Plant height measurement using UAV-based aerial RGB and LiDAR images in soybean
Phenotypic traits like plant height are crucial in assessing plant growth and physiological performance. Manual plant height measurement is labor and time-intensive, low throughput, and error-prone. Hence, aerial phenotyping using aerial imagery-based sensors combined with image processing technique...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1488760/full |
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| author | Lalit Pun Magar Jeremy Sandifer Deepak Khatri Sudip Poudel Suraj KC Buddhi Gyawali Maheteme Gebremedhin Anuj Chiluwal |
| author_facet | Lalit Pun Magar Jeremy Sandifer Deepak Khatri Sudip Poudel Suraj KC Buddhi Gyawali Maheteme Gebremedhin Anuj Chiluwal |
| author_sort | Lalit Pun Magar |
| collection | DOAJ |
| description | Phenotypic traits like plant height are crucial in assessing plant growth and physiological performance. Manual plant height measurement is labor and time-intensive, low throughput, and error-prone. Hence, aerial phenotyping using aerial imagery-based sensors combined with image processing technique is quickly emerging as a more effective alternative to estimate plant height and other morphophysiological parameters. Studies have demonstrated the effectiveness of both RGB and LiDAR images in estimating plant height in several crops. However, there is limited information on their comparison, especially in soybean (Glycine max [L.] Merr.). As a result, there is not enough information to decide on the appropriate sensor for plant height estimation in soybean. Hence, the study was conducted to identify the most effective sensor for high throughput aerial phenotyping to estimate plant height in soybean. Aerial images were collected in a field experiment at multiple time points during soybean growing season using an Unmanned Aerial Vehicle (UAV or drone) equipped with RGB and LiDAR sensors. Our method established the relationship between manually measured plant height and the height obtained from aerial platforms. We found that the LiDAR sensor had a better performance (R2 = 0.83) than the RGB camera (R2 = 0.53) when compared with ground reference height during pod growth and seed filling stages. However, RGB showed more reliability in estimating plant height at physiological maturity when the LiDAR could not capture an accurate plant height measurement. The results from this study contribute to identifying ideal aerial phenotyping sensors to estimate plant height in soybean during different growth stages. |
| format | Article |
| id | doaj-art-51a7e45f340644099557bc211b4b6c08 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-51a7e45f340644099557bc211b4b6c082025-01-30T06:22:52ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011610.3389/fpls.2025.14887601488760Plant height measurement using UAV-based aerial RGB and LiDAR images in soybeanLalit Pun MagarJeremy SandiferDeepak KhatriSudip PoudelSuraj KCBuddhi GyawaliMaheteme GebremedhinAnuj ChiluwalPhenotypic traits like plant height are crucial in assessing plant growth and physiological performance. Manual plant height measurement is labor and time-intensive, low throughput, and error-prone. Hence, aerial phenotyping using aerial imagery-based sensors combined with image processing technique is quickly emerging as a more effective alternative to estimate plant height and other morphophysiological parameters. Studies have demonstrated the effectiveness of both RGB and LiDAR images in estimating plant height in several crops. However, there is limited information on their comparison, especially in soybean (Glycine max [L.] Merr.). As a result, there is not enough information to decide on the appropriate sensor for plant height estimation in soybean. Hence, the study was conducted to identify the most effective sensor for high throughput aerial phenotyping to estimate plant height in soybean. Aerial images were collected in a field experiment at multiple time points during soybean growing season using an Unmanned Aerial Vehicle (UAV or drone) equipped with RGB and LiDAR sensors. Our method established the relationship between manually measured plant height and the height obtained from aerial platforms. We found that the LiDAR sensor had a better performance (R2 = 0.83) than the RGB camera (R2 = 0.53) when compared with ground reference height during pod growth and seed filling stages. However, RGB showed more reliability in estimating plant height at physiological maturity when the LiDAR could not capture an accurate plant height measurement. The results from this study contribute to identifying ideal aerial phenotyping sensors to estimate plant height in soybean during different growth stages.https://www.frontiersin.org/articles/10.3389/fpls.2025.1488760/fullsoybeanplant heighthigh throughput aerial phenotypingunmanned aerial vehiclesRGBlidar |
| spellingShingle | Lalit Pun Magar Jeremy Sandifer Deepak Khatri Sudip Poudel Suraj KC Buddhi Gyawali Maheteme Gebremedhin Anuj Chiluwal Plant height measurement using UAV-based aerial RGB and LiDAR images in soybean Frontiers in Plant Science soybean plant height high throughput aerial phenotyping unmanned aerial vehicles RGB lidar |
| title | Plant height measurement using UAV-based aerial RGB and LiDAR images in soybean |
| title_full | Plant height measurement using UAV-based aerial RGB and LiDAR images in soybean |
| title_fullStr | Plant height measurement using UAV-based aerial RGB and LiDAR images in soybean |
| title_full_unstemmed | Plant height measurement using UAV-based aerial RGB and LiDAR images in soybean |
| title_short | Plant height measurement using UAV-based aerial RGB and LiDAR images in soybean |
| title_sort | plant height measurement using uav based aerial rgb and lidar images in soybean |
| topic | soybean plant height high throughput aerial phenotyping unmanned aerial vehicles RGB lidar |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1488760/full |
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