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- Title
Deep Learning Regression Approaches Applied to Estimate Tillering in Tropical Forages Using Mobile Phone Images.
- Authors
Santos, Luiz; Junior, José Marcato; Zamboni, Pedro; Santos, Mateus; Jank, Liana; Campos, Edilene; Matsubara, Edson Takashi
- Abstract
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.
- Subjects
DEEP learning; CONVOLUTIONAL neural networks; FORAGE plants; CELL phones; MOBILE learning
- Publication
Sensors (14248220), 2022, Vol 22, Issue 11, p4116
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s22114116