**1. Introduction**

Remote sensing techniques can be useful for the estimation of plant health conditions, including monitoring the nutritional status [1–4], the stress response [5–7], plant count [8,9], yield prediction [10–12], chlorophyll content [13–15], pest and disease identification [16,17], and biomass estimation [18], among others. Multisensory data is often used to accomplish this task, including the ones acquired by orbital sensors, aircraft or Unnamed Aerial Vehicle (UAV)-embedded cameras, terrestrial sensors, and field spectroradiometers, known as proximal sensors [19–23]. This type of sensor can measure the spectral response of a target at very-high resolutions while having a reductive amount of radiometric interference by being near the leaf sample.

The usage of proximal sensors for plant evaluation has assisted phenological studies of different species. Due to the high spectral resolution capability of these sensors, studies have been relatively successful in modeling phenomena, such as the ones previously stated, but at the leaf level, like plant stress, yield prediction, nutrient content, chlorophyll, and many other attributes [24–27]. They also have the advantage of helping to define, in detail, the appropriate spectral regions to estimate these phenomena. This definition is relatively important as it can guide future research towards the development of equipment specifically designed to measure these regions [23]. Another type of contribution is that it can assist in creating spectral vegetation indices or other simpler mathematical models that contribute to identifying the different characteristics of plants [13,28].

Currently, one of the most common problems in monitoring crops is knowing the proper amounts of fertilization rates. Traditional agronomic methods used to evaluate plant nutrients are done regularly, in key periods, to manage fertilization of agricultural fields [29]. These methods require the collection of a high number of leaves for the chemical analysis of the leaf tissue. However, this chemical analysis is a time-consuming, labor-intensive, and pollutive task [3,30,31]. Remote sensing, specifically proximal sensing, can provide an effective alternative in assisting nutritional analysis of plants more accurately. The use of proximal sensors has an advantage over traditional agronomic methods since it allows to infer vegetation conditions in a non-invasive and non-destructive manner [32–35].

Regarding the monitoring of plant and leaf nutritional conditions by remote sensing systems, recent research has made significant advances, especially in the estimation of nitrogen (N) content [1–4,21,25,28,31]. These studies were conducted at orbital, aerial, terrestrial, or proximal levels in different crops. N deficiency is linked to a characteristic chlorosis symptom, which is observable at the visible spectra [21,25,28]. Still, considerable research was also able to identify spectral bands and wavelengths in the near and short-wave infrared regions related to this nutrient [2,3,25,28,36]. Regardless, even though N is a pretty standard nutrient to be evaluated by remote sensing systems, the same cannot be said about others.

The evaluation of nutrients, other than N, by proximal sensors, is more unusual. One study was able to infer potassium (K) content by computing random two-band spectral indices calculated from hyperspectral data ranging from 350 to 2500 nm [37]. Others focused on evaluating a large group of macronutrients, magnesium (Mg), S, phosphor (P), K, and calcium (Ca), and found important associations between the spectral region of 470 to 800 nm with them [32] Lastly, one approach aimed to predict macronutrients like K, calcium (Ca), and magnesium (Mg), as well as micronutrients like manganese (Mn) and iron (Fe), by using near-infrared spectroscopy, but their method did not return satisfactory results for micronutrients [24]. Recent literature demonstrates how hyperspectral measurements are being linked to nutrients, specifically macro. However, there is a gap in terms of micronutrient prediction by spectral sensors that need to be addressed by new research, and few studies were conducted within this theme. In citrus, a study performed a Partial Least-Squares Regression (PLSR) evaluation on both macro- and micronutrients and archived interesting results using

Laser-Induced Breakdown Spectroscopy (LIBS) [38]. Similar research, focusing only on near-infrared spectroscopy, also returned high predictions for both classes of nutrients [39].

Another way to infer chemical components from hyperspectral measurements is by applying a derivative analysis. The derivation of the reflectance data allows highlighting absorption features of components that, in a traditional spectral curve (i.e., reflectance curve), may not be measured with the same accuracy or even be detected [40–42]. Studies that apply a derivation of the reflectance curves in plants have found good correlations with N [40,41] and cadmium (Cd) concentrations [42]. Since the gains of derivative analysis are known in the literature, there are also methods for data analysis in the remote sensing scenario that may benefit from it. The advantages proportionated by derivatives may assist in the evaluation of leaf nutritional content when combined with more robust techniques.

The aforementioned studies found high relationships with hyperspectral data by employing various statistical methods in their analysis. However, methods like Partial Least-Squares Regression (PLSR), Principal Component Analysis (PCA), Stepwise-Multiple Linear Regression (SMLR), among others, returned different accuracies even for the same cultures [15,24,32,37–40,42]. Some of these methods are also reductive, and the prediction may decrease if an increase occurs in the model complexity [32]. Since hyperspectral measurements produce high and complex amounts of data, one type of approach that could ideally deal with this is machine learning.

Machine learning algorithms are a robust and intelligent technique that can model different types of data [43,44]. These algorithms have the advantage of being non-parametric and non-linear while being able to analyze noised and imperfect data [45–47]. They are also capable to perform numerous combinations and calculations in a matter of seconds, achieving relative success in remote sensing applications regarding plant analysis [48,49]. Concerning hyperspectral measurements, among the applications evaluated, these algorithms were able to return state-of-the-art performances for many situations [5,16,23,50–52]. Even though, to date, no study evaluated the performance of machine learning algorithms in inferring both plant macro- and micro-nutritional content with only leaf hyperspectral measurements. Since these algorithms have returned good accuracies in different hyperspectral analyses, they could be appropriate to deal with the complexity imposed by this type of dataset in the described situation.

As previously stated, the first-derivative of the reflectance data has already been proved to be effective in associating with different chemical components. From this information, it could be assumed that both the reflectance data and its first-derivative could be of assistance in predicting different nutrients of the leaf tissue. Since the derivation of a reflectance curve can highlight hard-to-detect components at the first level, it is possible that, by integrating these curves with machine learning algorithms, one can create important information regarding proximal sensing and plant nutritional analysis. In this spirit, a framework adopting machine learning algorithms are proposed to predict macro- and micronutrient content in the leaf-tissue directly from its hyperspectral response. This is the first approach to evaluate different nutrient content combining machine learning methods and reflectance/first-derivative data.

In this study, citrus leaves—more specifically, from Valencia-orange trees—were selected to compose the experimental dataset. It is well known that a sufficient supply of both macro- and micronutrients is critical to the management and sustainability of these plants, and the balance of available nutrients is a key component to profitability [53]. Citrus plants are economically important to the agricultural sector of many countries and may benefit significantly from a rapid and indirect nutritional assessment, such as the one proposed here. In this manner, the aims of this work are to a) show a method to indicate the most suitable spectra (reflectance/first-derivative), in order to model the nutrient content according to the algorithms' performance; and b) determine the important wavelengths or spectral regions associated with each nutrient.
