Abstract
Crop biomass, a critical indicator of plant growth, health, and productivity, is invaluable for crop breeding programs and agronomic research. However, the accurate and scalable quantification of crop biomass remains inaccessible due to limitations in existing measurement methods. One of the obstacles impeding the advancement of current crop biomass prediction methodologies is the scarcity of publicly available datasets. Addressing this gap, we introduce a new dataset in this domain, i.e. Multi-modality dataset for crop biomass estimation (MMCBE). Comprising 216 sets of multi-view drone images, coupled with LiDAR point clouds, and hand-labelled ground truth, MMCBE represents the first multi-modality one in the field. This dataset aims to establish benchmark methods for crop biomass quantification and foster the development of vision-based approaches. We have rigorously evaluated state-of-the-art crop biomass estimation methods using MMCBE and ventured into additional potential applications, such as 3D crop reconstruction from drone imagery and novel-view rendering. With this publication, we are making our comprehensive dataset available to the broader community.
Abstract (translated)
农作物生物质，作为植物生长、健康状况和生产力的关键指标，对于农作物育种项目和农业研究具有巨大的价值。然而，由于现有测量方法的局限性，准确且可扩展地量化农作物生物质的仍然无法实现。阻碍当前农作物生物质预测方法进步的一个障碍是公共数据资源的稀少。为了解决这一缺口，我们在该领域引入了一个新的数据集，即多模态生物质估计数据集（MMCBE）。MMCBE由216个多视角无人机图像组成，与激光雷达点云和手动标注的地面真实数据集相结合，代表了该领域中的第一个多模态数据集。这个数据集旨在为农作物生物质计量建立基准方法，并推动基于视觉方法的开发。我们使用MMBCE对最先进的农作物生物质估计方法进行了严格的评估，并探索了其他潜在应用，如从无人机影像进行的三维农作物重建和基于新视角渲染。通过这一出版物，我们将全面的 dataset 提供给更广泛的社区。
URL
https://arxiv.org/abs/2404.11256
