With the rapid development of LiDAR and multi-view image reconstruction, three-dimensional (3D) technologies have become an important way to percept and understand the real world for artificial intelligence (AI) related research and applications, such as in movies, games, and driverless cars. In recent years, 3D technology has been gradually introduced into smart agriculture to unleash the geometric and topological potential within the plant sciences. 3D approaches are effective means to depict the difference in morphological and structural characteristics of plants in 3D space and the utilization of spatial resources. However, the complex plant morphology and structure, rich details, serious cross and occlusion, and subtle differences among species and cultivars have brought great challenges to 3D plant studies. 3D plant phenotyping, shoot architecture quantification, digital preservation and protection of germplasm resources, visual display of new cultivars, analysis of light energy utilization efficiency in 3D spaces, digital twin, and agricultural meta-universe all put forward higher requirements for 3D plant approaches.
3D plant studies are expected to promote the research and application of plant morphology and structure in smart agriculture, including 3D plant phenotyping, geometric modeling, and 3D visual computation. Robust and effective point cloud processing approaches have to be developed to better understand the semantic information in 3D data of plants. Accurate, high-throughput and fully automated measuring of plant morphology and structure using 3D data are urgently needed by agronomists. Realistic and efficient reconstruction of plant organs, individual plants, and canopies remains a major challenge of 3D plant approaches. Furthermore, how to accurately compute and analyze the functions of plants on basis of 3D structural models and make them practical remains a difficult problem.
In this Research Topic, we encourage the submissions related to 3D plant techniques and applications, especially those that focus on:
• Point cloud processing techniques of plants, including point cloud segmentation, completion, and semantic understanding
• 3D point cloud, image, and spectral data fusion of plants
• Automatic phenotype extraction approaches for plant morphology and structure
• Interactive design, geometric modeling, and 3D reconstruction of plants
• 3D visual computing and functional-structural plant modeling
With the rapid development of LiDAR and multi-view image reconstruction, three-dimensional (3D) technologies have become an important way to percept and understand the real world for artificial intelligence (AI) related research and applications, such as in movies, games, and driverless cars. In recent years, 3D technology has been gradually introduced into smart agriculture to unleash the geometric and topological potential within the plant sciences. 3D approaches are effective means to depict the difference in morphological and structural characteristics of plants in 3D space and the utilization of spatial resources. However, the complex plant morphology and structure, rich details, serious cross and occlusion, and subtle differences among species and cultivars have brought great challenges to 3D plant studies. 3D plant phenotyping, shoot architecture quantification, digital preservation and protection of germplasm resources, visual display of new cultivars, analysis of light energy utilization efficiency in 3D spaces, digital twin, and agricultural meta-universe all put forward higher requirements for 3D plant approaches.
3D plant studies are expected to promote the research and application of plant morphology and structure in smart agriculture, including 3D plant phenotyping, geometric modeling, and 3D visual computation. Robust and effective point cloud processing approaches have to be developed to better understand the semantic information in 3D data of plants. Accurate, high-throughput and fully automated measuring of plant morphology and structure using 3D data are urgently needed by agronomists. Realistic and efficient reconstruction of plant organs, individual plants, and canopies remains a major challenge of 3D plant approaches. Furthermore, how to accurately compute and analyze the functions of plants on basis of 3D structural models and make them practical remains a difficult problem.
In this Research Topic, we encourage the submissions related to 3D plant techniques and applications, especially those that focus on:
• Point cloud processing techniques of plants, including point cloud segmentation, completion, and semantic understanding
• 3D point cloud, image, and spectral data fusion of plants
• Automatic phenotype extraction approaches for plant morphology and structure
• Interactive design, geometric modeling, and 3D reconstruction of plants
• 3D visual computing and functional-structural plant modeling
