Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors
Controlling fire blight in pear production areas depends strongly on regular visual inspections of pome fruit orchards, nurseries and other hosts of <i>Erwinia amylovora</i>. In addition, these inspections play an essential role in delineating fire blight free production areas, which has...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/10/5/615 |
| _version_ | 1797569611930009600 |
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| author | Hilde Schoofs Stephanie Delalieux Tom Deckers Dany Bylemans |
| author_facet | Hilde Schoofs Stephanie Delalieux Tom Deckers Dany Bylemans |
| author_sort | Hilde Schoofs |
| collection | DOAJ |
| description | Controlling fire blight in pear production areas depends strongly on regular visual inspections of pome fruit orchards, nurseries and other hosts of <i>Erwinia amylovora</i>. In addition, these inspections play an essential role in delineating fire blight free production areas, which has important implications for fruit export. However, visual monitoring is labor intensive and time consuming. As a potential alternative, the performance of spectral sensors on unmanned airborne vehicles (UAV) or drones was evaluated, since this allows the monitoring of larger areas compared to the current field inspections. Unlike more traditional remote sensing platforms such as manned aircrafts and satellites, UAVs offer a higher flexibility and an extremely high level of detail. In this project, a UAV platform carrying a hyperspectral COSI-cam camera was used to map a heavily infected pear orchard. The hyperspectral data were used to assess which wavebands contain information on fire blight infections. In this study, wavelengths 611 nm and 784 nm were found appropriate to detect symptoms associated with fire blight. Vegetation indices that allow to discriminate between healthy and infected trees were identified, too. This manuscript highlights the potential use of the UAV methodology in fire blight detection and remaining difficulties that still need to be overcome for the technique to become fully operational in practice. |
| first_indexed | 2024-03-10T20:13:27Z |
| format | Article |
| id | doaj.art-02c86998511b4bfaaf318b79deb75425 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T20:13:27Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-02c86998511b4bfaaf318b79deb754252023-11-19T22:43:24ZengMDPI AGAgronomy2073-43952020-04-0110561510.3390/agronomy10050615Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral SensorsHilde Schoofs0Stephanie Delalieux1Tom Deckers2Dany Bylemans3Pcfruit Research Station for fruit, Fruittuinweg 1, 3800 Sint-Truiden, BelgiumFlemish Institute for Technological Research-VITO NV, Boeretang 200, 2400 Mol, BelgiumPcfruit Research Station for fruit, Fruittuinweg 1, 3800 Sint-Truiden, BelgiumPcfruit Research Station for fruit, Fruittuinweg 1, 3800 Sint-Truiden, BelgiumControlling fire blight in pear production areas depends strongly on regular visual inspections of pome fruit orchards, nurseries and other hosts of <i>Erwinia amylovora</i>. In addition, these inspections play an essential role in delineating fire blight free production areas, which has important implications for fruit export. However, visual monitoring is labor intensive and time consuming. As a potential alternative, the performance of spectral sensors on unmanned airborne vehicles (UAV) or drones was evaluated, since this allows the monitoring of larger areas compared to the current field inspections. Unlike more traditional remote sensing platforms such as manned aircrafts and satellites, UAVs offer a higher flexibility and an extremely high level of detail. In this project, a UAV platform carrying a hyperspectral COSI-cam camera was used to map a heavily infected pear orchard. The hyperspectral data were used to assess which wavebands contain information on fire blight infections. In this study, wavelengths 611 nm and 784 nm were found appropriate to detect symptoms associated with fire blight. Vegetation indices that allow to discriminate between healthy and infected trees were identified, too. This manuscript highlights the potential use of the UAV methodology in fire blight detection and remaining difficulties that still need to be overcome for the technique to become fully operational in practice.https://www.mdpi.com/2073-4395/10/5/615fire blightUAVspectral sensorsprecision agriculture |
| spellingShingle | Hilde Schoofs Stephanie Delalieux Tom Deckers Dany Bylemans Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors Agronomy fire blight UAV spectral sensors precision agriculture |
| title | Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors |
| title_full | Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors |
| title_fullStr | Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors |
| title_full_unstemmed | Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors |
| title_short | Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors |
| title_sort | fire blight monitoring in pear orchards by unmanned airborne vehicles uav systems carrying spectral sensors |
| topic | fire blight UAV spectral sensors precision agriculture |
| url | https://www.mdpi.com/2073-4395/10/5/615 |
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