  The cotton industry in the United States is committed to sustainable
production practices that minimize water, land, and energy use while improving
soil health and cotton output. Climate-smart agricultural technologies are
being developed to boost yields while decreasing operating expenses. Crop yield
prediction, on the other hand, is difficult because of the complex and
nonlinear impacts of cultivar, soil type, management, pest and disease,
climate, and weather patterns on crops. To solve this issue, we employ machine
learning (ML) to forecast production while considering climate change, soil
diversity, cultivar, and inorganic nitrogen levels. From the 1980s to the
1990s, field data were gathered across the southern cotton belt of the United
States. To capture the most current effects of climate change over the previous
six years, a second data source was produced using the process-based crop
model, GOSSYM. We concentrated our efforts on three distinct areas inside each
of the three southern states: Texas, Mississippi, and Georgia. To simplify the
amount of computations, accumulated heat units (AHU) for each set of
experimental data were employed as an analogy to use time-series weather data.
The Random Forest Regressor yielded a 97.75% accuracy rate, with a root mean
square error of 55.05 kg/ha and an R2 of around 0.98. These findings
demonstrate how an ML technique may be developed and applied as a reliable and
easy-to-use model to support the cotton climate-smart initiative.
