MEASURING NUT VOLUMES USING THE AZURE KINECT

Abstract. Rapid and accurate nut count and volume measurement techniques are critical for nut production and its automation. In this work, a three-dimensional (3D) depth camera, the Azure Kinect, was used to measure the volume of the nuts, along with our proposed point cloud processing framework. A group of nut16rowns was first collected as point clouds using the Azure Kinect, and then the point nearest neighbor (PNN) algorithm was used to perform fast nut single segmentation. After that, noise caused by the multi-path effect was identified from the collected point clouds. Most of the noise was eliminated using the surface boundary filter (SBF) algorithm. After the preprocessing, the volumes of the nut point cloud were estimated using the least-squares ellipsoid fitting (LSEF). We selected three different nuts in the experiment: 15 each of walnuts, pecans, and macadamia nuts. Their volumes were first measured by the Azure Kinect, and then by the water displacement method (WDM) as control. The results show that the proposed measurement setup can accurately count nuts and the average nuts volume estimation accuracy is 92.1% when compared to the reference volumes. The Azure Kinect can effectively count the number of nuts and accurately estimate the volume of nuts of different types and sizes, based on our proposed framework.