Addressing Ergonomic Challenges in Agriculture through AI-Enabled Posture Classification
Source abstract
Abstract
In this study, we explored the application of Artificial Intelligence (AI) for posture detection in the context of ergonomics in the agricultural field. Leveraging computer vision and machine learning, we aim to overcome limitations in accuracy, robustness, and real-time application found in traditional approaches such as observation and direct measurement. We first collected field videos to capture real-world scenarios of workers in an outdoor plant nursery. Next, we labeled workers’ trunk postures into three distinct categories: neutral, slight forward bending and full forward bending. Then, through CNNs, transfer learning, and MoveNet, we investigated the effectiveness of different approaches in accurately classifying trunk postures. Specifically, MoveNet was utilized to extract key anatomical features, which were then fed into various classification algorithms including DT, SVM, RF and ANN. The best performance was obtained using MoveNet together with ANN (accuracy = 87.80%, precision = 87.46%, recall = 87.52%, and F1-score = 87.41%). The findings of this research contributed to the integration of computer vision techniques with ergonomic assessments especially in the outdoor field settings. The results highlighted the potential of correct posture classification systems to enhance health and safety prevention practices in the agricultural industry.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Agriculture and Farm Safety · Ergonomics and Musculoskeletal Disorders
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, École de technologie supérieure, Montreal, QC H3C 1K3, Canada
Department of Metallurgical and Material Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou 511442, China
Funding
École de technologie supérieure start-up fund for new professor · Mitacs Globalink Research Internship program · Natural Sciences and Engineering Research Council of Canada, Discovery Grant Program
References (50)
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