ArticlePlant methods2017
Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling.
Article in Plant methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 126 citations in OpenAlex.
- Improving pear fruit quality without yield loss through 3D point cloud-based estimation of reasonable fruit load.Plant phenomics (Washington, D.C.) · 2026Article
- Tree shape and bearing shoot type jointly drive within-canopy differentiation of fruit quality and mineral element allocation in jujube.Frontiers in plant science · 2026Article
- Can a Light Detection and Ranging (LiDAR) and Multispectral Sensor Discriminate Canopy Structure Changes Due to Pruning in Olive Growing? A Field Experimentation.Sensors (Basel, Switzerland) · 2024Article
- Architectural approach to evaluate the design and management of almond cultivars suitable for super high-density orchards.Frontiers in plant science · 2024Article
- UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies.Plants (Basel, Switzerland) · 2022Article
- Utilization of Spectral Indices for High-Throughput Phenotyping.Plants (Basel, Switzerland) · 2022Review
- Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards.Sensors (Basel, Switzerland) · 2021Article
- Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN.Sensors (Basel, Switzerland) · 2021Article
- Development and Validation of Methodology for Estimating Potato Canopy Structure for Field Crop Phenotyping and Improved Breeding.Frontiers in plant science · 2021Article
- A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information.Sensors (Basel, Switzerland) · 2020Article
- Article
- An efficient RGB-UAV-based platform for field almond tree phenotyping: 3-D architecture and flowering traits.Plant methods · 2019Article
- High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques.Frontiers in plant science · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTree pruning is a costly practice with important implications for crop harvest and nutrition, pest and disease control, soil protection and irrigation strategies. Investigations on tree pruning usually involve tedious on-ground measurements of the primary tree crown dimensions, which also might generate inconsistent results due to the irregular geometry of the trees. As an alternative to intensive field-work, this study shows a innovative procedure based on combining unmanned aerial vehicle (UAV) technology and advanced object-based image analysis (OBIA) methodology for multi-temporal three-dimensional (3D) monitoring of hundreds of olive trees that were pruned with three different strategies (traditional, adapted and mechanical pruning). The UAV images were collected before pruning, after pruning and a year after pruning, and the impacts of each pruning treatment on the projected canopy area, tree height and crown volume of every tree were quantified and analyzed over time.
resultsThe full procedure described here automatically identified every olive tree on the orchard and computed their primary 3D dimensions on the three study dates with high accuracy in the most cases. Adapted pruning was generally the most aggressive treatment in terms of the area and volume (the trees decreased by 38.95 and 42.05% on average, respectively), followed by trees under traditional pruning (33.02 and 35.72% on average, respectively). Regarding the tree heights, mechanical pruning produced a greater decrease (12.15%), and these values were minimal for the other two treatments. The tree growth over one year was affected by the pruning severity and by the type of pruning treatment, i.e., the adapted-pruning trees experienced higher growth than the trees from the other two treatments when pruning intensity was low (<10%), similar to the traditionally pruned trees at moderate intensity (10-30%), and lower than the other trees when the pruning intensity was higher than 30% of the crown volume.
conclusionsCombining UAV-based images and an OBIA procedure allowed measuring tree dimensions and quantifying the impacts of three different pruning treatments on hundreds of trees with minimal field work. Tree foliage losses and annual canopy growth showed different trends as affected by the type and severity of the pruning treatments. Additionally, this technology offers valuable geo-spatial information for designing site-specific crop management strategies in the context of precision agriculture, with the consequent economic and environmental benefits. GRAPHICAL ABSTRACT:
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.