mlsanityData to 7 Oct 2026

The TerraByte Client: providing access to terabytes of plant data

arXiv 2203.13691 ↗First posted 25 Mar 2022cs.CV cs.AI cs.DBcited by 3

Not on the map. Why?

Abstract (arXiv)

In this paper we demonstrate the TerraByte Client, a software to download user-defined plant datasets from a data portal hosted at Compute Canada. To that end the client offers two key functionalities: (1) It allows the user to get an overview on what data is available and a quick way to visually check samples of that data. For this the client receives the results of queries to a database and displays the number of images that fulfill the search criteria. Furthermore, a sample can be downloaded within seconds to confirm that the data suits the user's needs. (2) The user can then download the specified data to their own drive. This data is prepared into chunks server-side and sent to the user's end-system, where it is automatically extracted into individual files. The first chunks of data are available for inspection after a brief waiting period of a minute or less depending on available bandwidth and type of data. The TerraByte Client has a full graphical user interface for easy usage and uses end-to-end encryption. The user interface is built on top of a low-level client. This architecture in combination of offering the client program open-source makes it possible for the user to develop their own user interface or use the client's functionality directly. An example for direct usage could be to download specific data on demand within a larger application, such as training machine learning models.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 5 of its 20 references that are in the map. First posted on arXiv 25 Mar 2022.

Builds on

5 of 20 references.

References · 5

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  4. 2018Agricultural Robotics: The Future of Robotic Agriculturecited by 17
  5. 2020An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculturecited by 10

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2.

3 papers.

  1. 2023A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral imagescited by 1
  2. 2022Investigating classification learning curves for automatically generated and labelled plant imagescited by 0
  3. 2023Strategies and impact of learning curve estimation for CNN-based image classificationcited by 0