Ground-Density Clustering for Approximate Agricultural Field Segmentation

Instance and semantic segmentation form the backbone of robotic perception and are crucial to many tasks. While most research in the area focuses on improving segmentation quality metrics, there are plenty of applications where approximate methods are adequate as long as they are fast, especially in applications with large amounts of data like precision agriculture. In order to apply the recent successes of machine learning and computer vision on a large scale using robotics, efficient and general algorithms must be designed to intelligently split point clouds into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper, we capitalize on a similarity between the current state-of-the-art for roughly segmenting corn plants and a commonly used density-based clustering algorithm, Quickshift. Exploiting this similarity we propose a novel algorithm, Ground-Density Quickshift++, with the goal of producing a general and scalable field segmentation algorithm that segments individual plants and their stems. This algorithm produces quantitatively better results than the current state-of-the-art on both plant separation and stem segmentation while being less sensitive to input parameters and maintaining the same algorithmic time complexity. When incorporated into field-scale phenotyping systems, the proposed algorithm should work as a drop-in replacement that can greatly improve the accuracy of results while ensuring that performance and scalability remain undiminished.