Perna Carolina, Pagliai Andrea, Sarri Daniele, Lisci Riccardo, Vieri Marco
Department of Agricultural, Alimentary, Environmental and Forestry Sciences, Biosystem Engineering Division-DAGRI, University of Florence, Piazzale delle Cascine 15, 50144 Florence, Italy.
Sensors (Basel). 2024 Dec 10;24(24):7894. doi: 10.3390/s24247894.
The present research aimed to evaluate whether two sensors, optical and laser, could highlight the change in olive trees' canopy structure due to pruning. Therefore, two proximal sensors were mounted on a ground vehicle (Kubota B2420 tractor): a multispectral sensor (OptRx ACS 430 AgLeader) and a 2D LiDAR sensor (Sick TIM 561). The multispectral sensor was used to evaluate the potential effect of biomass variability before pruning on sensor response. The 2D LiDAR was used to assess its ability to discriminate volume before and after pruning. Data were collected in a traditional olive grove located in Tenute di Cesa Farm, in the east of Tuscany, Italy, characterized by a 4x6 m planting layout and by developed plants. LiDAR data were used to measure canopy volumes, height, and diameter, and the generated point cloud was studied to assess the difference in density between treatments. Ten plants were selected for the study. To validate the LiDAR results, manual measurements of the canopy height and diameter dimensions of the plants were taken. The pruning weights of the monitored plants were obtained to assess the correlation with the canopy characterization data. The results obtained showed that pruning did not affect the results of the multispectral sensor, and the potential variation in canopy density and porosity did not lead to different results with this instrument. Plant volumes, height, and diameters calculated with the LiDAR sensor correlated well with the values of manual measurements, while volume differences between before and after pruning obtained good correlations with pruning weights (Pearson correlation coefficient: 0.66-0.83). The study of point cloud density in canopy thickness and height showed different shapes before and after pruning, especially in the former case. Correlations between point cloud density obtained from LiDAR and multispectral sensor results were not statistically significant. Even if more studies are necessary, the results obtained can be of interest in pruning management.
本研究旨在评估光学和激光这两种传感器能否突出修剪导致的橄榄树冠层结构变化。因此，在一辆地面车辆（久保田B2420拖拉机）上安装了两个近端传感器：一个多光谱传感器（OptRx ACS 430 AgLeader）和一个二维激光雷达传感器（西克TIM 561）。多光谱传感器用于评估修剪前生物量变异性对传感器响应的潜在影响。二维激光雷达用于评估其区分修剪前后体积的能力。数据收集于意大利托斯卡纳东部切萨农场的一个传统橄榄园，该橄榄园种植布局为4x6米，植株已发育成熟。激光雷达数据用于测量冠层体积、高度和直径，并研究生成的点云以评估处理之间的密度差异。选择了10株植物进行研究。为了验证激光雷达的结果，对植物的冠层高度和直径尺寸进行了手动测量。获取了被监测植物的修剪重量，以评估其与冠层特征数据的相关性。获得的结果表明，修剪对多光谱传感器的结果没有影响，冠层密度和孔隙率的潜在变化并未导致该仪器得出不同结果。用激光雷达传感器计算的植物体积、高度和直径与手动测量值相关性良好，而修剪前后的体积差异与修剪重量具有良好的相关性（皮尔逊相关系数：0.66 - 0.83）。对冠层厚度和高度的点云密度研究表明，修剪前后形状不同，尤其是在前一种情况下。从激光雷达获得的点云密度与多光谱传感器结果之间的相关性无统计学意义。即使需要更多研究，但所获得的结果在修剪管理方面可能具有参考价值。
