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

By Sebastian Candiago, Fabio Remondino, Michaela De Giglio, Marco Dubbini, Mario Gattelli. Remote Sensing, 2015

707 citations recorded by the source corpus.

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.

Subfields: Ecology, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/7b2e28bec2b8a2708103865524a4a19b957e3b91

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Citations in the corpus

Agricultural remote sensing big data: Management and applications (2018, background): “How to rapidly and effectively process and apply the data acquired from UAV agricultural remote sensing platforms is being studied widely at present (Huang et al. 2013b; Suomalainen et al. 2014; Candiago et al. 2015).”
Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform (2019, methodology): “Candiago et al., 2015, used mosaic multispectral images for extracting vegetation indices, and the growing condition of the whole farm was obtained and evaluated.”
Applications of Remote Sensing in Precision Agriculture: A Review (2020, background): “However, images acquired from UAVs are likely to offer a low-cost alternative for small farm operations [272].”
Precision Agriculture Techniques and Practices: From Considerations to Applications (2019, methodology): “In [25], the ESAFLY A2500_WH helicopter was used to implement the platform of the UAV with Tetra cam ADC Micro as the camera to capture hyper-spectral images of two different types of cultivation, i.e., vineyard and tomato.”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, positive): “In [113], [118], [136], [137], most of the VIs found in the literature have been summarized and discussed.”
RELIABILITY OF NDVI DERIVED BY HIGH RESOLUTION SATELLITE AND UAV COMPARED TO IN-FIELD METHODS FOR THE EVALUATION OF EARLY CROP N STATUS AND GRAIN YIELD IN WHEAT (2017, background): “…calculated after a calibration based on different approaches as single or multiple reflectance calibration targets and incident light sensors (Candiago et al., 2015; Honkavaara et al., 2014; Saberioon et al., 2014) Winter wheat is a typical crop where the N rate is split in at least two…”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, background): “…to the advantages of low cost, fast frame imaging and high work efficiency; however, they are limited by the low number of bands, low spectral resolution, and discontinuous spectrum (Berni et al., 2009b; Issei et al., 2010; Overgaard et al., 2010; Diaz-Varela et al., 2014; Candiago et al., 2015).”