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

Sensors (Basel). 2022 May 28;22(11):4116. doi: 10.3390/s22114116.

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.

Keywords: deep learning; forages; regrowth density.

MeSH terms

  • Cell Phone*
  • Deep Learning*
  • Neural Networks, Computer

Grants and funding

Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), and Associaçao para o Fomento à Pesquisa de Melhormento de Forrageiras (UNIPASTO). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001. The authors acknowledge the support of the Universidade Federal de Mato Grosso do Sul (Federal University of Mato Grosso do Sul)—UFMS/MEC—Brasil.