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We present a large-scale (25TB and growing) multi-modal dataset containing raw environmental data collected during routine farming operations. The data was continuously recorded using a custom-built, rugged sensor setup mounted on a Fendt 724 Vario tractor.
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We present a large-scale (60 h / 40 TB of field recordings and 19 h / 13 TB at farmyards) multi-modal dataset containing raw environmental data collected during routine farming operations. The data was continuously recorded using a custom-built, rugged sensor setup mounted on a Fendt 724 Vario tractor.
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This dataset is designed to support research in agricultural robotics, autonomous navigation in unstructured environments, computer vision, environmental mapping, and multi-modal sensor fusion.
@@ -123,7 +124,7 @@ <h4>Key Components</h4>
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<li>Ouster OS0 LiDAR (3D point clouds)</li>
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<li>Blickfeld Qb2 LiDAR (3D point clouds)</li>
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<li>Novatel Smart7/Smart2 GNSS/INS/RTK/NTRIP</li>
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<li>NVidia Jetson Orin AGX</li>
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<li>NVIDIA Jetson Orin AGX</li>
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<li>Teltonica RUTX50 5G Router</li>
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<li>MittX Roof Bar (Customized)</li>
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<li>10x Hella LED Black Magic Cube 3.2" 3000 lm</li>
L. Gunreben, N. Heider, S. Zürner, M. Schieck, and B. Franczyk, "Real-time windrow detection from onboard tractor sensors for automated following," in 46. GIL-Jahrestagung, Gesellschaft für Informatik e.V., 2026, in press.
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L. Gunreben, N. Heider, S. Zürner, M. Schieck, and B. Franczyk, "Real-time windrow detection from onboard tractor sensors for automated following," in 46. GIL-Jahrestagung, Gesellschaft für Informatik e.V., 2026.
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