Sutter, Michael; Cornu, Mathieu; Aebischer, Philippe; Reidy, Beat (2022). Satellite-based estimation of herbage mass: comparison with destructive measurements and UAV model’s estimation In: Delaby, L.; Baumont, R.; Brocard, V.; Lemauviel-Lavenant, S.; Plantureux, S.; Vertès, F.; Peyraud, J. L. (eds.) Grassland at the heart of circular and sustainable food systems: Proceedings of the 29th General Meeting of the European Grassland Federation. Grassland Science in Europe: Vol. 27 (pp. 734-736). European Grassland Federation (EGF)
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Regular estimation of herbage mass (HM) is a prerequisite for efficient pasture management. In addition to classical estimation using rising plate meters, remote-sensing methods using unmanned aerial vehicles (UAV) or satellites are available. Pasture.io has developed a model that estimates HM based on daily satellite data, herbage growth models and herbage-ingested input data recorded by farmers combined with artificial intelligence. This study compared the accuracy of Pasture.io HM estimations with UAV estimations and destructive measurements. Pastures from three Swiss farms were assessed regularly in May, June and July 2021. It was found that Pasture.io estimates HM with an error value RMSE 342 kg dry matter (DM) ha-1 while the UAV model’s estimation showed a higher RMSE of 447 kg DM ha-1. The results suggest that even in small pasture structures (mean paddock size: 1.2 ha), it is possible to estimate HM with reasonable accuracy based on satellite data and artificial intelligence.
Item Type: |
Conference or Workshop Item (Paper) |
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Division/Institute: |
School of Agricultural, Forest and Food Sciences HAFL School of Agricultural, Forest and Food Sciences HAFL > Agriculture School of Agricultural, Forest and Food Sciences HAFL > Agriculture > Grasslands and Ruminant Production Systems |
Name: |
Sutter, Michael 0000-0003-0314-5697; Cornu, Mathieu; Aebischer, Philippe; Reidy, Beat 0000-0002-8619-0209; Delaby, L.; Baumont, R.; Brocard, V.; Lemauviel-Lavenant, S.; Plantureux, S.; Vertès, F. and Peyraud, J. L. |
Subjects: |
Q Science > QC Physics S Agriculture > S Agriculture (General) S Agriculture > SB Plant culture S Agriculture > SF Animal culture |
ISBN: |
978-2-7380-1445-0 |
Series: |
Grassland Science in Europe |
Publisher: |
European Grassland Federation (EGF) |
Language: |
English |
Submitter: |
Michael Sutter |
Date Deposited: |
27 Oct 2023 09:32 |
Last Modified: |
30 Oct 2023 11:22 |
Related URLs: |
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Additional Information: |
Die Erlaubnis, diese PDF-Datei im ARBOR-Repository zu veröffentlichen, wurde eingeholt |
Uncontrolled Keywords: |
grassland, artificial intelligence, pasture, spectroscopy, remote sensing, pasture-based agriculture |
ARBOR DOI: |
10.24451/arbor.20205 |
URI: |
https://arbor.bfh.ch/id/eprint/20205 |
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