Matching the Best Viewing Angle in Depth Cameras for Biomass Estimation Based on Poplar Seedling Geometry

In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (−45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and −45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass production, with several important advantages: low cost, low power needs and a high frame rate (frames per second) when dynamic measurements are required.

This research was funded by the Spanish CICyT (Project No. AGL2011-25243). We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).

Peer reviewed