## 1 Introduction  
Climate change, scarcity of resources and growing world population increasingly jeopardize global food and nutritional security [1]. To respond to this challenge, we need to accelerate plant breeding, provide new cultivars adapted to the changing environmental conditions, and optimize agricultural practices, decreasing the ecological footprint of food production [2]. The emergence of high throughput plant phenotyping and precision agriculture as key technologies to foster plant breeding and sustainable food production [3, 4] requires the development of non-invasive techniques enacted to collect structural and functional information from plants [5]. Additionally, the collected information (images and/or spectra) must be processed by automated high-throughput pipelines, and translated to biologically relevant information, including their water status, since it is expected to be critically affected by recurrent drought episodes [6]. This challenge has renewed the interest in the application of in vivo spectroscopic techniques in plant sciences, including reflectance spectroscopy. In fact, the use of reflectance spectroscopy to infer the constitutive and functional characteristics of plant tissues, from leaves [7] to fruits [8], is well established. Since the pioneering works that introduced reflectance spectroscopy in plant sciences [9] a multitude of reflectance indexes have been suggested to represent specific features of the plant material. Among the currently most used indexes, the Normalized Difference Vegetation Index (NDVI) [10] is related to the biochemical composition and structure of the plant tissue analysed, being used as an indicator of biomass, since it is strongly correlated with the amount of chlorophyll [11]. The Photosynthetic Reflectance Index (PRI) [12], in turn, is seen as a functional indicator, as it is related to the epoxidation state of the photosynthetic antenna xanthophylls [13], a very dynamic process connected with instant plant photosynthetic efficiency [14]. Reflectance indexes were developed to extract relevant information from reflectance spectra. However, increasing computational power and the developments in machine learning (ML) enacted the use of the entire data set of information present on reflectance spectra. Spectral reflectance within the visible region (400 to 750 nm) derives from the absorption of light by the leaf pigments (mostly chlorophylls and carotenoids), which absorb distinct wavelength bands. This selective absorbance creates reflectance signatures that depict numerous physiological processes in plants, such as senescence [15], nutrition status [16], and stress [17], among others, therefore holding important phenotypic information. The ability to use these large collections of reflectance-based information increases the chances of finding meaningful signals which contribute to a better interpretation of the data, allowing for the development of more accurate prediction models. To date, several machine learning algorithms have been used to analyze and understand plant spectral data [18; 19; 20], however, there is no standard consensus on the best technique to use as this depends on a number of factors, such as the type of problem, type and size of the data, and type of output, for example. Therefore, a comparative approach is a conventional method to assess an algorithm's suitability to solve a particular problem. In our study, we are interested in distinguishing between water-stressed and unstressed plants, and also between the level of stress (e.g., moderate or severe). Decision tree learning methods are a class of supervised learning that work well on simple and discrete classification [21]. Classification trees act like functions that map the input features to the different classes [22], and an individual decision tree is the simplest model of this type.