Kurtser, P., Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, 701 82, Sweden;
Ringdahl, O., Department of Computing Science, Umeå University, Umeå, 901 87, Sweden;
Rotstein, N., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, 8410501, Israel;
Edan, Y., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, 8410501, Israel
Current practice for vine yield estimation is based on RGB cameras and has limited performance. In this letter we present a method for outdoor vine yield estimation using a consumer grade RGB-D camera mounted on a mobile robotic platform. An algorithm for automatic grape cluster size estimation using depth information is evaluated both in controlled outdoor conditions and in commercial vineyard conditions. Ten video scans (3 camera viewpoints with 2 different backgrounds and 2 natural light conditions), acquired from a controlled outdoor experiment and a commercial vineyard setup, are used for analyses. The collected dataset (GRAPES3D) is released to the public. A total of 4542 regions of 49 grape clusters were manually labeled by a human annotator for comparison. Eight variations of the algorithm are assessed, both for manually labeled and auto-detected regions. The effect of viewpoint, presence of an artificial background, and the human annotator are analyzed using statistical tools. Results show 2.8-3.5 cm average error for all acquired data and reveal the potential of using low-cost commercial RGB-D cameras for improved robotic yield estimation.
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