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.

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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