**2. Materials and Methods**

The framework proposed in this paper was divided into four main phases (Figure 1). In the first phase, the hyperspectral measurements of the leaf samples in a Valencia-orange orchard were performed. These measurements were conducted with a field spectroradiometer. In the second phase, the spectral measurements were corrected, and the data were pre-processed. These corrections aimed to convert the radiance signal to reflectance, as well as remove the noise and calculate their first-derivative. The third phase involved the data analysis by machine learning algorithms. In this phase, a fine-tuning to determine the most appropriate parameters to model the data was performed. The fourth and final phase consisted of the organization of the prediction values into a hyperspectral map, where it was identified as the most appropriate algorithm and wavelength (i.e., spectral window) to predict each nutrient.

**Figure 1.** The workflow of the four main processes adopted for the proposed approach.
