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

Autor(es):Mostrar menos -

Osco, Lucas Prado ^[1] ; Marques Ramos, Ana Paula ^[2] ; Saito Moriya, Erika Akemi ^[3] ; Bavaresco, Lorrayne Guimaraes ^[4] ; de Lima, Bruna Coelho ^[4] ; Estrabis, Nayara ^[1] ; Pereira, Danilo Roberto ^[2] ; Creste, Jose Eduardo ^[2] ; Marcato Junior, Jose ^[1] ; Goncalves, Wesley Nunes ^[1] ; Imai, Nilton Nobuhiro ^[3] ; Li, Jonathan ^{[5, 6]} ; Liesenberg, Veraldo ^[7] ; de Araujo, Fabio Fernando ^[4]

Afiliação do(s) autor(es)

^[1] Univ Fed Mato Grosso do Sul, Fac Engn Architecture & Urbanism & Geog, Av Costa & Silva, BR-79070900 Campo Grande, MS - Brazil

^[2] Univ Western Sao Paulo, Environm & Reg Dev, R Jose Bongiovani 700, BR-19050920 Presidente Prudente - Brazil

^[3] Sao Paulo State Univ, Dept Cartog Sci, BR-19060900 Presidente Prudente - Brazil

^[4] Univ Western Sao Paulo, Agron Dev, R Jose Bongiovani 700, BR-19050920 Presidente Prudente - Brazil

^[5] Univ Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1 - Canada

^[6] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1 - Canada

^[7] Univ Estado Santa Catarina, Forest Engn Dept, BR-88040900 Florianopolis, SC - Brazil

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING; v. 11, n. 23 DEC 1 2019.
• Citações Web of Science: 3

Resumo

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. (AU)

• Processo FAPESP: 13/20328-0 - Promoção de crescimento e atividades bioquímicas em plantas inoculadas com Bacillus subtilis em condições de estresse hídrico
• Beneficiário: Fabio Fernando de Araujo
• Modalidade de apoio: Auxílio à Pesquisa - Regular