A new 3D vision-based leaf rolling index (LRI) and its application as a stable indicator of cotton drought stress
The leaf rolling index (LRI) is a phenotype with significant physiological implications under drought stress. However, research on the quantification of the cotton LRI is lacking, limiting its application in drought diagnosis, irrigation guidance, and physiological assessments. This study conducted...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Agricultural Water Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424005109 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850110595140943872 |
|---|---|
| author | Hangxing Huang Jian Kang Jinliang Chen Risheng Ding Hongna Lu Siyu Wu Shaozhong Kang |
| author_facet | Hangxing Huang Jian Kang Jinliang Chen Risheng Ding Hongna Lu Siyu Wu Shaozhong Kang |
| author_sort | Hangxing Huang |
| collection | DOAJ |
| description | The leaf rolling index (LRI) is a phenotype with significant physiological implications under drought stress. However, research on the quantification of the cotton LRI is lacking, limiting its application in drought diagnosis, irrigation guidance, and physiological assessments. This study conducted a 3D reconstruction of cotton using Structure from Motion (SFM) and Multi-View Stereo (MVS). Algorithms for leaf point cloud preprocessing and phenotype extraction were developed using the PCL point cloud library and integrated into software to calculate the leaf area and perimeter. The LRI was quantified in 3D space based on the point cloud area ratio. On this basis, we analyze the relationships between LRI and leaf physiological indicators such as leaf water potential (LWP), relative water content (RWC), stomatal conductance (gs), and electron transport rate (ETR) at the seedling and flowering stages. The results indicate that the cotton LRI provides a stable indicator of drought stress, which is mainly reflected in the stable correlation between the LRI and water physiological parameters (LWP, and RWC), with coefficients of determination (R²) exceeding 0.70. Furthermore, the correlation between the LRI and the ETR suggests that the LRI could be used to assess photosynthetic efficiency under drought stress. This study demonstrates that LRI based on 3D vision in cotton may serve as a reliable morphological indicator for indicating drought stress and evaluating photosynthetic efficiency. |
| format | Article |
| id | doaj-art-a26ca3d8139449fe8ba2472ae8bc6993 |
| institution | OA Journals |
| issn | 1873-2283 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-a26ca3d8139449fe8ba2472ae8bc69932025-08-20T02:37:48ZengElsevierAgricultural Water Management1873-22832024-12-0130610917410.1016/j.agwat.2024.109174A new 3D vision-based leaf rolling index (LRI) and its application as a stable indicator of cotton drought stressHangxing Huang0Jian Kang1Jinliang Chen2Risheng Ding3Hongna Lu4Siyu Wu5Shaozhong Kang6State Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing 100083, China; National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture, Wuwei 733009, China; Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, ChinaState Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing 100083, China; National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture, Wuwei 733009, China; Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, ChinaState Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing 100083, China; National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture, Wuwei 733009, China; Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, ChinaState Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing 100083, China; National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture, Wuwei 733009, China; Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, ChinaState Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing 100083, China; National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture, Wuwei 733009, China; Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, ChinaState Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing 100083, China; National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture, Wuwei 733009, China; Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, ChinaState Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing 100083, China; National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture, Wuwei 733009, China; Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China; Corresponding author at: State Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing 100083, China.The leaf rolling index (LRI) is a phenotype with significant physiological implications under drought stress. However, research on the quantification of the cotton LRI is lacking, limiting its application in drought diagnosis, irrigation guidance, and physiological assessments. This study conducted a 3D reconstruction of cotton using Structure from Motion (SFM) and Multi-View Stereo (MVS). Algorithms for leaf point cloud preprocessing and phenotype extraction were developed using the PCL point cloud library and integrated into software to calculate the leaf area and perimeter. The LRI was quantified in 3D space based on the point cloud area ratio. On this basis, we analyze the relationships between LRI and leaf physiological indicators such as leaf water potential (LWP), relative water content (RWC), stomatal conductance (gs), and electron transport rate (ETR) at the seedling and flowering stages. The results indicate that the cotton LRI provides a stable indicator of drought stress, which is mainly reflected in the stable correlation between the LRI and water physiological parameters (LWP, and RWC), with coefficients of determination (R²) exceeding 0.70. Furthermore, the correlation between the LRI and the ETR suggests that the LRI could be used to assess photosynthetic efficiency under drought stress. This study demonstrates that LRI based on 3D vision in cotton may serve as a reliable morphological indicator for indicating drought stress and evaluating photosynthetic efficiency.http://www.sciencedirect.com/science/article/pii/S0378377424005109Crop phenotype3D reconstructionWater diagnosisPhotosynthetic metabolism |
| spellingShingle | Hangxing Huang Jian Kang Jinliang Chen Risheng Ding Hongna Lu Siyu Wu Shaozhong Kang A new 3D vision-based leaf rolling index (LRI) and its application as a stable indicator of cotton drought stress Agricultural Water Management Crop phenotype 3D reconstruction Water diagnosis Photosynthetic metabolism |
| title | A new 3D vision-based leaf rolling index (LRI) and its application as a stable indicator of cotton drought stress |
| title_full | A new 3D vision-based leaf rolling index (LRI) and its application as a stable indicator of cotton drought stress |
| title_fullStr | A new 3D vision-based leaf rolling index (LRI) and its application as a stable indicator of cotton drought stress |
| title_full_unstemmed | A new 3D vision-based leaf rolling index (LRI) and its application as a stable indicator of cotton drought stress |
| title_short | A new 3D vision-based leaf rolling index (LRI) and its application as a stable indicator of cotton drought stress |
| title_sort | new 3d vision based leaf rolling index lri and its application as a stable indicator of cotton drought stress |
| topic | Crop phenotype 3D reconstruction Water diagnosis Photosynthetic metabolism |
| url | http://www.sciencedirect.com/science/article/pii/S0378377424005109 |
| work_keys_str_mv | AT hangxinghuang anew3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT jiankang anew3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT jinliangchen anew3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT rishengding anew3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT hongnalu anew3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT siyuwu anew3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT shaozhongkang anew3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT hangxinghuang new3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT jiankang new3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT jinliangchen new3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT rishengding new3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT hongnalu new3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT siyuwu new3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress AT shaozhongkang new3dvisionbasedleafrollingindexlrianditsapplicationasastableindicatorofcottondroughtstress |