Author(s): Colturato, Adimara Bentivoglio ; Gomes, Andre Benjamin ; Pigatto, Daniel Fernando ; Colturato, Danielle Bentivoglio ; Roschildt Pinto, Alex Sandro [UNESP] ; Castelo Branco, Luiz Henrique ; Furtado, Edson Luiz [UNESP] ; Jaquie Castelo Branco, Kalinka Regina Lucas ; Iliadis, L. ; Papadopoulos, H. ; Jayne, C.
Date: 2020
Persistent ID: http://hdl.handle.net/11449/197436
Origin: Oasisbr
Subject(s): Artificial neural networks; thermal images; Pine tree and UAVs
Made available in DSpace on 2020-12-10T22:31:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-01-01
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Pine is used primarily as a source of raw materials for the industries of lumber and laminated plates, resin, pulp and paper. Pine may be affected, from the nursery to adults, in plantations by pathogens such as fungi and/or pests. The aim of this work was to recognize patterns in images obtained from a thermal plants camera in pine. An Unmanned Aerial Vehicle with a thermal camera embedded was used to take video images of pine trees. The video was segmented in pictures and all the pictures were standardized to the same size 240 x 350px. The images were segmented and a two-layer neural network feed-forward and the Scaled Conjugate Gradient (SCG) algorithm were used. The results proved to be satisfactory, with most errors near zero.
Univ Sao Paulo, Inst Math & Comp Sci ICMC, Sao Paulo, Brazil
Univ Paulista UNIP, Sao Paulo, Brazil
Univ Estadual Paulista Julio Mesquita Filho UNESP, Sao Paulo, Brazil
Univ Estadual Paulista Julio Mesquita Filho UNESP, Sao Paulo, Brazil
FAPESP: 2012/08498-5
: 573963/2008-9
: 08/57870-9
