A Hybrid Model for Air Quality and Crop Production Leveraging Geospatial Data from Giovanni and Statistical Data from FAOSTAT

As the global focus intensifies on environmental sustainability and agricultural productivity, the intersection of satellite data analysis, artificial intelligence (AI), and crop yield prediction becomes increasingly pertinent. In this study, we harness satellite data from Giovanni to assess the concentration of gases, including O3, PM, and CO, and calculate the Air Quality Index (AIQ). Employing the Prophet model, we predict future trends in these pollutants and AIQ. Furthermore, we investigate the impact of these pollutants, alongside temperature and moisture concentrations, on rice yield using data from the Food and Agriculture Organization of the United Nations (FAO). Leveraging a feedforward neural network, we model the complex relationship between air pollutants, AIQ, temperature, moisture, and crop yield. Through this interdisciplinary approach, our research aims to elucidate the intricate dynamics influencing agricultural outcomes and contribute to the development of sustainable farming practices.