Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS
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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/18/11/3731 |
| _version_ | 1797999165455728640 |
|---|---|
| author | Wenan Yuan Jiating Li Madhav Bhatta Yeyin Shi P. Stephen Baenziger Yufeng Ge |
| author_facet | Wenan Yuan Jiating Li Madhav Bhatta Yeyin Shi P. Stephen Baenziger Yufeng Ge |
| author_sort | Wenan Yuan |
| collection | DOAJ |
| description | 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 R<sup>2</sup> of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R<sup>2</sup> 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. |
| first_indexed | 2024-04-11T11:00:17Z |
| format | Article |
| id | doaj.art-f7ad07902bfb43519eafbe75734f512d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:00:17Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f7ad07902bfb43519eafbe75734f512d2022-12-22T04:28:40ZengMDPI AGSensors1424-82202018-11-011811373110.3390/s18113731s18113731Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UASWenan Yuan0Jiating Li1Madhav Bhatta2Yeyin Shi3P. Stephen Baenziger4Yufeng Ge5Biological Systems Engineering Department, University of Nebraska–Lincoln, Lincoln, NE 68503, USABiological Systems Engineering Department, University of Nebraska–Lincoln, Lincoln, NE 68503, USADepartment of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68503, USABiological Systems Engineering Department, University of Nebraska–Lincoln, Lincoln, NE 68503, USADepartment of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68503, USABiological Systems Engineering Department, University of Nebraska–Lincoln, Lincoln, NE 68503, USAAs 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 R<sup>2</sup> of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R<sup>2</sup> 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.https://www.mdpi.com/1424-8220/18/11/3731cropplant breedingphenotypingproximal sensingremote sensing |
| spellingShingle | Wenan Yuan Jiating Li Madhav Bhatta Yeyin Shi P. Stephen Baenziger Yufeng Ge Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS Sensors crop plant breeding phenotyping proximal sensing remote sensing |
| title | Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS |
| title_full | Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS |
| title_fullStr | Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS |
| title_full_unstemmed | Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS |
| title_short | Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS |
| title_sort | wheat height estimation using lidar in comparison to ultrasonic sensor and uas |
| topic | crop plant breeding phenotyping proximal sensing remote sensing |
| url | https://www.mdpi.com/1424-8220/18/11/3731 |
| work_keys_str_mv | AT wenanyuan wheatheightestimationusinglidarincomparisontoultrasonicsensoranduas AT jiatingli wheatheightestimationusinglidarincomparisontoultrasonicsensoranduas AT madhavbhatta wheatheightestimationusinglidarincomparisontoultrasonicsensoranduas AT yeyinshi wheatheightestimationusinglidarincomparisontoultrasonicsensoranduas AT pstephenbaenziger wheatheightestimationusinglidarincomparisontoultrasonicsensoranduas AT yufengge wheatheightestimationusinglidarincomparisontoultrasonicsensoranduas |