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Abstract Accurate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracy remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root phenotyping by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding.
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Bird-view 3D Reconstruction for Crops with Repeated Textures
Large-scale in-situ 3D reconstruction of crop fields presents a challenging task, as the 3D crop structures play a crucial role in plant phenotyping and significantly influence crop growth and yield. While existing efforts focus on close range plants, only a limited number of deep learning-based methods have been developed explicitly for large-scale 3D crop reconstruction, mainly due to the scarcity of large-scale crop sensing data. In this paper, we leverage unmanned aerial vehicles (UAVs) in agriculture and utilize a recently captured multiview real-world snap beans crop dataset to develop an unsupervised structure-from-motion (SfM) framework. Our framework is designed specifically for reconstructing large-scale 3D crop structures. It addresses the challenge of inaccurate depth inference caused by excessively repeated patterns in the crop dataset, resulting in highly accurate 3D crop reconstruction for large-scale scenarios. Through experiments conducted on the crop dataset, we demonstrate the accuracy and robustness of our 3D crop reconstruction algorithm. The application of our proposed framework has the potential to advance research in agriculture, enabling better plant phenotyping and understanding of crop growth and yield.
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- PAR ID:
- 10443231
- Date Published:
- Journal Name:
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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