AI-based spatial analysis of crop yield and its relationship with weather variables using satellite agrometeorology

Satellite agrometeorology is the use of satellite remote sensing data to estimate meteorological factors like temperature, precipitation, and radiation, which have a significant impact on crop development and productivity. Remote sensing data, which provides details on the spatial distribution of meteorological parameters, is collected by satellites orbiting the Earth. The researchers intend to investigate the spatial variance in agricultural productivity and its link to weather variables like temperature and precipitation using satellite agrometeorology. The work creates spatially detailed maps of crop productivity and weather parameters using ground-based weather measurements and satellite remote sensing data. The study employed statistical analysis and geographic modelling techniques. Regression analysis is used by researchers to investigate the relationship between agricultural yield and climatic variables. To construct maps of agricultural production and meteorological variables, they also use geospatial modelling. The study’s importance arises from its potential to give farmers, decision-makers, and other stakeholders vital information. The study’s analysis of geographical patterns of crop production and their relationship to climatic variables will assist farmers in making knowledgeable decisions about crop management practices, such as planting dates and irrigation schedules. The research findings can also assist decision-makers in creating efficient agriculture policies that address how crop output is impacted by climate change.