Pseudo-LiDAR Meets Agriculture: Leveraging 3D Monocular Point Cloud Processing for Coffee Beans

Green coffee beans are among the most globally recognized agricultural products. However, the conventional practice of manual sorting has been riddled with inaccuracies due to human error. Recent advances in 3D monocular imaging, which employ monocular depth estimation to generate 3D point clouds, transform cameras into pseudo-LiDAR sensors. This method proved to be more accurate in our study, delivering detailed information about bean size, shape, and potential flaws, thereby presenting a potential foundation for developing a green coffee bean sorting system. In this paper, we propose a more refined pseudo-LiDAR model that is not only simpler but relies solely on a monocular camera with basic calibration parameters. Moreover, our model generates pseudo-LiDAR directly from depth maps created by a single monocular camera. Through our quantitative analysis, we have achieved highly satisfactory results in terms of distance metric similarity and Intersection over Union (IoU), validating the efficiency and precision of our model. To the best of our knowledge, this is the first study that explores the application of LiDAR-based detection algorithms for coffee beans.