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\begin{document}

\title{Assessing the potential of UAV Spectral Data and Machine learning for Soil Organic Carbon Prediction in Sorghum Fields}



\author[1]{Boubacar Gano}%
\author[1]{Jocelyn Saxton}%
\author[1]{Marie De Gracia Coquerel}%
\author[1]{Nathaniel Eck}%
\author[1]{Jaccob Stanton}%
\author[1]{Nurzaman Ahmed}%
\author[1]{Nadia Shakoor}%
\affil[1]{Donald Danforth Plant Science Center}%


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  \date{June 14, 2024}


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\begin{abstract}
Soil Organic Carbon (SOC) fluctuations in agricultural fields play a
critical role in determining soil fertility and carbon sequestration.
Efficient, non-destructive monitoring of SOC is essential. This study
utilized Unmanned Aerial Vehicles (UAVs) and four Machine Learning (ML)
algorithms-Multiple Linear Regression (MLR), Partial Least Squares
Regression (PLSR), Random Forest (RF), and Xtreme Gradient Boosting
(XGB)-to predict SOC levels.

Our research, set at the Danforth Center Field Research Site (FRS),
comprised 35 sorghum genotypes in a complete block design. Using a
GeoProbe drilling machine, we collected soil cores for compositional
analysis through the Haney test. Concurrently, drone-captured
multispectral images were processed to create orthomosaic maps and
extract features. The ML algorithms effectively predicted SOC levels,
with MLR models showing the highest accuracy (RMSE = 7.12 ppm; R =
0.74). We observed variation in SOC among genotypes, suggesting that
genotype-specific traits could influence SOC estimation accuracy. Field
plots planted with genotypes like SC1345, BTx623, and SAP-133 showed
strong predictability with errors below 5\%. In contrast, SC283 and
SAP-154 had prediction errors exceeding 30\%.

UAV-based remote sensing offers promising avenues for soil health
assessment, contributing to precision agriculture and sustainable land
management. Future studies could benefit from integrating hyperspectral
sensors to fully harness this technology's potential.%
\end{abstract}%



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