01578naa a2200325 a 450000100080000000500110000800800410001902200140006002400430007410000110011724501400012826000090026850000470027752006340032465000110095865000260096965000240099565000140101965000250103365000130105865000200107165000150109170000230110670000160112970000180114570000130116370000150117670000210119177300400121221512042023-01-25       2022    bl uuuu     u00u1 u    #d  a1424-82207 ahttps://doi.org/10.3390/s221141162DOI1 aSANTOS  aDeep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.h[electronic resource]  c2022  aNa publicação: Mateus Figueiredo Santos.  aWe 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.  aForage  aMechanical harvesting  aRegression analysis  aTillering  aBanco de Germoplasma  aForragem  aPanicum Maximum  aTecnologia1 aMARCATO JUNIOR, J.1 aZAMBONI, P.1 aSANTOS, M. F.1 aJANK, L.1 aCAMPOS, E.1 aMATSUBARA, E. T.  tSensorsgv. 22, article 4116, 2022.