**1. Introduction**

Remote sensing is an important tool for the analysis of vegetation in agricultural fields because it allows farmers to obtain data in a faster manner than most traditional methods [1,2]. Changes in the spectral response of plants can be observed with equipment that records wavelength values, such as a spectroradiometer [3]. The analysis of the spectral signatures enables the identification of vegetation characteristics that would not be visually perceived in asymptomatic plants [4]. Because of this, many studies focusing on phytosanitary problems are based on spectroscopy. These problems include nutritional deficiencies [5–7]; diseases [8], biomass [9], and, as in the present study, water stress [10].

The spectral response of a plant differs according to the species, which motivates the creation of different approaches to model it [11]. One of the problems commonly faced by farmers is related to water stress, which limits growth and compromises its production [12]. Water stress is responsible for chlorophyll variation and impairing other biological components, such as leaf area and root size [13]. The alteration of these components results in the appearance of visual symptoms, but they are difficult to identify due to their similarity to other problems, such as diseases, malnutrition, and cold damage [14]. An alternative that can identify changes caused by water stress alone is hyperspectral analysis [10,11].

The amount of leaf water is best estimated in the near-infrared and medium-infrared spectral regions [15]. In the near-infrared region, the spectral response is associated with the structural organization of intracellular molecules located in the mesophyll, which is affected as a consequence of stress [14]. Stress may also unbalance other physiological conditions and cause changes in visible and red-edge regions [15]. Changes in these spectral regions are associated with foliar pigmentation. Studies have sought to evaluate spectral behavior in these and other regions of the spectrum [16,17]. In addition, the absorbance curve has shown a better relationship with leaf pigmentation [18], which encourages the use of absorbance data to evaluate the negative effects caused by stress in plants.

Different approaches have been adopted to model the hyperspectral response when detecting water stress in cultures. In rice, multivariate analysis models were applied to determine the spectral response of the plant under different stress levels [10]. In tomatoes, classification trees were used to separate the spectral indices that best corresponded to the induced water stress [12]. In winter wheat crops, through continuous analysis of hyperspectral data over time, it was possible to quantify water stress in relation to other variables, such as disease and nitrogen accumulation [19]. Other studies have evaluated the implications of water stress through hyperspectral data in different plants, such as vineyards [11] and citrus fruits [20].

Recently, machine learning approaches have been used in modeling the hyperspectral response of different conditions associated with vegetation [21]. The popular techniques used for analyzing data include regression analysis, vegetation indices, linear polarizations, wavelet-based filtering, and, currently, machine learning algorithms like random forest, decision tree, support vector machine (SVM), k-nearest neighbor (kNN), artificial neural networks (ANN), naïve Bayes (NB), and others [22–25]. To evaluate the hyperspectral response of plants, machine learning has already been implemented in different scenarios. A radial basis function and the kNN were used to detect citrus canker in several disease development stages [26]. ANN, NB, and kNN were also used to model pepper fusarium disease in a climate room [27]. A combination of different machine learning algorithms like SVM, ANN, and others were also evaluated to model photosynthetic variables [28].

In lettuce, water stress poses a major threat. To deal with this, commercially available seeds are being inoculated with rhizobacteria, because it mitigates the effects of the stress [29]. These effects; however, may not be visually perceptible, which makes detection by ordinary approaches difficult. Hyperspectral data have already demonstrated high potential in assessing water stress in plants in different spectral regions (350–2500 nm) [11,23,30]. However, to date, no model has evaluated the spectral response of lettuce submitted to water stress. Here we evaluate the hyperspectral response of water-stress induced lettuce with a machine learning method through ANN. The contribution of this study is twofold. Firstly, we identified the effects of water stress in lettuce and its association with their spectral response. Secondly, we evaluated the performance of the ANN algorithm to model its effects. The rest of this article is organized as follows. Section 2 presents the materials and methods adopted

in this study. Sections 3 and 4 present and discuss the results obtained in the experimental analysis. Finally, Section 5 concludes the article.
