ReviewJournal of imaging2023
Applications of LiDAR in Agriculture and Future Research Directions.
Review in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- Plant3R: Fusing 3D feature learning with Gaussian splatting to enhance wheat plant 3D reconstruction precision.Plant phenomics (Washington, D.C.) · 2026Article
- Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.Plant communications · 2026Review
- A review of remote sensing-based crop yield estimation: machine learning techniques and environmental, algorithmic, and hardware limitations.Frontiers in plant science · 2026Review
- LiDAR Point Cloud Colourisation Using Multi-Camera Fusion and Low-Light Image Enhancement.Sensors (Basel, Switzerland) · 2025Article
- A Comprehensive Review of Remote Sensing and Artificial Intelligence Integration: Advances, Applications, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Enhancing Instance Segmentation in Agriculture: An Optimized YOLOv8 Solution.Sensors (Basel, Switzerland) · 2025Article
- Insights of freshness phenotype detection for postharvest fruit and vegetables.Plant phenomics (Washington, D.C.) · 2025Review
- A Review of Optical-Based Three-Dimensional Reconstruction and Multi-Source Fusion for Plant Phenotyping.Sensors (Basel, Switzerland) · 2025Review
- Cutting-edge computational approaches to plant phenotyping.Plant molecular biology · 2025Review
- Comparison of 3D and 2D area measurement of acute burn wounds with LiDAR technique and deep learning model.Frontiers in artificial intelligence · 2025Article
- Sensing and Perception in Robotic Weeding: Innovations and Limitations for Digital Agriculture.Sensors (Basel, Switzerland) · 2024Review
- A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture.Sensors (Basel, Switzerland) · 2024Review
- Extrinsic Calibration of Thermal Camera and 3D LiDAR Sensor via Human Matching in Both Modalities during Sensor Setup Movement.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Light detection and ranging (LiDAR) sensors have accrued an ever-increasing presence in the agricultural sector due to their non-destructive mode of capturing data. LiDAR sensors emit pulsed light waves that return to the sensor upon bouncing off surrounding objects. The distances that the pulses travel are calculated by measuring the time for all pulses to return to the source. There are many reported applications of the data obtained from LiDAR in agricultural sectors. LiDAR sensors are widely used to measure agricultural landscaping and topography and the structural characteristics of trees such as leaf area index and canopy volume; they are also used for crop biomass estimation, phenotype characterisation, crop growth, etc. A LiDAR-based system and LiDAR data can also be used to measure spray drift and detect soil properties. It has also been proposed in the literature that crop damage detection and yield prediction can also be obtained with LiDAR data. This review focuses on different LiDAR-based system applications and data obtained from LiDAR in agricultural sectors. Comparisons of aspects of LiDAR data in different agricultural applications are also provided. Furthermore, future research directions based on this emerging technology are also presented in this review.
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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.