*Letter* **Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks**

**Lucas Prado Osco 1,\*, Ana Paula Marques Ramos 2, Érika Akemi Saito Moriya 3, Lorrayne Guimarães Bavaresco 4, Bruna Coelho de Lima 4, Nayara Estrabis 1, Danilo Roberto Pereira 2, José Eduardo Creste 2, José Marcato Júnior 1, Wesley Nunes Gonçalves 1, Nilton Nobuhiro Imai 3, Jonathan Li 5, Veraldo Liesenberg <sup>6</sup> and Fábio Fernando de Araújo <sup>4</sup>**


Received: 2 November 2019; Accepted: 23 November 2019; Published: 26 November 2019

**Abstract:** Modeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of water-stress induced lettuce (*Lactuca sativa* L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325–1075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields.

**Keywords:** spectroscopy; artificial intelligence; proximal sensing data; precision agriculture
