The efficacy of three-dimensional (3D) point clouds in studying crop morphological structures is based on their direct and accurate data presentation ability. With deep-learning integration, organ segmentation from point clouds could serve as the basis for tremendous advancements in organ-level phenotyping. However, despite the potential, the acquisition of a sufficient number of annotated plant point clouds for practical model training remains a major hurdle. To help overcome this limitation, we constructed a 3D point-cloud dataset specifically for maize stem–leaf segmentation encompassing 428 maize plants ranging from 2 to 12 leaves. We also developed a point cloud enhancement strategy that uses highly controllable deformations to improve the morphological diversity of the training set significantly, while preserving the local geometric features of organs. Our dataset supports the generation of abundant training data from a limited number of labelled data, and we also provide a segmentation framework based on the augmented data to validate the efficiency of our enhancement technique. Two labelled data items were randomly chosen from our plant dataset based on every leaf number, yielding 22 labelled data items total, to produce several deformed point clouds for training the PointNet++ semantic segmentation model, as well as the hierarchical aggregation for the 3D instant segmentation (HAIS) model. These models were tested on 406 datasets, where the PointNet++ model secured a 91.93 % mean intersection-over-union (mIoU) in semantic segmentation and the HAIS model obtained an 89.57 % mean average precision (mAP) in instance segmentation. Following post-processing, an instance segmentation result of 93.74 % mAP was achieved with the HAIS model. These findings demonstrate that our method allows for the efficient training of organ segmentation models with minimal labelled data input in a reduced timeframe. Moreover, it offers an effective tool for point-cloud parsing in maize phenotyping research. Our Maize dataset is available from , and the source code of our method can be found at .

中文翻译：

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基于可变形点云的玉米茎叶分割框架

三维（3D）点云在研究作物形态结构方面的功效基于其直接、准确的数据呈现能力。通过深度学习集成，点云的器官分割可以作为器官水平表型分析巨大进步的基础。然而，尽管有潜力，获取足够数量的带注释的植物点云用于实际模型训练仍然是一个主要障碍。为了帮助克服这一限制，我们构建了一个专门用于玉米茎叶分割的 3D 点云数据集，其中包含 428 株玉米植株，叶子数量从 2 到 12 个不等。我们还开发了一种点云增强策略，该策略使用高度可控的变形来显着提高训练集的形态多样性，同时保留器官的局部几何特征。我们的数据集支持从有限数量的标记数据生成丰富的训练数据，并且我们还提供基于增强数据的分割框架来验证我们增强技术的效率。根据每个叶子的数量，从我们的植物数据集中随机选择两个标记数据项，总共产生 22 个标记数据项，以生成多个变形点云，用于训练 PointNet++ 语义分割模型，以及用于 3D 即时分割的分层聚合（ HAIS）模型。这些模型在 406 个数据集上进行了测试，其中 PointNet++ 模型在语义分割中获得了 91.93% 的平均交集（mIoU），HAIS 模型在实例分割中获得了 89.57% 的平均平均精度（mAP）。经过后处理后，HAIS 模型获得了 93.74% mAP 的实例分割结果。这些发现表明，我们的方法可以在更短的时间内以最少的标记数据输入有效地训练器官分割模型。此外，它为玉米表型研究中的点云解析提供了有效的工具。我们的玉米数据集可从 获得，我们方法的源代码可以在 找到。