Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.

Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.

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.

Saved in:
Bibliographic Details
Main Authors: SANTOS, MARCATO JUNIOR, J., ZAMBONI, P., SANTOS, M. F., JANK, L., CAMPOS, E., MATSUBARA, E. T.
Other Authors: 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.
Format: Artigo de periódico biblioteca
Language:Ingles
English
Published: Sensors, v. 22, article 4116, 2022. 2023-01-25
Subjects:Banco de Germoplasma, Forragem, Panicum Maximum, Tecnologia, Forage, Mechanical harvesting, Regression analysis, Tillering,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1151204
https://doi.org/10.3390/s22114116
Tags: Add Tag
No Tags, Be the first to tag this record!
id dig-alice-doc-1151204
record_format koha
spelling dig-alice-doc-11512042023-01-25T13:01:26Z Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images. SANTOS MARCATO JUNIOR, J. ZAMBONI, P. SANTOS, M. F. JANK, L. CAMPOS, E. MATSUBARA, E. T. 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. Banco de Germoplasma Forragem Panicum Maximum Tecnologia Forage Mechanical harvesting Regression analysis Tillering 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. Na publicação: Mateus Figueiredo Santos. 2023-01-25T13:01:26Z 2023-01-25T13:01:26Z 2023-01-25 2022 Artigo de periódico 1424-8220 http://www.alice.cnptia.embrapa.br/alice/handle/doc/1151204 https://doi.org/10.3390/s22114116 Ingles en openAccess Sensors, v. 22, article 4116, 2022.
institution EMBRAPA
collection DSpace
country Brasil
countrycode BR
component Bibliográfico
access En linea
databasecode dig-alice
tag biblioteca
region America del Sur
libraryname Sistema de bibliotecas de EMBRAPA
language Ingles
English
topic Banco de Germoplasma
Forragem
Panicum Maximum
Tecnologia
Forage
Mechanical harvesting
Regression analysis
Tillering
Banco de Germoplasma
Forragem
Panicum Maximum
Tecnologia
Forage
Mechanical harvesting
Regression analysis
Tillering
spellingShingle Banco de Germoplasma
Forragem
Panicum Maximum
Tecnologia
Forage
Mechanical harvesting
Regression analysis
Tillering
Banco de Germoplasma
Forragem
Panicum Maximum
Tecnologia
Forage
Mechanical harvesting
Regression analysis
Tillering
SANTOS
MARCATO JUNIOR, J.
ZAMBONI, P.
SANTOS, M. F.
JANK, L.
CAMPOS, E.
MATSUBARA, E. T.
Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
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.
author2 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.
author_facet 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.
SANTOS
MARCATO JUNIOR, J.
ZAMBONI, P.
SANTOS, M. F.
JANK, L.
CAMPOS, E.
MATSUBARA, E. T.
format Artigo de periódico
topic_facet Banco de Germoplasma
Forragem
Panicum Maximum
Tecnologia
Forage
Mechanical harvesting
Regression analysis
Tillering
author SANTOS
MARCATO JUNIOR, J.
ZAMBONI, P.
SANTOS, M. F.
JANK, L.
CAMPOS, E.
MATSUBARA, E. T.
author_sort SANTOS
title Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
title_short Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
title_full Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
title_fullStr Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
title_full_unstemmed Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
title_sort deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
publisher Sensors, v. 22, article 4116, 2022.
publishDate 2023-01-25
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1151204
https://doi.org/10.3390/s22114116
work_keys_str_mv AT santos deeplearningregressionapproachesappliedtoestimatetilleringintropicalforagesusingmobilephoneimages
AT marcatojuniorj deeplearningregressionapproachesappliedtoestimatetilleringintropicalforagesusingmobilephoneimages
AT zambonip deeplearningregressionapproachesappliedtoestimatetilleringintropicalforagesusingmobilephoneimages
AT santosmf deeplearningregressionapproachesappliedtoestimatetilleringintropicalforagesusingmobilephoneimages
AT jankl deeplearningregressionapproachesappliedtoestimatetilleringintropicalforagesusingmobilephoneimages
AT campose deeplearningregressionapproachesappliedtoestimatetilleringintropicalforagesusingmobilephoneimages
AT matsubaraet deeplearningregressionapproachesappliedtoestimatetilleringintropicalforagesusingmobilephoneimages
_version_ 1756194050356019200

Similar Items