3D Reconstruction-Based Seed Counting of Sorghum Panicles for Agricultural Inspection
This addresses the need for precise phenotyping in sorghum breeding experiments, offering a domain-specific incremental improvement over existing methods.
The paper tackles the problem of accurately counting seeds in sorghum panicles for agricultural phenotyping by developing a 3D reconstruction method that uses seeds as semantic landmarks, resulting in improved seed count and weight estimates compared to 2D image-based approaches.
In this paper, we present a method for creating high-quality 3D models of sorghum panicles for phenotyping in breeding experiments. This is achieved with a novel reconstruction approach that uses seeds as semantic landmarks in both 2D and 3D. To evaluate the performance, we develop a new metric for assessing the quality of reconstructed point clouds without having a ground-truth point cloud. Finally, a counting method is presented where the density of seed centers in the 3D model allows 2D counts from multiple views to be effectively combined into a whole-panicle count. We demonstrate that using this method to estimate seed count and weight for sorghum outperforms count extrapolation from 2D images, an approach used in most state of the art methods for seeds and grains of comparable size.