Addressing Ergonomic Challenges in Agriculture through AI-Enabled Posture Classification

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.