Can a Light Detection and Ranging (LiDAR) and Multispectral Sensor Discriminate Canopy Structure Changes Due to Pruning in Olive Growing? A Field Experimentation

Abstract

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.

Plant phenotyping relevance

LiDARとマルチスペクトルセンサーでオリーブ樹冠の体積・高さ・径・密度を測定し、手動測定および剪定重量との相関で技術検証しているため、植物フェノタイピング手法が中心です。

Code and data availability

The paper's LiDAR/multispectral phenotyping dataset is not publicly deposited; the Data Availability Statement says it is available only upon request from the authors. No author analysis code or public repository URL is provided; the cited VoxR, dplyr, and RStudio links are generic third-party tools, not paper-specific

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