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As one of the most perilous industries, agriculture presents both fatal and non-fatal risks to farmers. Pakistan, a notable Asian nation, heavily depends on agriculture, which constitutes 23% of its GDP and employs 37.4% of its workforce. The study investigates the challenges within Pakistan's agriculture sector and enriches the existing literature by gathering data on agricultural machine-related injuries in Pakistan during 2022–2023. The survey, conducted from November 2022 to April 2023, involved 55 respondents, predominantly from Sindh and Punjab. The findings indicate a total of 507 injuries, with approximately a quarter being fatal (121 incidents). Sindh emerges as a significant hotspot, with the majority of injuries documented there. With respect to the key sources of injuries, the fodder cutter is a primary source of injuries with 201 injuries documented, accounting for approximately 40% of all injuries. Threshers contributed to 197 injuries, approximately 39% of the total, and about 52 are tractor-related injuries, making up about 10% of machine injuries. Regarding the severity and demographics of injuries, about 38% of cases involve amputation, with a higher incidence among males (77%) and individuals aged 15–34 years (65%). It is important to acknowledge the study's limitations, including a small participant pool and a brief data collection period. This research advocates for safety regulations, accident reduction measures, and increased safety awareness among farmers, aiming to foster a safer and more sustainable agricultural environment in Pakistan. [ABSTRACT FROM AUTHOR]

Automated agricultural machinery has advanced significantly in the previous ten years; however, the ability of such robots to operate safely will be critical to their commercialization. This study provides a holistic evaluation of the work carried out so far in the field of automated agricultural machines' safety, as well as a framework for future research considerations. Previous automated agricultural machines' safety-related studies are analyzed and grouped into three categories: (1) environmental perception, (2) risk assessment as well as risk mitigation, and (3) human factors as well as ergonomics. The key findings are as follows: (1) The usage of single perception, multiple perception sensors, developing datasets of agricultural environments, different algorithms, and external solutions to improve sensor performance were all explored as options to improve automated agricultural machines' safety. (2) Current risk assessment methods cannot be efficient when dealing with new technology, such as automated agricultural machines, due to a lack of pre-existing knowledge. Full compliance with the guidelines provided by the current International Organization for Standardization (ISO 18497) cannot ensure automated agricultural machines' safety. A regulatory framework and being able to test the functionalities of automated agricultural machines within a reliable software environment are efficient ways to mitigate risks. (3) Knowing foreseeable human activity is critical to ensure safe human–robot interaction. [ABSTRACT FROM AUTHOR]