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.

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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
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Description
Summary: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.

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