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\begin{document}

\title{Determining the potential of predicting soil nutrient concentration using hyperspectral imaging}



\author[1]{Terence Seldon Kwafo}%
\author[1,2]{Justin M McGrath}%
\affil[1]{Department of Plant Biology, University of Illinois}%
\affil[2]{Global Change and Photosynthesis Research Unit, USDA-ARS}%


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  \date{May 25, 2024}


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\begin{abstract}
Proper concentrations of several nutrients, such as iron (Fe), zinc (Zn)
and potassium (K) in the soil are needed for plant growth. Thus, farmers
sometimes test soils to determine fertiliser application rates. However,
measuring soil and plant nutrient concentrations is relatively
time-consuming and expensive. Therefore, usually, only a few samples are
collected and analysed. This limits the scale of research but also poses
some limitations on farming practices, as farmers cannot sample fields
at high density in order to customize application rates. Cheap, fast,
and high spatial resolution methods to measure soil nutrient
concentrations would alleviate some of these limitations. This work
aimed to determine the potential of hyperspectral imaging (HI) to
predict the concentration of some soil nutrients. Soil samples were
scanned by visible and near-infrared imaging systems with a total
wavelength range of 450-1700 nm. Fe, Zn, and K were analyzed. Partial
least-square regression models (PLSR) were used to correlate the
relative reflectance of total Fe, Zn and K in the soil samples. The PLSR
models could highly predict Fe concentration (R\textsuperscript{2} 0.81,
RMSE\% 16.6) and performed moderately well for Zn (R\textsuperscript{2}
0.30, RMSE\% 0.9) and K (R\textsuperscript{2~}0.47, RMSE\% 5.58)
concentrations. The overall results indicated that the hyperspectral
technique coupled with PLSR could be an accurate and reliable method for
determining soil nutrient concentrations.%
\end{abstract}%



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