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Unsupervised Domain Adaptation and Super Resolution on Drone Images for Autonomous Dry Herbage Biomass Estimation by Albert, Paul; Saadeldin, Mohamed; Narayanan, Badri; Fernandez, Jaime; Mac Namee, Brian; Hennessey, Deirdre; O'Connor, Noel E.; McGuinness, Kevin is a scholarly article available to read on EtoBox.

What is Unsupervised Domain Adaptation and Super Resolution on Drone Images for Autonomous Dry Herbage Biomass Estimation about?

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 production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. 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. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of l

Author

Albert, Paul; Saadeldin, Mohamed; Narayanan, Badri; Fernandez, Jaime; Mac Namee, Brian; Hennessey, Deirdre; O'Connor, Noel E.; McGuinness, Kevin

Published

2022

Language

EN