The Augmented Agronomist Pipeline and Time Series Forecasting

George Onoufriou, Marc Hanheide, Georgios Leontidis*

*Corresponding author for this work

Research output: Contribution to conferencePosterpeer-review

Abstract

We propose a new pipeline to facilitate deep learning at scale for agriculture and food robotics, and exemplify it using strawberry tabletop. We use this multimodal, autonomously selfcollected, distributed dataset for predicting strawberry tabletop yield, aiming at informing both agronomists and creating a robotic attention system. We call this system the augmented agronomist, which is designed for agronomy forecasting, and support, maximizing the human time and awareness to areas most critical. This project seeks to be relatively protective of both its neural networks, and its data, to prevent things such as adversarial attacks, or sensitive method leaks from damaging the future growers livelihoods. Toward this end this project shall take advantage of, and further our existing distributed deep- learning framework Nemesyst. The augmented agronomist will take advantage of our existing strawberry tabletop in our Riseholme campus, and will use the generalized robotics platform Thorvald for the autonomous data collection.
Original languageEnglish
Number of pages3
DOIs
Publication statusPublished - 17 Apr 2020
Event3rd UK Robotics & Autonomous Systems Conference (UK-RAS) -
Duration: 17 Apr 202017 Apr 2020
https://www.ukras.org/news-and-events/uk-ras/

Conference

Conference3rd UK Robotics & Autonomous Systems Conference (UK-RAS)
Period17/04/2017/04/20
Internet address

Keywords

  • Machine Learning
  • Robotics
  • AGRICULTURE

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