# Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
###### Abstract  
Although hyperspectral remote sensing techniques have increasingly been used in the nutritional quantification of plants, it is important to understand whether the method shows a satisfactory response during the various phenological stages of the crop. The aim of this study was to quantify the levels of phosphorus (P), potassium (K), calcium (Ca) and zinc (Zn) in the leaves of Vigna Unguculata (L.) Walp using spectral data obtained by a spectradradometer. A randomised block design was used, with three treatments and twenty-five replications. The crop was evaluated at three growth stages: V4, R6 and R9. Single-band models were fitted using simple correlations. For the band ratio models, the wavelengths were selected by 2D correlation. For the models using partial least squares regression (PLSR), the stepwise method was used. The model showing the best fit was used to estimate the phosphorus content in the single-band (R\({}^{2}\) = 0.62; RMSE = 0.54 and RPD = 1.61), band ratio (R\({}^{2}\) = 0.66; RMSE = 0.65 and RPD = 1.52) and PLSR models, using data from each of the phenological stages (R\({}^{2}\) = 0.80; RMSE = 0.47 and RPD = 1.66). Accuracy in modelling leaf nutrients depends on the phenological stage, as well as the amount of data used, and is more accurate with a larger number of samples. Vigna unguculata; hyperspectral data; evaluating nutritional status 2022 2  
to crop development [7]; therefore, to assess whether the adopted soil management has a positive result on the plant, leaf analysis is used. Plant nutrition is a determining factor in carrying out physiological processes that influence crop growth, development and production [8]. The nutritional status of plants is generally monitored by means of laboratory analyses that require a large number of leaves and methods that are time-consuming, laborious and that generate polluting residue [9]. Furthermore, such analyses must be carried out frequently to ensure a better representation of the nutritional status of the crops, generating extra costs for farmers [10]. Faced with so many challenges in assessing leaf nutrients, remote sensing is seen as an efficient alternative technique for nutrient monitoring, as well as for chlorophyll content and identifying diseases, among other activities [11; 12; 13; 14; 15]. Remote sensing allows fast and non-destructive nutrient monitoring of plants using satellite images and multi and hyperspectral sensors [15; 16; 17]. Researchers have been using multispectral [18; 19; 20] and hyperspectral data from the leaf [21; 22; 23] for monitoring and estimating the nutritional status of crops. The use of multispectral data records less information as fewer broad bands are collected for a given region of the electromagnetic spectrum [22]. On the other hand, hyperspectral data records almost continuous spectral information from narrow bands located in the visible (400-700 nm), near-infrared (NIR: 700-1300 nm) and shortwave infrared (SWIR: 1300-2500) regions [24]. The most evaluated nutrient by hyperspectral sensors is nitrogen (N); however, studies have also obtained satisfactory results in determining phosphorus (P), potassium (K) and calcium (Ca), at wavelengths that vary from 350 to 2500 nm [24; 25]. The large amount of information generated by hyperspectral sensors results in multicollinearity between neighbouring bands, which makes it difficult to analyse the data. Partial least squares regression models (PLSR) give satisfactory results in reducing multicollinearity in spectroradiometric data [25]. Studies have used PLSR in generating models with small sets of samples and a large number of responses. The PLSR model has proven to be suitable for estimating nutrient levels in crops [25; 26; 27; 28]. Researchers have obtained efficient results in estimating leaf nutrients using hyperspectral data in legumes [28; 29], the Valencia orange [13], maize [22], rapeseed [23] and pasture [21]. Several studies have obtained positive calibrations for predicting nutrients in plants using the PLSR model.