no code implementations • 20 Apr 2022 • Paul Albert, Mohamed Saadeldin, Badri Narayanan, Brian Mac Namee, Deirdre Hennessy, Aisling H. O'Connor, Noel E. O'Connor, Kevin McGuinness
Sward species composition estimation is a tedious one.
1 code implementation • 18 Apr 2022 • Paul Albert, Mohamed Saadeldin, Badri Narayanan, Jaime Fernandez, Brian Mac Namee, Deirdre Hennessey, Noel E. O'Connor, Kevin McGuinness
In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage.
no code implementations • 26 Oct 2021 • Paul Albert, Mohamed Saadeldin, Badri Narayanan, Brian Mac Namee, Deirdre Hennessy, Aisling O'Connor, Noel O'Connor, Kevin McGuinness
Deep learning for computer vision is a powerful tool in this context as it can accurately estimate the dry biomass of a herbage parcel using images of the grass canopy taken using a portable device.
no code implementations • 8 Jan 2021 • Badri Narayanan, Mohamed Saadeldin, Paul Albert, Kevin McGuinness, Brian Mac Namee
In this paper, we demonstrate that applying data augmentation and transfer learning is effective in predicting multi-target biomass percentages of different plant species, even with a small training dataset.