Bitella Giovanni, Bochicchio Rocco, Castronuovo Donato, Lovelli Stella, Mercurio Giuseppe, Rivelli Anna Rita, Rosati Leonardo, D'Antonio Paola, Casiero Pierluigi, Laghetti Gaetano, Amato Mariana, Rossi Roberta
School of Agriculture, Forestry, Food and Environmental Sciences, University of Basilicata, 85100 Potenza, Italy.
Department of European and Mediterranean Cultures, Environment, and Cultural Heritage, University of Basilicata, 85100 Potenza, Italy.
Plants (Basel). 2024 Apr 12;13(8):1085. doi: 10.3390/plants13081085.
Measuring canopy height is important for phenotyping as it has been identified as the most relevant parameter for the fast determination of plant mass and carbon stock, as well as crop responses and their spatial variability. In this work, we develop a low-cost tool for measuring plant height proximally based on an ultrasound sensor for flexible use in static or on-the-go mode. The tool was lab-tested and field-tested on crop systems of different geometry and spacings: in a static setting on faba bean ( L.) and in an on-the-go setting on chia ( L.), alfalfa ( L.), and wheat ( Desf.). Cross-correlation (CC) or a dynamic time-warping algorithm (DTW) was used to analyze and correct shifts between manual and sensor data in chia. Sensor data were able to reproduce with minor shifts in canopy profile and plant status indicators in the field when plant heights varied gradually in narrow-spaced chia (R = 0.98), faba bean (R = 0.96), and wheat (R = up to 0.99). Abrupt height changes resulted in systematic errors in height estimation, and short-scale variations were not well reproduced (e.g., R in widely spaced chia was 0.57 to 0.66 after shifting based on CC or DTW, respectively)). In alfalfa, ultrasound data were a better predictor than NDVI (Normalized Difference Vegetation Index) for Leaf Area Index and biomass (R from 0.81 to 0.84). Maps of ultrasound-determined height showed that clusters were useful for spatial management. The good performance of the tool both in a static setting and in the on-the-go setting provides flexibility for the determination of plant height and spatial variation of plant responses in different conditions from natural to managed systems.
测量冠层高度对于表型分析很重要，因为它已被确定为快速测定植物生物量和碳储量以及作物反应及其空间变异性的最相关参数。在这项工作中，我们基于超声波传感器开发了一种低成本的近端植物高度测量工具，可灵活用于静态或移动模式。该工具在不同几何形状和间距的作物系统上进行了实验室测试和田间测试：在蚕豆（L.）的静态环境中以及在奇亚（L.）、苜蓿（L.）和小麦（Desf.）的移动环境中。使用互相关（CC）或动态时间规整算法（DTW）来分析和校正奇亚中手动数据和传感器数据之间的偏移。当窄间距奇亚（R = 0.98）、蚕豆（R = 0.96）和小麦（R高达0.99）的株高逐渐变化时，传感器数据能够在田间以较小的冠层轮廓和植物状态指标偏移进行再现。高度的突然变化导致高度估计出现系统误差，并且短尺度变化没有得到很好的再现（例如，在基于CC或DTW进行偏移后，宽间距奇亚中的R分别为0.57至0.66）。在苜蓿中，超声数据在预测叶面积指数和生物量方面比归一化差异植被指数（NDVI）更好（R为0.81至0.84）。超声测定高度的地图表明，聚类对于空间管理很有用。该工具在静态环境和移动环境中的良好性能为在从自然系统到管理系统的不同条件下测定植物高度和植物反应的空间变异性提供了灵活性
