Monitoring the effects of weed management strategies on tree canopy structure and growth using UAV-LiDAR in a young almond orchard

Abstract

The primary objective of this study was to assess the potential effect of integrated weed management (IWM) on canopy structure and growth in a young almond orchard using unmanned aerial vehicle (UAV) LiDAR point cloud data. The experiment took place in the Neve Ya’ar Model Farm, with four IWM strategies tested: (1) standard herbicide, (2) physical-mechanical, (3) cover crops, and (4) integrated management combining herbicide and mowing. One pre-treatment sessions in 2019 and two experiment sessions in 2020–2021 were conducted. During these three experimental sessions, manual measurements, including trunk diameter (TD), were taken for all trees in the orchard. Six canopy growth parameters (e.g., tree height and volume), and functionality parameters (e.g., leaf density, gap fraction profile (GFP) and entropy) were calculated for individual trees using UAV-LiDAR data and were compared to manual measurement (i.e., TD). The results indicated that the herbicide treatment enabled effectively maintained weed coverage at approximately 5–10 % throughout the experiment, demonstrating successful control over weed growth. In contrast, the integrated weed management strategy yielded the highest weed cover (∼50 %). With the view of replacing ground manual measurements with UAV-LiDAR-derived measurements, we explored the relationships between TD and UAV-LiDAR-derived volume and GFP. The relationships between TD and GFP were with R2 values of 0.43 and 0.57 for 2021, respectively. The relationship between GFP and volume showed an R2 value of 0.65 for both years. We observed significant differences between IWM for TD, volume, and GFP in both years, indicating on higher tree development in the herbicide and integrated management treatments. The standard herbicide-based management yielded the highest crop yield, while the cover crop strategy yielded the lowest per plot. Finally, random forest (RF) model was used to identify key factors that significantly influence tree volume, TD, and GFP. The RF results showed that IWM explained about 30 % of tree structural changes, while the rest were attributed to environmental factors (i.e., topographical and climatic). The RF model R2 values ranged between 0.79 and 0.86, representing the topographical and climatic effects on tree structural parameters and growth, reflecting the natural field variability. Our study designed and successfully implemented a framework for estimating canopy characterization in young almond orchards subjected to varied IWM treatments. Ensuring the precise calibration of the LiDAR system and validating data, especially in complex canopy structures, can be challenging since the accuracy of ground-truth data has its errors. For future study, the fusion of LiDAR data with multispectral imagery or terrestrial LiDAR data can be considered, as well as more frequent UAV-LiDAR re-visits, offering insights into phenological states and higher resolution of canopy characteristics in orchards. This research enhances our understanding of the advantages and challenges of using UAV-LiDAR point cloud data analysis to assess the impact of IWM on young almond tree growth.