24 citations · 55 across the 21 of their papers we have counts for
5 papers · 1 filter
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning
Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee
Identifying spurious correlations learned by a trained model is at the core of refining a trained model and building a trustworthy model. We present a simple method to identify spu…
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation
Paul Albert, Mohamed Saadeldin, Badri Narayanan +5
Sward species composition estimation is a tedious one. Herbage must be collected in the field, manually separated into components, dried and weighed to estimate species composition…
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation
Paul Albert, Mohamed Saadeldin, Badri Narayanan +5
Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk producti…
Semi-supervised dry herbage mass estimation using automatic data and synthetic images
Paul Albert, Mohamed Saadeldin, Badri Narayanan +5
Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk production systems. Being aware of the herbage biomass in the field enables farmers to…
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset
Badri Narayanan, Mohamed Saadeldin, Paul Albert +2
The dairy industry uses clover and grass as fodder for cows. Accurate estimation of grass and clover biomass yield enables smart decisions in optimizing fertilization and seeding d…