Monitoring and Prediction of Wild Blueberry Phenology Using a Multispectral Sensor
(1) Background: Research and development in remote sensing have been used to determine and monitor crop phenology. This approach assesses the internal and external changes of the plant. Therefore, the objective of this study was to determine the potential of using a multispectral sensor to predict p...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/334 |
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| author | Kenneth Anku David Percival Mathew Vankoughnett Rajasekaran Lada Brandon Heung |
| author_facet | Kenneth Anku David Percival Mathew Vankoughnett Rajasekaran Lada Brandon Heung |
| author_sort | Kenneth Anku |
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| description | (1) Background: Research and development in remote sensing have been used to determine and monitor crop phenology. This approach assesses the internal and external changes of the plant. Therefore, the objective of this study was to determine the potential of using a multispectral sensor to predict phenology in wild blueberry fields. (2) Method: A UAV equipped with a five-banded multispectral camera was used to collect aerial imagery. Sites consisted of two commercial fields, Lemmon Hill and Kemptown. An RCBD with six replications, four treatments, and a plot size of 6 × 8 m with a 2 m buffer between plots was used. Orthomosaic maps and vegetative indices were generated. (3) Results: There were significant correlations between VIs and growth parameters at different stages. The F4/F5 and F6/F7 stages showed significantly high correlation values among all growth stages. LAI, floral, and vegetative bud stages could be estimated at the tight cluster (F4/F5) and bloom (F6/F7) stages with R<sup>2</sup>/CCC = 0.90/0.84. Variable importance showed that NDVI, ENDVI, GLI, VARI, and GRVI contributed significantly to achieving these predicted values, with NDRE showing low effects. (4) Conclusion: This implies that the F4/F5 and F6/F7 stages are good stages for making phenological predictions and estimations about wild blueberry plants. |
| format | Article |
| id | doaj-art-7711a77f239e436a88850bcaddf5c7ff |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-7711a77f239e436a88850bcaddf5c7ff2025-01-24T13:48:10ZengMDPI AGRemote Sensing2072-42922025-01-0117233410.3390/rs17020334Monitoring and Prediction of Wild Blueberry Phenology Using a Multispectral SensorKenneth Anku0David Percival1Mathew Vankoughnett2Rajasekaran Lada3Brandon Heung4Department of Plant, Food, and Environmental Sciences, Dalhousie University, 50 Pictou Road, Truro, NS B2N 5E3, CanadaDepartment of Plant, Food, and Environmental Sciences, Dalhousie University, 50 Pictou Road, Truro, NS B2N 5E3, CanadaCenter of Geographic Sciences, Nova Scotia Community College, 50 Elliott Road, Halifax, NS B0S 1M0, CanadaDepartment of Plant, Food, and Environmental Sciences, Dalhousie University, 50 Pictou Road, Truro, NS B2N 5E3, CanadaDepartment of Plant, Food, and Environmental Sciences, Dalhousie University, 50 Pictou Road, Truro, NS B2N 5E3, Canada(1) Background: Research and development in remote sensing have been used to determine and monitor crop phenology. This approach assesses the internal and external changes of the plant. Therefore, the objective of this study was to determine the potential of using a multispectral sensor to predict phenology in wild blueberry fields. (2) Method: A UAV equipped with a five-banded multispectral camera was used to collect aerial imagery. Sites consisted of two commercial fields, Lemmon Hill and Kemptown. An RCBD with six replications, four treatments, and a plot size of 6 × 8 m with a 2 m buffer between plots was used. Orthomosaic maps and vegetative indices were generated. (3) Results: There were significant correlations between VIs and growth parameters at different stages. The F4/F5 and F6/F7 stages showed significantly high correlation values among all growth stages. LAI, floral, and vegetative bud stages could be estimated at the tight cluster (F4/F5) and bloom (F6/F7) stages with R<sup>2</sup>/CCC = 0.90/0.84. Variable importance showed that NDVI, ENDVI, GLI, VARI, and GRVI contributed significantly to achieving these predicted values, with NDRE showing low effects. (4) Conclusion: This implies that the F4/F5 and F6/F7 stages are good stages for making phenological predictions and estimations about wild blueberry plants.https://www.mdpi.com/2072-4292/17/2/334unmanned aerial vehiclevegetative indicesmachine learningremote sensing<i>Vaccinium angustifolium</i>leaf area index |
| spellingShingle | Kenneth Anku David Percival Mathew Vankoughnett Rajasekaran Lada Brandon Heung Monitoring and Prediction of Wild Blueberry Phenology Using a Multispectral Sensor Remote Sensing unmanned aerial vehicle vegetative indices machine learning remote sensing <i>Vaccinium angustifolium</i> leaf area index |
| title | Monitoring and Prediction of Wild Blueberry Phenology Using a Multispectral Sensor |
| title_full | Monitoring and Prediction of Wild Blueberry Phenology Using a Multispectral Sensor |
| title_fullStr | Monitoring and Prediction of Wild Blueberry Phenology Using a Multispectral Sensor |
| title_full_unstemmed | Monitoring and Prediction of Wild Blueberry Phenology Using a Multispectral Sensor |
| title_short | Monitoring and Prediction of Wild Blueberry Phenology Using a Multispectral Sensor |
| title_sort | monitoring and prediction of wild blueberry phenology using a multispectral sensor |
| topic | unmanned aerial vehicle vegetative indices machine learning remote sensing <i>Vaccinium angustifolium</i> leaf area index |
| url | https://www.mdpi.com/2072-4292/17/2/334 |
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