Search Results for author: Badri Narayanan

Found 5 papers, 2 papers with code

Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

1 code implementation18 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.

Super-Resolution Unsupervised Domain Adaptation

Semi-supervised dry herbage mass estimation using automatic data and synthetic images

no code implementations26 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.

Semantic Segmentation Synthetic Data Generation

Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset

no code implementations8 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.

Data Augmentation Transfer Learning

Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations

1 code implementation7 Jun 2019 Logan Ward, Ben Blaiszik, Ian Foster, Rajeev S. Assary, Badri Narayanan, Larry Curtiss

Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost.

BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.