Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley
| Autor: | José Luis Araus, Omar Vergara-Díaz, Rubén Vicente, Antonio Lopez, Shawn C. Kefauver, James P. E. Melichar, Maria Dolors Serret Molins, Samir Kerfal, Jose A. Fernandez-Gallego |
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| Přispěvatelé: | Universitat de Barcelona |
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
Nitrogen Computer science multispectral UAV Multispectral image Plant Science lcsh:Plant culture 01 natural sciences nitrogen Bottleneck thermal Barley lcsh:SB1-1110 Image analysis Throughput (business) Original Research Hordeum vulgare Remote sensing RGB Data processing Data collection Ordi 04 agricultural and veterinary sciences hybrid barley Agronomy Data extraction 040103 agronomy & agriculture 0401 agriculture forestry and fisheries vegetation index 010606 plant biology & botany |
| Zdroj: | Frontiers in Plant Science, Vol 8 (2017) Recercat. Dipósit de la Recerca de Catalunya instname Frontiers in Plant Science Dipòsit Digital de la UB Universidad de Barcelona |
| ISSN: | 1664-462X |
| DOI: | 10.3389/fpls.2017.01733 |
| Popis: | With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively. |
| Databáze: | OpenAIRE |
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