REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS
UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely...
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Copernicus Publications
2017-08-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W3/41/2017/isprs-annals-IV-2-W3-41-2017.pdf |
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doaj-6a8a6df89ee1484b96c48d2c95bb845f2020-11-25T00:20:34ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502017-08-01IV-2-W3414810.5194/isprs-annals-IV-2-W3-41-2017REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKSA. Milioto0P. Lottes1C. Stachniss2Institute of Geodesy and GeoInformation, University of Bonn, GermanyInstitute of Geodesy and GeoInformation, University of Bonn, GermanyInstitute of Geodesy and GeoInformation, University of Bonn, GermanyUAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W3/41/2017/isprs-annals-IV-2-W3-41-2017.pdf |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
A. Milioto P. Lottes C. Stachniss |
| spellingShingle |
A. Milioto P. Lottes C. Stachniss REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| author_facet |
A. Milioto P. Lottes C. Stachniss |
| author_sort |
A. Milioto |
| title |
REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS |
| title_short |
REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS |
| title_full |
REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS |
| title_fullStr |
REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS |
| title_full_unstemmed |
REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS |
| title_sort |
real-time blob-wise sugar beets vs weeds classification for monitoring fields using convolutional neural networks |
| publisher |
Copernicus Publications |
| series |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| issn |
2194-9042 2194-9050 |
| publishDate |
2017-08-01 |
| description |
UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is
the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and
weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain
a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our
system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the
weeds on the field. |
| url |
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W3/41/2017/isprs-annals-IV-2-W3-41-2017.pdf |
| work_keys_str_mv |
AT amilioto realtimeblobwisesugarbeetsvsweedsclassificationformonitoringfieldsusingconvolutionalneuralnetworks AT plottes realtimeblobwisesugarbeetsvsweedsclassificationformonitoringfieldsusingconvolutionalneuralnetworks AT cstachniss realtimeblobwisesugarbeetsvsweedsclassificationformonitoringfieldsusingconvolutionalneuralnetworks |
| _version_ |
1725366487386947584 |