Three‐dimensional phenotyping of peach tree‐crown architecture utilizing terrestrial laser scanning
Three‐dimensional phenotyping of peach tree‐crown architecture utilizing terrestrial laser scanning
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DOI：
10.1002/ppj2.20073
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
J. Knapp‐Wilson;Rafael Bohn Reckziegel;Srijana Thapa Magar;Alexander Bucksch;D. Chavez
中科院分区：
文献类型：
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作者：
J. Knapp‐Wilson;Rafael Bohn Reckziegel;Srijana Thapa Magar;Alexander Bucksch;D. Chavez
在整个历史上，果树专家一直在开发温带水果的整树系统，以提高光截获、果实产量和水果质量。这些训练系统将树冠和树枝的生长引导到特定的配置。量化树冠结构可以帮助选择需要修剪较少或自然在特定生长/整枝系统条件下表现优异的树木。桃树[Prunus persica(L.)在[Batsch]中，已经开发了诸如分枝指数之类的访问工具来表征树冠结构。然而，开发这些指数所需的分支数据(BD)很难收集。传统上，BD都是人工收集的，但这个过程繁琐、耗时，而且容易出现人为错误。这些障碍可以通过利用地面激光扫描(TLS)来绕过，以获得真实树木的数字孪生树。TLS生成树冠的三维(3D)点云，其中每个点都包含3D坐标(x，y，z)。为了便于对桃树使用这些工具，我们选择了16棵在2021年和2022年扫描的桃树幼树。然后使用开源软件TreeQSM对这16棵树进行建模和量化。结果，计算了幼龄桃树的“计算机”分枝和生物测定数据，以证明桃树-树冠结构的TLS表型能力。利用现场测量(现场)和硅胶BD、生物统计学数据和定量结构模型分支不确定度数据的比较和分析，确定重建模型作为现场测量的替代来源的可靠性。幼树高度(YT)比较时的平均偏差约为。5.93%，树冠体积接近。2021年和2022年均为13.26%。2022年YTS的所有点云显示，所有分支的残差小于12毫米到柱面，主干和主要分支的平均表面覆盖率都超过40%。
Tree training systems for temperate fruit have been developed throughout history by pomologists to improve light interception, fruit yield, and fruit quality. These training systems direct crown and branch growth to specific configurations. Quantifying crown architecture could aid the selection of trees that require less pruning or that naturally excel in specific growing/training system conditions. Regarding peaches [Prunus persica (L.) Batsch], access tools such as branching indices have been developed to characterize tree‐crown architecture. However, the required branching data (BD) to develop these indices are difficult to collect. Traditionally, BD have been collected manually, but this process is tedious, time‐consuming, and prone to human error. These barriers can be circumnavigated by utilizing terrestrial laser scanning (TLS) to obtain a digital twin of the real tree. TLS generates three‐dimensional (3D) point clouds of the tree crown, wherein every point contains 3D coordinates (x, y, z). To facilitate the use of these tools for peach, we selected 16 young peach trees scanned in 2021 and 2022. These 16 trees were then modeled and quantified using the open‐source software TreeQSM. As a result, “in silico” branching and biometric data for the young peach trees were calculated to demonstrate the capabilities of TLS phenotyping of peach tree‐crown architecture. The comparison and analysis of field measurements (in situ) and in silico BD, biometric data, and quantitative structural model branch uncertainty data were utilized to determine the reconstructive model's reliability as a source substitute for field measurements. Mean average deviation when comparing young tree (YT) height was approx. 5.93%, with crown volume was approx. 13.26% across both 2021 and 2022. All point clouds of the YTs in 2022 showed residuals lower than 12 mm to cylinders fitted to all branches, and mean surface coverage greater than 40% for both the trunk and primary branching orders.