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

Journal: Sensors (Basel, Switzerland)
PMID: 35684736

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.

Authors

  • Luiz Santos
    Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil.
  • José Marcato Junior
    Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil.
  • Pedro Zamboni
    Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil.
  • Mateus Santos
    Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande 79106550, MS, Brazil.
  • Liana Jank
    Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande 79106550, MS, Brazil.
  • Edilene Campos
    Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil.
  • Edson Takashi Matsubara
    Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil.

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