Logo des Repositoriums
 

Deep Learning-based UAV-assisted grassland monitoring to facilitate Eco-scheme 5 realization

Kurzanzeige
dc.contributor.authorBasavegowda, Deepak H.
dc.contributor.authorHöhne, Marina M.-C.
dc.contributor.authorWeltzien, Cornelia
dc.date.accessioned2024-04-08T11:56:33Z
dc.date.available2024-04-08T11:56:33Z
dc.date.issued2024
dc.description.abstractEco-scheme 5 has been introduced to promote biodiversity in permanent grasslands through sustainable land management. While this scheme motivates farmers through result-based remuneration, it also entails a significant monitoring cost in terms of time and money to identify indicators manually. To overcome this burden and facilitate the realization of Eco-scheme 5, we developed an object detection model based on Deep Learning (DL) to automate the indicator species identification. First, we trained and evaluated the model on high-resolution Unmanned Aerial Vehicle (UAV) data. The model achieved an Average Precision (AP) rate of 80.8 AP50, but limited training data and the class imbalance problem among indicators affected the model performance. To address these problems, we enriched training data with proximal images of indicators, resulting in a performance gain from 80.8 AP50 to 95.3 AP50. Our results demonstrate the potential of DL and UAV applications in assisting result-based agri-environmental schemes (AES) such as Eco-scheme 5.en
dc.identifier.isbn978-3-88579-738-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43872
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 344
dc.subjectbiodiversity monitoring
dc.subjectresult-based AES
dc.subjectobject detection
dc.subjectcross-domain knowledge transfer
dc.titleDeep Learning-based UAV-assisted grassland monitoring to facilitate Eco-scheme 5 realizationen
dc.typeText/Conference Paper
gi.citation.endPage202
gi.citation.publisherPlaceBonn
gi.citation.startPage197
gi.conference.date27.-28. Februar 2040
gi.conference.locationStuttgart
gi.conference.reviewfull

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild
Name:
GIL_2024_Basavegowda_197-202.pdf
Größe:
448.19 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

P344 - 44. GIL-Jahrestagung 2024 - Fokus: Biodiversität fördern durch digitale Landwirtschaft