and children are often exposed to high levels of noise. Lastly, an analysis of the prevention and control strategies over time is provided, focusing on prevention programmes, screening, and the use of hearing protection devices (HPD). The identified literature suggests that additional efforts are required in regards to machinery design relating to the socio-technical aspects of agricultural activities and that side-effects of NIHL, as well as the negative impact of noise on other risks, might deserve further investigation.

Accurate detection of machinery-induced strip roads after forest operations is fundamental for assessing soil disturbance and supporting sustainable forest management. However, in Mediterranean pine forests where canopy openings after boom-corridor thinning are moderate, the effectiveness of different remote sensing techniques remains uncertain. Previous studies have shown that LiDAR-based methods can reliably detect logging trails in different forest stands, but their direct transfer to structurally simpler, even-aged Mediterranean stands has not been validated. This study addresses this gap by testing whether UAV-derived RGB imagery can achieve comparable accuracy to LiDAR-based methods under the canopy conditions of boom-corridor thinning. We compared four approaches for detecting strip roads in a black pine (Pinus nigra Arn.) plantation on Mount Amiata (Tuscany, Italy): one based on high-resolution UAV RGB imagery and three based on LiDAR data, namely Hillshading (Hill), Local Relief Model (LRM), and Relative Density Model (RDM). The RDM method was specifically adapted to Mediterranean conditions by redefining its return-density height interval (1–30 cm) to better capture areas of bare soil typical of recently trafficked strip roads. Accuracy was evaluated against a GNSS-derived control map using nine performance metrics and a balanced subsampling framework with bootstrapped confidence intervals and ANOVA-based statistical comparisons. Results confirmed that UAV-RGB imagery provides reliable detection of strip roads under moderate canopy openings (accuracy = 0.64, Kappa = 0.27), while the parameter-tuned RDM achieved the highest accuracy and recall (accuracy = 0.75, Kappa = 0.49). This study demonstrates that RGB-based mapping can serve as a cost-effective solution for operational monitoring, while a properly tuned RDM provides the most robust performance when computational resources are sufficient to work on large point clouds. By adapting the RDM to Mediterranean forest conditions and validating the effectiveness of low-cost UAV-RGB surveys, this study bridges a key methodological gap in post-harvest disturbance mapping, offering forest managers practical, scalable tools to monitor soil impacts and support sustainable mechanized harvesting.