HYBRID AI MODEL FOR PREDICTING AIR QUALITY AND RICE CROP YIELD USING SATELLITE DATA AND ENVIRONMENTAL VARIABLES

Dev Ashrit Behera, Paras Singh Bhatia, Meenakshi Kandpal, Jyotirmayee Routray, Pranati Mishra, Shivani Agarwal, Rabindra Kumar Barik

DOI Number
https://doi.org/10.2298/FUEE2503431B
First page
431
Last page
456

Abstract


Air quality has a significant impact on agricultural productivity, with pollutants such as ozone (O3), particulate matter (PM), and carbon monoxide (CO) posing serious threats to crop yields. Plant growth and yields can be disrupted by these pollutants, highlighting the need for effective solutions. Strategies, including improvements in air quality management to reduce pollutant levels and the development of advanced predictive models, have been proposed. The concentrations of air pollutants and their potential impacts on agriculture can be forecasted by these models, allowing proactive measures to be taken to mitigate adverse effects on crop yields. In this research, the challenges associated with predicting the impact of key air pollutants on rice yield in India are addressed. Satellite data from the Giovanni Data Centre was utilized to monitor concentrations of O₃, PM, and CO, and to calculate the Air Quality Index (AQI). The Prophet model is employed to predict future pollutant levels and AQI. Soil temperature and air moisture data were incorporated to assess their combined impact on crop yield. Nineteen years of monthly rice yield data from FAOSTAT was used to train a feed-forward neural network with inputs including PM, O₃, CO, AQI, soil temperature, and air moisture. A high accuracy of 94% was achieved by the model, effectively predicting crop yields based on these factors, and a clear inverse relationship between air quality and crop yield was demonstrated: significant decreases in yield were correlated with higher concentrations of O₃, PM, and CO. Concrete evidence of the detrimental effects of air pollution on crop productivity is provided by these findings.

Keywords

Air Quality Index, Agricultural productivity, Giovanni Data centre, Prophet Model, Feed Forward Neural Network.

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References


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