An Empirical and Deep Learning Investigation of Working Conditions and Musculoskeletal Outcomes in Wheat Farmers

Background: In developing countries, various farming activities are performed manually with the help of traditional hand tools. Therefore, agriculture is recognized as one of the risky occupations. Objective: This research study aims to identify the critical working conditions of wheat farmers in Rajasthan state, India. Method: Data were collected through the survey conducted on 75 randomly selected wheat farmers of Rajasthan. The survey questionnaire gathered information related to demographic, occupational, and musculoskeletal discomfort faced by the farmers. In addition, a deep learning-based posture detection study of the workers was performed to assess the postural risks through a rapid upper limb assessment score. Result: The collected data were analyzed further for fruitful insights. The survey outcomes showed that awkward posture (41%) and repetitive movement (35%) were the most reported reasons for the severe risks of musculoskeletal disorders among farmers. The posture evaluationbased patent study outcomes showed that approximately 51% of subjects lie in the action category 4, which shows the higher level of risks in the activities performed by farmers. Conclusion: It is suggested to apply the principles of physical ergonomics in the agriculture sector and spread awareness among the farmers about the agriculture risks associated with farming activities.