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Open AccessArticle
Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images
1
Remote Sensing Group, Institute for Computer Science, Osnabrück University, 49074 Osnabrück, Germany
2
Faculty of Agricultural Sciences and Landscape Architecture, Osnabrück University of Applied Sciences, 49090 Osnabrück, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(14), 2684; https://doi.org/10.3390/rs16142684 (registering DOI)
Submission received: 28 June 2024
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Revised: 18 July 2024
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Accepted: 19 July 2024
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Published: 22 July 2024
Abstract
In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures.
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MDPI and ACS Style
Nahrstedt, K.; Reuter, T.; Trautz, D.; Waske, B.; Jarmer, T.
Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images. Remote Sens. 2024, 16, 2684.
https://doi.org/10.3390/rs16142684
AMA Style
Nahrstedt K, Reuter T, Trautz D, Waske B, Jarmer T.
Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images. Remote Sensing. 2024; 16(14):2684.
https://doi.org/10.3390/rs16142684
Chicago/Turabian Style
Nahrstedt, Konstantin, Tobias Reuter, Dieter Trautz, Björn Waske, and Thomas Jarmer.
2024. "Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images" Remote Sensing 16, no. 14: 2684.
https://doi.org/10.3390/rs16142684
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