Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images
Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying cr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/7/4/4026 |
| _version_ | 1798024457333243904 |
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| author | Sebastian Candiago Fabio Remondino Michaela De Giglio Marco Dubbini Mario Gattelli |
| author_facet | Sebastian Candiago Fabio Remondino Michaela De Giglio Marco Dubbini Mario Gattelli |
| author_sort | Sebastian Candiago |
| collection | DOAJ |
| description | Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying crop health. This paper reports some experiences related to the analysis of cultivations (vineyards and tomatoes) with Tetracam multispectral data. The Tetracam camera was mounted on a multi-rotor hexacopter. The multispectral data were processed with a photogrammetric pipeline to create triband orthoimages of the surveyed sites. Those orthoimages were employed to extract some Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Soil Adjusted Vegetation Index (SAVI), examining the vegetation vigor for each crop. The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications. |
| first_indexed | 2024-04-11T18:03:53Z |
| format | Article |
| id | doaj.art-278e537f4dab426091b1fae9f2f76816 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:03:53Z |
| publishDate | 2015-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-278e537f4dab426091b1fae9f2f768162022-12-22T04:10:23ZengMDPI AGRemote Sensing2072-42922015-04-01744026404710.3390/rs70404026rs70404026Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV ImagesSebastian Candiago0Fabio Remondino1Michaela De Giglio2Marco Dubbini3Mario Gattelli4DiSCi, Geography Sec., University of Bologna, Piazza San Giovanni in Monte 2, I-40124 Bologna, ItalyOptical Metrology Unit, Bruno Kessler Foundation (FBK), Via Sommarive 18, I-38123 Trento, ItalyDICAM, School of Engineering and Architecture, University of Bologna, Viale Risorgimento 2, I-40136 Bologna, ItalyDiSCi, Geography Sec., University of Bologna, Piazza San Giovanni in Monte 2, I-40124 Bologna, ItalySAL Engineering, via Vittorio Veneto 2, I-41124 Modena, ItalyUnmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying crop health. This paper reports some experiences related to the analysis of cultivations (vineyards and tomatoes) with Tetracam multispectral data. The Tetracam camera was mounted on a multi-rotor hexacopter. The multispectral data were processed with a photogrammetric pipeline to create triband orthoimages of the surveyed sites. Those orthoimages were employed to extract some Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Soil Adjusted Vegetation Index (SAVI), examining the vegetation vigor for each crop. The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications.http://www.mdpi.com/2072-4292/7/4/4026unmanned aerial vehiclesvegetationagriculturemultispectralphotogrammetryvegetation indicescrops |
| spellingShingle | Sebastian Candiago Fabio Remondino Michaela De Giglio Marco Dubbini Mario Gattelli Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images Remote Sensing unmanned aerial vehicles vegetation agriculture multispectral photogrammetry vegetation indices crops |
| title | Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images |
| title_full | Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images |
| title_fullStr | Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images |
| title_full_unstemmed | Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images |
| title_short | Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images |
| title_sort | evaluating multispectral images and vegetation indices for precision farming applications from uav images |
| topic | unmanned aerial vehicles vegetation agriculture multispectral photogrammetry vegetation indices crops |
| url | http://www.mdpi.com/2072-4292/7/4/4026 |
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