A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs

Calvario, Gabriela; Sierra, Basilio; Alarcón, Teresa E.; Hernández, Carmen Añez; Dalmau, Oscar

doi:10.3390/s17061411

Cited by 19 publications

(37 citation statements)

References 36 publications

(38 reference statements)

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“…The research by Calvario et alRemote Sens. 2019, 11, 1023 3 of 24 on agave control is a good example of the ability of UAV mixing computer vision and data mining technique in precision agriculture [26].Another example is the LABFVC algorithm proposed by Liu [19]. Passing by the L*a*b* color space, the Fractional Vegetation Cover (FVC) is extracted automatically.…”

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confidence: 99%

“…Remote Sens. 2019, 11, 1023 3 of 24 on agave control is a good example of the ability of UAV mixing computer vision and data mining technique in precision agriculture [26].…”

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confidence: 99%

“…K-means parameter was imposed to identify k=5 clusters [26,28,36], considering the mean value 295 of pixels in both the a* and b* channels. The k-means segregates clusters considering their mean 296 value.…”

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confidence: 99%

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Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

et al. 2019

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Technical resources are currently supporting and enhancing the ability of precision agriculture techniques in crop management. The accuracy of prescription maps is a key aspect to ensure a fast and targeted intervention. In this context, remote sensing acquisition by unmanned aerial vehicles (UAV) is one of the most advanced platforms to collect imagery of the field. Besides the imagery acquisition, canopy segmentation among soil, plants and shadows is another practical and technical aspect that must be fast and precise to ensure a targeted intervention. In this paper, algorithms to be applied to UAV imagery are proposed according to the sensor used that could either be visible spectral or multispectral. These algorithms, called HSV-based (Hue, Saturation, Value), DEM (Digital Elevation Model) and K-means, are unsupervised, i.e., they perform canopy segmentation without human support. They were tested and compared in three different scenarios obtained from two vineyards over two years, 2017 and 2018 for RGB (Red-Green-Blue) and NRG (Near Infrared-Red-Green) imagery. Particular attention is given to the unsupervised ability of these algorithms to identify vines in these different acquisition conditions. This ability is quantified by the introduction of over- and under- estimation indexes, which are the algorithm’s ability to over-estimate or under-estimate vine canopies. For RGB imagery, the HSV-based algorithms consistently over-estimate vines, and never under-estimate them. The k-means and DEM method have a similar trend of under-estimation. While for NRG imagery, the HSV is the more stable algorithm and the DEM model slightly over-estimates the vines. HSV-based algorithms and the DEM algorithm have comparable computation time. The k-means algorithm increases computational demand as the quality of the DEM decreases. The algorithms developed can isolate canopy vegetation data, which is useful information about the current vineyard state, and can be used as a tool to be efficiently applied in the crop management procedure within precision viticulture applications.

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confidence: 99%

“…Remote Sens. 2019, 11, 1023 3 of 24 on agave control is a good example of the ability of UAV mixing computer vision and data mining technique in precision agriculture [26].…”

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confidence: 99%

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Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

et al. 2019

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“…The use of UAVs in agriculture [36][37][38]56,57,62], horticulture [51], plant ecology [66,68,89], and forestry [59,61,62,65] is increasing worldwide. However, UAVs were not heretofore used to quantify the flower production of black locust.…”

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confidence: 99%

Robinia pseudoacacia L. Flower Analyzed by Using An Unmanned Aerial Vehicle (UAV)

et al. 2017

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Tree flowers are important for flower-insect relationships, seeds, fruits, and honey production. Flowers are difficult to analyze, particularly in complex ecosystems such as forests. However, unmanned aerial vehicles (UAVs) enable detailed analyses with high spatial resolution, and avoid destruction of sensitive ecosystems. In this study, we hypothesize that UAVs can be used to estimate the number of existing flowers, the quantity of nectar, and habitat potential for honeybees (Apis mellifera). To test this idea, in 2017 we combined UAV image analysis with manual counting and weighing of the flowers of eight-year-old black locust (Robinia pseudoacacia L.) trees to calculate the number of flowers, their surface area, and their volume. Estimates of flower surface area ranged from 2.97 to 0.03% as the flying altitude above the crowns increased from 2.6 m to 92.6 m. Second, for the horizontal analysis, a 133 m 2 flower area at a one-hectare black locust plantation was monitored in 2017 by a UAV. Flower numbers ranged from 1913 to 15,559 per tree with an average surface area of 1.92 cm 2 and average volume of 5.96 cm 3 . The UAV monitored 11% of the total surface and 3% of the total volume. Consequently, at the one-hectare black locust study area we estimate 5.3 million flowers (69 kg honey), which is sufficient for one bee hive to survive for one year.

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“…Data from UAV-borne sensors have been used to provide indirect estimates of biomass, grain yield [22], and nitrogen status [23], and the output of multi-spectral sensors can produce an array of vegetation indices, including the normalized difference of the vegetation index (NDVI) [24]. UAVs are employed to detect and monitor pest infestations [25,26], analyze the structure of vegetation [27], detect nutrient deficiencies, and monitor weed and flower density in crop fields [28][29][30]. Müllerová et al [31] recently demonstrated that UAV imagery could be used to successfully differentiate between flowers of different invasive species.…”

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confidence: 99%

Remotely Estimating Beneficial Arthropod Populations: Implications of a Low-Cost Small Unmanned Aerial System

et al. 2018

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Studies show that agricultural land requires investment in the habitat management of non-cropped areas to support healthy beneficial arthropods and the ecosystem services they provide. In a previous small plot study, we manually counted blooms over the season, and found that plots providing greater numbers of flowers supported significantly higher pollinator populations over that of spontaneous weed plots. Here, we examined the potential of deploying an inexpensive small unmanned aerial vehicle (UAV) as a tool to remotely estimate floral resources and corresponding pollinator populations. Data were collected from previously established native wildflower plots in 19 locations on the University of Georgia experimental farms in South Georgia, USA. A UAV equipped with a lightweight digital camera was deployed to capture images of the flowers during the months of June and September 2017. Supervised image classification using a geographic information system (GIS) was carried out on the acquired images, and classified images were used to evaluate the floral area. The floral area obtained from the images positively correlated with the floral counts gathered from the quadrat samples. Furthermore, the floral area derived from imagery significantly predicted pollinator populations, with a positive correlation indicating that plots with greater area of blooming flowers contained higher numbers of pollinators.

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A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs

Cited by 19 publications

References 36 publications

Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

Robinia pseudoacacia L. Flower Analyzed by Using An Unmanned Aerial Vehicle (UAV)

Remotely Estimating Beneficial Arthropod Populations: Implications of a Low-Cost Small Unmanned Aerial System

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