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\begin{document}

\title{A Novel Machine Learning Approach for Cotton Yield Prediction}



\author[1,2]{Alakananda Mitra}%
\author[1,2]{Sahila Beegum}%
\author[1]{David Fleisher}%
\author[1]{Vangimalla R Reddy}%
\author[1,2]{Wenguang Sun}%
\author[2]{Chittaranjan Ray}%
\author[1]{Dennis Timlin}%
\author[2]{Arindam Malakar}%
\affil[1]{U.S. Department of Agriculture, Agricultural Research Service, Adaptive Cropping Systems Laboratory}%
\affil[2]{Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln}%


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  \date{June 09, 2024}


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\begin{abstract}
Cotton production in the United States has reduced since 1950. The U.S.
cotton industry is committed to sustainable cotton production practices
that reduce water, land, and energy usage and soil loss while improving
soil health and cotton yield. Various climate-smart agriculture
strategies have been planned that boost yields and may lower operating
costs. Cultivars, soil types, management strategies, pests and diseases,
climate, and weather patterns impact crops in complex and nonlinear ways
and make crop yield prediction difficult. This is where machine learning
(ML) comes in. In this work (Fig.1), we aim to accurately predict cotton
yield using an ML method incorporating the effects of climate change,
soil variety, cultivars, and the nitrogen from NH\textsubscript{4},
NO\textsubscript{3}, and Urea. Two types of cotton yield data---field
data and synthetic data---were used. The field data was collected in the
1980s and early 1990s across the southern cotton belt. The dataset
consisted of different soil types, cultivars, and amounts of nitrogen.
However, these data do not reflect the most recent effects of climate
change over the past few years. To address this issue, six years of
cotton yield data were generated using the process-based cotton model,
GOSSYM. This dataset helps train the ML algorithm with the climate
change effect and more precisely predict cotton yield. We concentrated
on three southern states: Texas, Mississippi, and Georgia. For each
state, three different locations in the cotton-producing counties were
chosen. Weather data from 2017 to 2022 for each location were generated
using the POWER Data Access Viewer web interface. The same planting and
harvest dates were selected for all cases. For each case, the
accumulated heat unit (AHU) was calculated from the weather data and
used as one of the inputs to the ML model. Instead of applying time
series weather data, calculating AHU simplified the scenario, and we
were able to reduce the number of computations. Soil types, cultivars,
and amounts of nitrogen were varied to create combinations of inputs
likely to correspond to the range of farm input factors currently being
experienced in these locations. We then developed a Random Forest
Regressor to predict the yield. The results show the use of this method
is highly accurate (\textasciitilde{}89\%), with
R\textsuperscript{2}~averaging around 0.82 and a root mean square error
of 117.03 kg/ha.%
\end{abstract}%



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