Deep Learning Regression Approaches Applied to Estimate Tillering in Tropical Forages Using Mobile Phone Images

Autor:	Luiz Santos, José Marcato Junior, Pedro Zamboni, Mateus Santos, Liana Jank, Edilene Campos, Edson Takashi Matsubara
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	regrowth density deep learning forages Chemical technology TP1-1185
Zdroj:	Sensors, Vol 22, Iss 11, p 4116 (2022)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s22114116
Popis:	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.
Databáze:	Directory of Open Access Journals
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