## 4 Conclusions  
This study examined an artificial intelligence (AI)-based helmet detection model developed to enhance safety practices in tractor factories and agricultural machinery manufacturing facilities. The objective of the proposed model is to reliably ascertain the status of helmet-wearing among employees through the integration of image processing and machine-learning techniques. The proposed AI-based model offers a number of significant advantages. A number of these advantages are outlined below:  
* The model exhibits a high level of accuracy, with a rate of 90.39%, which demonstrates its capacity to reliably detect helmet-wearing status, a crucial aspect for ensuring safety in tractor factories. * The model's capacity to perform deep feature extraction is enabled by the integration of nine distinct pre-trained neural networks, thereby facilitating the capture of more intricate features and the generation of more precise outcomes. * The application of explainable AI techniques facilitates a more comprehensive understanding of the model's decision-making process, enabling occupational health and safety experts to comprehend the rationale behind the model's conclusions. It is important to consider the limitations of the proposed model. * One potential limitation is the model's ability to generalize, which may be affected if it has not been tested under varying conditions, such as different lighting, image angles, and individual types. * The utilization of the model is contingent upon the availability of a sufficient and representative dataset. An imbalanced class distribution or an insufficient number of samples may have an adverse effect on the model's performance. * It is important to note that the computational power and time required to train and run the model may be constrained in certain cases, which is particularly pertinent for real-time applications. It is recommended that further research be conducted in the following areas: Although the proposed model represents a substantial advance in the field of safety practices in tractor factories, further research is required in several areas. * To enhance the generalizability of the findings, it would be beneficial to utilize larger and more diverse datasets from a greater number of tractor factories. * It would be beneficial to investigate the model's performance under a range of conditions, including different lighting, image angles, and individual types. * The model's real-time applicability and performance could be optimized to facilitate the instantaneous monitoring of helmet-wearing status. * It is possible that similar models could be applied to safety practices in other industries, thereby providing potential for further research in this area. The steps represent prospective avenues for further development, with the objective of augmenting the impact of the AI-based helmet detection model on occupational safety. In conclusion, this study presented an AI-based helmet detection model, which represents a significant advancement in the enhancement of workplace safety in facilities engaged in the production of tractors and agricultural machinery. It is anticipated that this model will provide valuable assistance to safety professionals and enhance worker safety. It is recommended that this model be employed in conjunction with other safety measures, taking into account a range of factors to guarantee a secure and safe working environment. Conceptualization, S.O. and O.E. ; methodology, S.O. and O.E. ; software, S.O. ; validation, S.O. and O.E. ; formal analysis, S.O. ; investigation, S.O. ; resources, O.E. ; data curation, S.O. ; writing--original draft preparation, S.O. and O.E. ; writing--review and editing, O.E. ; visualization, S.O. ; supervision, O.E. ; project administration, O.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request.