Quantifying cereal crop movement through hemispherical video analysis of agricultural plots
Abstract Background Violent movement of crop stems can lead to failure under high winds. Known as lodging, this phenomenon is particularly detrimental to cool-season cereals such as oat, barley, and wheat; contributing to yield and economic losses. Phenotyping the movement of cereal crops in real-ti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-05-01
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| Series: | Plant Methods |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13007-019-0437-5 |
| _version_ | 1817985116368011264 |
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| author | Alexander Q. Susko Peter Marchetto D. Jo Heuschele Kevin P. Smith |
| author_facet | Alexander Q. Susko Peter Marchetto D. Jo Heuschele Kevin P. Smith |
| author_sort | Alexander Q. Susko |
| collection | DOAJ |
| description | Abstract Background Violent movement of crop stems can lead to failure under high winds. Known as lodging, this phenomenon is particularly detrimental to cool-season cereals such as oat, barley, and wheat; contributing to yield and economic losses. Phenotyping the movement of cereal crops in real-time could aid in the breeding and selecting of lodging resistant cereals. Since no methods exist to quantify dynamic, real time plant responses in an agricultural setting, we devised a video analysis protocol to quantify mean frequency and amplitude of plant movement for a 360° field of view camera system. Results We present both the image analysis method for identifying predefined regions of a 2D field design as they appear on 360° field of view video, as well as a signal processing pipeline to quantify movement from time varying color signals from plot canopies within these predefined field regions. We detected significant differences in the natural frequency and amplitude of plant movement from video of 16 cereal cultivars planted in a randomized complete block design on five different windy days. Natural frequencies quantified by this method averaged 1.37 Hz, while over 2.5-fold differences in amplitude within similar frequency ranges were detected across the 16 cereal cultivars. Conclusions This method is sensitive enough to systematically differentiate small frequency and amplitude differences in cultivar movement, and shows promise for investigating the physiological basis for differences in cereal movement and lodging resistance. The relative accuracy of the plot demarcation protocol suggests it could be used for other high-throughput phenotyping applications that require both high image resolution and a large field of view. |
| first_indexed | 2024-04-13T23:54:03Z |
| format | Article |
| id | doaj.art-da2e225ad3bf41d3bcc22c3c4493eb3e |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-04-13T23:54:03Z |
| publishDate | 2019-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-da2e225ad3bf41d3bcc22c3c4493eb3e2022-12-22T02:23:59ZengBMCPlant Methods1746-48112019-05-0115112010.1186/s13007-019-0437-5Quantifying cereal crop movement through hemispherical video analysis of agricultural plotsAlexander Q. Susko0Peter Marchetto1D. Jo Heuschele2Kevin P. Smith3Department of Agronomy and Plant Genetics, University of MinnesotaDepartment of Bioproducts and Biosystems Engineering, University of MinnesotaDepartment of Agronomy and Plant Genetics, University of MinnesotaDepartment of Agronomy and Plant Genetics, University of MinnesotaAbstract Background Violent movement of crop stems can lead to failure under high winds. Known as lodging, this phenomenon is particularly detrimental to cool-season cereals such as oat, barley, and wheat; contributing to yield and economic losses. Phenotyping the movement of cereal crops in real-time could aid in the breeding and selecting of lodging resistant cereals. Since no methods exist to quantify dynamic, real time plant responses in an agricultural setting, we devised a video analysis protocol to quantify mean frequency and amplitude of plant movement for a 360° field of view camera system. Results We present both the image analysis method for identifying predefined regions of a 2D field design as they appear on 360° field of view video, as well as a signal processing pipeline to quantify movement from time varying color signals from plot canopies within these predefined field regions. We detected significant differences in the natural frequency and amplitude of plant movement from video of 16 cereal cultivars planted in a randomized complete block design on five different windy days. Natural frequencies quantified by this method averaged 1.37 Hz, while over 2.5-fold differences in amplitude within similar frequency ranges were detected across the 16 cereal cultivars. Conclusions This method is sensitive enough to systematically differentiate small frequency and amplitude differences in cultivar movement, and shows promise for investigating the physiological basis for differences in cereal movement and lodging resistance. The relative accuracy of the plot demarcation protocol suggests it could be used for other high-throughput phenotyping applications that require both high image resolution and a large field of view.http://link.springer.com/article/10.1186/s13007-019-0437-5High throughput phenotypingImage analysisLodging360 CameraOatWheat |
| spellingShingle | Alexander Q. Susko Peter Marchetto D. Jo Heuschele Kevin P. Smith Quantifying cereal crop movement through hemispherical video analysis of agricultural plots Plant Methods High throughput phenotyping Image analysis Lodging 360 Camera Oat Wheat |
| title | Quantifying cereal crop movement through hemispherical video analysis of agricultural plots |
| title_full | Quantifying cereal crop movement through hemispherical video analysis of agricultural plots |
| title_fullStr | Quantifying cereal crop movement through hemispherical video analysis of agricultural plots |
| title_full_unstemmed | Quantifying cereal crop movement through hemispherical video analysis of agricultural plots |
| title_short | Quantifying cereal crop movement through hemispherical video analysis of agricultural plots |
| title_sort | quantifying cereal crop movement through hemispherical video analysis of agricultural plots |
| topic | High throughput phenotyping Image analysis Lodging 360 Camera Oat Wheat |
| url | http://link.springer.com/article/10.1186/s13007-019-0437-5 |
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