Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials
Close remote sensing approaches can be used for high throughput on-field phenotyping in the context of plant breeding and biological research. Data on canopy cover (CC) and canopy height (CH) and their temporal changes throughout the growing season can yield information about crop growth and perform...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/10/1644 |
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| author | Irene Borra-Serrano Tom De Swaef Paul Quataert Jonas Aper Aamir Saleem Wouter Saeys Ben Somers Isabel Roldán-Ruiz Peter Lootens |
| author_facet | Irene Borra-Serrano Tom De Swaef Paul Quataert Jonas Aper Aamir Saleem Wouter Saeys Ben Somers Isabel Roldán-Ruiz Peter Lootens |
| author_sort | Irene Borra-Serrano |
| collection | DOAJ |
| description | Close remote sensing approaches can be used for high throughput on-field phenotyping in the context of plant breeding and biological research. Data on canopy cover (CC) and canopy height (CH) and their temporal changes throughout the growing season can yield information about crop growth and performance. In the present study, sigmoid models were fitted to multi-temporal CC and CH data obtained using RGB imagery captured with a drone for a broad set of soybean genotypes. The Gompertz and Beta functions were used to fit CC and CH data, respectively. Overall, 90.4% fits for CC and 99.4% fits for CH reached an adjusted R<sup>2</sup> > 0.70, demonstrating good performance of the models chosen. Using these growth curves, parameters including maximum absolute growth rate, early vigor, maximum height, and senescence were calculated for a collection of soybean genotypes. This information was also used to estimate seed yield and maturity (R8 stage) (adjusted R<sup>2</sup> = 0.51 and 0.82). Combinations of parameter values were tested to identify genotypes with interesting traits. An integrative approach of fitting a curve to a multi-temporal dataset resulted in biologically interpretable parameters that were informative for relevant traits. |
| first_indexed | 2024-03-10T19:41:31Z |
| format | Article |
| id | doaj.art-24b7b5aadf7a480eb65d554534b3c397 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:41:31Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-24b7b5aadf7a480eb65d554534b3c3972023-11-20T01:09:15ZengMDPI AGRemote Sensing2072-42922020-05-011210164410.3390/rs12101644Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field TrialsIrene Borra-Serrano0Tom De Swaef1Paul Quataert2Jonas Aper3Aamir Saleem4Wouter Saeys5Ben Somers6Isabel Roldán-Ruiz7Peter Lootens8Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Caritasstraat 39, 9090 Melle, BelgiumFlanders Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Caritasstraat 39, 9090 Melle, BelgiumFlanders Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Caritasstraat 39, 9090 Melle, BelgiumFlanders Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Caritasstraat 39, 9090 Melle, BelgiumFlanders Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Caritasstraat 39, 9090 Melle, BelgiumKU Leuven, Department of Biosystems, MeBios, Kasteelpark Arenberg 30, 3001 Leuven, BelgiumKU Leuven, Division of Forest, Nature and Landscape, Celestijnenlaan 200E, 3001 Leuven, BelgiumFlanders Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Caritasstraat 39, 9090 Melle, BelgiumFlanders Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Caritasstraat 39, 9090 Melle, BelgiumClose remote sensing approaches can be used for high throughput on-field phenotyping in the context of plant breeding and biological research. Data on canopy cover (CC) and canopy height (CH) and their temporal changes throughout the growing season can yield information about crop growth and performance. In the present study, sigmoid models were fitted to multi-temporal CC and CH data obtained using RGB imagery captured with a drone for a broad set of soybean genotypes. The Gompertz and Beta functions were used to fit CC and CH data, respectively. Overall, 90.4% fits for CC and 99.4% fits for CH reached an adjusted R<sup>2</sup> > 0.70, demonstrating good performance of the models chosen. Using these growth curves, parameters including maximum absolute growth rate, early vigor, maximum height, and senescence were calculated for a collection of soybean genotypes. This information was also used to estimate seed yield and maturity (R8 stage) (adjusted R<sup>2</sup> = 0.51 and 0.82). Combinations of parameter values were tested to identify genotypes with interesting traits. An integrative approach of fitting a curve to a multi-temporal dataset resulted in biologically interpretable parameters that were informative for relevant traits.https://www.mdpi.com/2072-4292/12/10/1644<i>Glycine max</i>RGBcanopy covercanopy heightclose remote sensinggrowth model |
| spellingShingle | Irene Borra-Serrano Tom De Swaef Paul Quataert Jonas Aper Aamir Saleem Wouter Saeys Ben Somers Isabel Roldán-Ruiz Peter Lootens Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials Remote Sensing <i>Glycine max</i> RGB canopy cover canopy height close remote sensing growth model |
| title | Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials |
| title_full | Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials |
| title_fullStr | Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials |
| title_full_unstemmed | Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials |
| title_short | Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials |
| title_sort | closing the phenotyping gap high resolution uav time series for soybean growth analysis provides objective data from field trials |
| topic | <i>Glycine max</i> RGB canopy cover canopy height close remote sensing growth model |
| url | https://www.mdpi.com/2072-4292/12/10/1644 |
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