Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case

Sergey Nesteruk, Dmitrii Shadrin, Mariia Pukalchik, Andrey Somov, Conrad Zeidler, Paul Zabel, Daniel Schubert

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

In this article, we share our experience in the scope of controlled-environment agriculture automation in the Antarctic station greenhouse facility called EDEN ISS. For remote plant monitoring, control, and maintenance, we solve the problem of plant classification. Due to the inherent communication limitations between Antarctica and Europe, we first propose the image compression mechanism for the data collection. We show that we can compress the images, on average, 7.2 times for efficient transmission over the weak channel. Moreover, we prove that decompressed images can be further used for computer vision applications. Upon decompressing images, we apply machine learning for the classification task. We achieve 92.6% accuracy on an 18-classes unbalanced dataset. The proposed approach is promising for a number of agriculture related applications, including the plant classification, identification of plant diseases, and deviation of plant phenology.

Original languageEnglish
Article number9316732
Pages (from-to)17564-17572
Number of pages9
JournalIEEE Sensors Journal
Volume21
Issue number16
DOIs
Publication statusPublished - 15 Aug 2021

Keywords

  • Agriculture
  • Antarctica
  • Cameras
  • Classification
  • computer vision
  • controlled-environment agriculture
  • Image coding
  • image compression
  • Machine learning
  • machine learning
  • Monitoring
  • Plants (biology)

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