Salter William T, Shrestha Arjina, Barbour Margaret M
School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, NSW, 2570, Brownlow Hill, Australia.
School of Science, University of Waikato, Hillcrest, Hamilton, 3216, New Zealand.
Plant Methods. 2021 Sep 16;17(1):95. doi: 10.1186/s13007-021-00795-6.
Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea.
In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5-10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against "ground truth" measurements, producing R > 0.99 and a mean absolute percentage error < 10%. We demonstrate the ability to assess several important architectural traits, including canopy volume and projected area, and estimate relative growth rate in commercial chickpea cultivars and lines from local and international breeding collections. Detailed analysis of individual reconstructions also allowed us to investigate partitioning of plant surface area, and by proxy plant biomass.
Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.
能够准确评估植物冠层的三维结构可以使我们更好地估计植物生产力，并增进我们对植物潜在过程的理解。如果我们能够在植物整个发育过程中监测这些特征，情况尤其如此。摄影测量技术，如运动结构法，已被证明能够为小麦和水稻等单子叶作物物种提供准确的三维重建，但在重建叶片较小且分支结构更复杂的作物物种（如鹰嘴豆）方面却鲜有成功。
在这项工作中，我们开发了一种低成本的三维扫描仪，并使用开源数据处理管道来评估单个鹰嘴豆植株的三维结构。我们开发的成像系统由一个用户可编程的转盘和三个摄像头组成，该系统能自动捕捉每株植物的120张图像，并将这些图像传输到计算机进行处理。每株植物的捕捉过程需要5 - 10分钟，并且在Windows个人电脑上的大部分重建过程是自动化的。植株高度和总植株表面积与“地面真值”测量值进行了验证，相关系数R > 0.99，平均绝对百分比误差 < 10%。我们展示了评估几个重要结构特征的能力，包括冠层体积和投影面积，并估计了来自本地和国际育种群体的商业鹰嘴豆品种和品系的相对生长速率。对单个重建结果的详细分析还使我们能够研究植物表面积的分配情况，并由此推断植物生物量。
我们的结果表明，使用低成本摄影测量技术准确重建单个鹰嘴豆植株是可行的，鹰嘴豆是一种具有复杂结构的作物，由许多小叶子和高度分支的结构组成。我们希望我们对开源软件和低成本硬件的使用将鼓励其他人将这种有前景的技术用于结构更复杂的物种。
