Abstract
Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.
Highlights
Studying growth processes of plants plays an essential role in modern agriculture and has a long history in research
We provide a multi-temporal dataset of 3D point clouds of maize (7 plants, 12 days) and tomato plants (7 plants, 20 days)
The contribution of this paper is a large and freely available dataset featuring highly accurate and registered point clouds of 7 maize and 7 tomato plants collected on different days containing approximately 260 million 3D points, scanned at high frequency and precision
Summary
Studying growth processes of plants plays an essential role in modern agriculture and has a long history in research. One can generate even more useful information by monitoring the plants and the abovementioned traits over time to analyze plant growth and health [4, 6, 7] Such applications mostly rely on high-resolution point clouds of a certain number of plants measured at different times. Some advanced plant analysis methods require annotated training data which is time-consuming and requires skilled annotators To address these issues, we provide a multi-temporal dataset of 3D point clouds of maize (7 plants, 12 days) and tomato plants (7 plants, 20 days). The contribution of this paper is a large and freely available dataset featuring highly accurate and registered point clouds of 7 maize and 7 tomato plants collected on different days containing approximately 260 million 3D points, scanned at high frequency and precision. We calculate phenotypic traits to underline the capability of the dataset to track plant organs over time based on its traits such as leaf length
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