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@article{209576,
        author = {Kushal A S and Dr. Beenarani Manoj},
        title = {Air Quality Prediction and Monitoring System in Food Processing Industries},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {2615-2623},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209576},
        abstract = {Maintaining suitable air quality in food processing industries is important for workplace safety and environmental monitoring. Conventional monitoring approaches may rely on periodic measurements and provide limited insight into changing air conditions. This paper presents an Air Quality Prediction and Monitoring System that integrates environmental data processing, machine learning, and dashboard-based visualization. The proposed system uses simulated or prepared environmental data representing parameters such as temperature, humidity, particulate matter, carbon dioxide, and other relevant gas concentrations. The data are processed using Python-based data analysis techniques and prepared for machine learning prediction. The system architecture includes environmental data preparation, data quality checking, prediction, and visualization modules. A dashboard developed using Streamlit is intended to present environmental readings, air-quality trends, prediction outputs, and warning indicators. The approach provides a framework for examining how data-driven prediction can support air-quality monitoring in food processing environments. The implementation is evaluated through functional testing and appropriate prediction metrics, subject to the availability of verified experimental results. Further validation using real sensor measurements is required before the system can be applied in an actual industrial environment.},
        keywords = {Air Quality Prediction, Environmental Monitoring, Machine Learning, Data Preprocessing, Streamlit, Food Processing Industries},
        month = {October},
        }