Título:	Predicting coffee water potential from spectral reflectance indices with neural networks.
Autoria:	NUNES, P. H. PIERANGELI, E. V. SANTOS, M. O. SILVEIRA, H. R. O. MATOS, C. S. M. de PEREIRA, A. B. ALVES, H. M. R. VOLPATO, M. M. L. SILVA, V. A. FERREIRA, D. D.
Afiliação:	PEDRO HENRIQUE NUNES, UNIVERSIDADE FEDERAL DE LAVRAS; EDUARDO VILELA PIERANGELI, UNIVERSIDADE FEDERAL DE LAVRAS; MELINE OLIVEIRA SANTOS, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; HELBERT REZENDE OLIVEIRA SILVEIRA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; CHRISTIANO SOUSA MACHADO DE MATOS, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; ALESSANDRO BOTELHO PEREIRA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; HELENA MARIA RAMOS ALVES, CNPCa; MARGARETE MARIN LORDELO VOLPATO, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; VÂNIA APARECIDA SILVA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; DANTON DIEGO FERREIRA, UNIVERSIDADE FEDERAL DE LAVRAS.
Ano de publicação:	2023
Referência:	Smart Agricultural Technology, v. 4, 100213, 2023.
Páginas:	6 p.
Conteúdo:	Leaf water potential is one of the main parameters used to assess water relations in plants by revealing levels of tissue hydration. It is commonly measured with the Scholander pressure chamber; which demands hard work and a time-consuming process. On the other hand, there is a diversified literature demonstrating the assessments of several plant variables via indices of leaf reflectance, that also present direct and indirect relationships with water potential. The aim of this work is to exploit spectral variables to estimate the water potential of coffee plants by using computational intelligence approaches. Data was collected in the cities of Santo Antônio do Amparo and Diamantina, Brazil, from 2014 to 2018. Two neural networks (Multi-Layer Perceptron) were designed to estimate and classify leaf water potential based on spectral variables. Moreover, a classifier and an estimator based on decision tree were also developed. The results showed that the artificial neural network model was superior as an estimator when compared with the decision tree model, with an average confidence index of 0.8550. On the other hand, decision trees showed a slightly higher performance as a classifier, with an overall accuracy of 88.8% and a Kappa index of 70.07%. We concluded that the leaf reflectance indices may be properly used to build accurate models for estimating coffee water potential. The indices PRI, NDVI, CRI1 and SIPI were the most relevant ones for estimating and classifying the coffee water potential.
NAL Thesaurus:	Artificial intelligence Neural networks Trees Water potential Coffea
Digital Object Identifier:	https://doi.org/10.1016/j.atech.2023.100213
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (SAPC)