ArticleSensors (Basel, Switzerland)2018
Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Advancements in 3D field-crop phenotyping using point clouds: a comparative review of sensor technology, target traits, and challenges under controlled and field conditions.Frontiers in plant science · 2026Review
- Sensor-Centric Intelligent Systems for Soybean Harvest Mechanization in Challenging Agro-Environments of China: A Review.Sensors (Basel, Switzerland) · 2025Review
- Plant height measurement using UAV-based aerial RGB and LiDAR images in soybean.Frontiers in plant science · 2025Article
- A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture.Sensors (Basel, Switzerland) · 2024Review
- Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.Frontiers in plant science · 2024Review
- Calibrating ultrasonic sensor measurements of crop canopy heights: a case study of maize and wheat.Frontiers in plant science · 2024Article
- Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data.Plant methods · 2023Article
- Applications of LiDAR in Agriculture and Future Research Directions.Journal of imaging · 2023Review
- Phenological stage and vegetation index for predicting corn yield under rainfed environments.Frontiers in plant science · 2023Article
- Sprayer boom height measurement in wheat field using ultrasonic sensor: An exploratory study.Frontiers in plant science · 2022Article
- Within and combined season prediction models for perennial ryegrass biomass yield using ground- and air-based sensor data.Frontiers in plant science · 2022Article
- CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements.Biosensors · 2021Article
- Study on the Estimation of Forest Volume Based on Multi-Source Data.Sensors (Basel, Switzerland) · 2021Article
- High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.Frontiers in plant science · 2021Article
- Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm.Plants (Basel, Switzerland) · 2020Review
- Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial.Frontiers in plant science · 2020Article
- Imaging Wheat Canopy Through Stereo Vision: Overcoming the Challenges of the Laboratory to Field Transition for Morphological Features Extraction.Frontiers in plant science · 2020Article
- Deployment of Lidar from a Ground Platform: Customizing a Low-Cost, Information-Rich and User-Friendly Application for Field Phenomics Research.Sensors (Basel, Switzerland) · 2019Article
- Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R² of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R² of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.
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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.