Title:	Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
Authors:	SANTOS MARCATO JUNIOR, J. ZAMBONI, P. SANTOS, M. F. JANK, L. CAMPOS, E. MATSUBARA, E. T.
Affiliation:	LUIZ SANTOS, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JOSÉ MARCATO JUNIOR, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; PEDRO ZAMBONI, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; MATEUS FIGUEIREDO SANTOS, CNPGC; LIANA JANK, CNPGC; EDILENE CAMPOS, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; EDSON TAKASHI MATSUBARA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL.
Date Issued:	2022
Citation:	Sensors, v. 22, article 4116, 2022.
Description:	We assessed the performance of Convolutional Neural Network (CNN)-based approaches using mobile phone images to estimate regrowth density in tropical forages. We generated a dataset composed of 1124 labeled images with 2 mobile phones 7 days after the harvest of the forage plants. Six architectures were evaluated, including AlexNet, ResNet (18, 34, and 50 layers), ResNeXt101, and DarkNet. The best regression model showed a mean absolute error of 7.70 and a correlation of 0.89. Our findings suggest that our proposal using deep learning on mobile phone images can successfully be used to estimate regrowth density in forages.
Thesagro:	Banco de Germoplasma Forragem Panicum Maximum Tecnologia
NAL Thesaurus:	Forage Mechanical harvesting Regression analysis Tillering
ISSN:	1424-8220
DOI:	https://doi.org/10.3390/s22114116
Notes:	Na publicação: Mateus Figueiredo Santos.
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPGC)