Title:	An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods.
Authors:	OSCO, L. P. FURUYA, D. E. G. FURUYA, M. T. G. CORRÊA, D. V. GONÇALVEZ, W. N. MARCATO JUNIOR, J. BORGES, M. MORAES, M. C. B. MICHEREFF, M. F. F. AQUINO, M. F. S. LAUMANN, R. A. LISENBERG, V. RAMOS, A. P. M. JORGE, L. A. de C.
Affiliation:	LUCAS PRADO OSCO, Unoeste; DANIELLE ELIS GARCIA FURUYA, Unoeste; MICHELLE TAÍS GARCIA FURUYA, Unoeste; DANIEL VERAS CORRÊA, Unoeste; WESLEY NUNES GONÇALVEZ, UFMS; JOSÉ MARCATO JUNIOR, UFMS; MIGUEL BORGES, Cenargen; MARIA CAROLINA BLASSIOLI MORAES, Cenargen; MIRIAN FERNANDES FURTADO MICHEREFF; MICHELY FERREIRA SANTOS AQUINO; RAUL ALBERTO LAUMANN, Cenargen; VERALDO LISENBERG, UDESC; ANA PAULA MARQUES RAMOS, Unoeste; LUCIO ANDRE DE CASTRO JORGE, CNPDIA.
Date Issued:	2022
Citation:	Infrared Physics & Technology, v. 123, 2022. 104203.
NAL Thesaurus:	Remote sensing Precision agriculture Artificial intelligence
Keywords:	Field spectroscopy
DOI:	https://doi.org/10.1016/j.infrared.2022.104203
Notes:	Na publicação: Maria Carolina Blassioli-Moraes.
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CENARGEN)