Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case
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...
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ftdlr:oai:elib.dlr.de:143259 2024-01-14T10:02:16+01:00 Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case Nesteruk, Sergey Shadrin, Dmitrii Pukalchik, Mariia Andrey, Somov conrad, Zeidler Zabel, Paul Schubert, Daniel 2021-01-09 application/pdf https://elib.dlr.de/143259/ https://elib.dlr.de/143259/1/Antarctic_Station_Image_Compression_f.pdf en eng IEEE - Institute of Electrical and Electronics Engineers https://elib.dlr.de/143259/1/Antarctic_Station_Image_Compression_f.pdf Nesteruk, Sergey und Shadrin, Dmitrii und Pukalchik, Mariia und Andrey, Somov und conrad, Zeidler und Zabel, Paul und Schubert, Daniel (2021) Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case. IEEE Sensors Journal, 1 (1). IEEE - Institute of Electrical and Electronics Engineers. doi:10.1109/JSEN.2021.3050084 <https://doi.org/10.1109/JSEN.2021.3050084>. ISSN 1530-437X. Systemanalyse Raumsegment Zeitschriftenbeitrag PeerReviewed 2021 ftdlr https://doi.org/10.1109/JSEN.2021.3050084 2023-12-18T00:23:58Z 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 Article in Journal/Newspaper Antarc* Antarctic Antarctica German Aerospace Center: elib - DLR electronic library Antarctic The Antarctic IEEE Sensors Journal 21 16 17564 17572 |
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Systemanalyse Raumsegment Nesteruk, Sergey Shadrin, Dmitrii Pukalchik, Mariia Andrey, Somov conrad, Zeidler Zabel, Paul Schubert, Daniel Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case |
topic_facet |
Systemanalyse Raumsegment |
description |
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 |
format |
Article in Journal/Newspaper |
author |
Nesteruk, Sergey Shadrin, Dmitrii Pukalchik, Mariia Andrey, Somov conrad, Zeidler Zabel, Paul Schubert, Daniel |
author_facet |
Nesteruk, Sergey Shadrin, Dmitrii Pukalchik, Mariia Andrey, Somov conrad, Zeidler Zabel, Paul Schubert, Daniel |
author_sort |
Nesteruk, Sergey |
title |
Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case |
title_short |
Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case |
title_full |
Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case |
title_fullStr |
Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case |
title_full_unstemmed |
Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case |
title_sort |
image compression and plants classification using machine learning in controlled-environment agriculture: antarctic station use case |
publisher |
IEEE - Institute of Electrical and Electronics Engineers |
publishDate |
2021 |
url |
https://elib.dlr.de/143259/ https://elib.dlr.de/143259/1/Antarctic_Station_Image_Compression_f.pdf |
geographic |
Antarctic The Antarctic |
geographic_facet |
Antarctic The Antarctic |
genre |
Antarc* Antarctic Antarctica |
genre_facet |
Antarc* Antarctic Antarctica |
op_relation |
https://elib.dlr.de/143259/1/Antarctic_Station_Image_Compression_f.pdf Nesteruk, Sergey und Shadrin, Dmitrii und Pukalchik, Mariia und Andrey, Somov und conrad, Zeidler und Zabel, Paul und Schubert, Daniel (2021) Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case. IEEE Sensors Journal, 1 (1). IEEE - Institute of Electrical and Electronics Engineers. doi:10.1109/JSEN.2021.3050084 <https://doi.org/10.1109/JSEN.2021.3050084>. ISSN 1530-437X. |
op_doi |
https://doi.org/10.1109/JSEN.2021.3050084 |
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IEEE Sensors Journal |
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21 |
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16 |
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17564 |
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17572 |
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1788057183331549184 |