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Open AccessArticle

Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images

1
DiSCi, Geography Sec., University of Bologna, Piazza San Giovanni in Monte 2, I-40124 Bologna, Italy
2
Optical Metrology Unit, Bruno Kessler Foundation (FBK), Via Sommarive 18, I-38123 Trento, Italy
3
DICAM, School of Engineering and Architecture, University of Bologna, Viale Risorgimento 2, I-40136 Bologna, Italy
4
SAL Engineering, via Vittorio Veneto 2, I-41124 Modena, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Arko Lucieer, Pablo J. Zarco-Tejada, Uwe Rascher, Georg Bareth, Yoshio Inoue and Prasad S. Thenkabail
Remote Sens. 2015, 7(4), 4026-4047; https://doi.org/10.3390/rs70404026
Received: 24 November 2014 / Revised: 5 February 2015 / Accepted: 23 March 2015 / Published: 2 April 2015
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. View Full-Text
Keywords: unmanned aerial vehicles; vegetation; agriculture; multispectral; photogrammetry; vegetation indices; crops unmanned aerial vehicles; vegetation; agriculture; multispectral; photogrammetry; vegetation indices; crops
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Candiago, S.; Remondino, F.; De Giglio, M.; Dubbini, M.; Gattelli, M. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. Remote Sens. 2015, 7, 4026-4047.

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