Application of remote sensing by unmanned aerial vehicles to map variability in Ontario ‘Riesling’ and ‘Cabernet franc’ vineyards

Reynolds, Andrew G.; Brown, Ralph B.; Jollineau, Marilyne; Shemrock, Adam; Kotsaki, Elena; Lee, H. S.; Wu, Zheng

doi:10.17660/actahortic.2017.1188.10

Cited by 2 publications

(4 citation statements)

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“…The applications of remote sensing [4] to agriculture include crop type (i.e., plant species) mapping; crop condition assessment; crop yield estimation; mapping of soil parameters and crop operations (e.g., tillage methods); monitoring the compliance of crop operations to EU regulations; and directives, e.g., "Farm to Fork" and European Green Deal [5][6][7][8][9][10]. The images remotely sensed from Unmanned Aerial Vehicles (UAVs) [3,11,12] or Remotely Piloted Aerial Systems (RPASs) [13], aircrafts [3] and satellites [5,[14][15][16][17][18][19] are used as mapping tools in order to classify crops and examine their health and viability, as well as monitor crop operations.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, if a UAV or aircraft is used for remote sensing of within-field crop and soil parameters, it is necessary to equip it with a multispectral camera and a GNSS (e.g., GPS, GLONASS and EGNOS) mobile receiver [20], as well as to use a proper testing protocol [3,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Among several vegetation indexes that can be calculated, NDVI (Normalised Difference Vegetation Index) and NDWI (Normalised Difference Water Index) have some practical advantages. In fact, NDVI can be applied to evaluate the plant photosynthetic activity and, therefore, its vegetative vigour and soil coverage during growth phases, so that it is measured by optical sensors (e.g., Arvatec OptRx and Trimble GreenSeeker) for planning fertilisation [12,[32][33][34][35][36][37] and irrigation. Therefore, NDVI mapping remains the most affordable way to easily and objectively obtain vineyard spatial information [3], as this index has proved to be a useful tool for monitoring crop yield [18] and table grape quality characteristics [14].…”

Section: Introductionmentioning

confidence: 99%

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Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Comparetti

Silva

2022

Sustainability

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Satellites can be used for producing maps of within-field crop and soil parameters and, consequentially, spatially variable rate crop input application maps. The plant vegetative vigour index (i.e., Normalised Difference Vegetation Index—NDVI) and the leaf water content index (i.e., Normalised Difference Water Index—NDWI) maps were used to study—through both time and space—the phenological phases of two plots, with Syrah and Nero d’Avola grapevine varieties, in a Sicilian vineyard farm, located in Naro (Agrigento, Sicily, Italy). The aim of this work is to produce spatially variable rate nitrogen fertiliser maps to be applied in the two vineyard plots under study as well as to understand when they should be fertilised or not according to their target crop yields. The average plant vegetative vigour and leaf water content of both the plots showed a high temporal and spatial variability during all phenological phases and, according to these results, the optimal fertilisation time should have been 12 April 2021. In fact, this crop operation is aimed at supporting the vegetative activity but must be performed when the soil water and, therefore, the plant leaf water content are high. Therefore, spatially variable rate fertilisation should have been performed around 12 April 2021 in both plots, using previous NDVI maps and taking into consideration two management zones. This work demonstrates the usefulness of remote sensing data as Decision Support Systems (DSS) for nitrogen fertilisation in order to reduce the production cost, environmental impact and climate footprints per kg of produced grapes, according to the European Green Deal challenges.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Comparetti

Silva

2022

Sustainability

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“…In addition, scholars have proposed the utilization of spectral detection technology to acquire the normalized difference vegetation index (NDVI) of the crop canopy to evaluate crop growth for targeted zoning and topdressing. This method offers advantages such as a simple operation and timeliness (Honkavaara et al, 2013;Reynolds et al, 2015). At present, the sensors used to detect the NDVI of crop canopies are mainly satellite-borne canopy spectral sensors, airborne canopy spectral sensors, and vehicle-borne canopy spectral sensors, which are categorized based on their different carrying platforms.…”

mentioning

confidence: 99%

Method for Zoning Corn Based on the NDVI and the Improved SOM-K-Means Algorithm

Di,

Wang

2023

Journal of the ASABE

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HIGHLIGHTS• A partitioning method based on the NDVI and improved SOM-K-MEANS algorithm is proposed.• The optimal number of partitions is determined according to the DBI, silhouette coefficient and silhouette analysis.• This method provides a new approach for the real-time variable-rate fertilization of maize. ABSTRACT.To solve problems such as low nitrogen use efficiency during corn intertillage and topdressing and the presence of spatial differences in corn growth, a method for zoning and division based on the normalized difference vegetation index (NDVI) and the improved self-organizing map (SOM)-K-means algorithm was proposed. First, the GreenSeeker spectrum sensor was utilized to acquire the NDVI of the corn canopy in the vegetative V6-V10 stage during the intertillage period. Second, the acquired data were screened, and the SOM-K-means algorithm was used to perform a cluster analysis of the processed data. Finally, the clustering performance was analyzed. The initial clustering center was acquired with an SOM neural network, the clustering center was used in K-means clustering, and zoning was performed. The optimal number of zones was 4 according to the Davies-Boulding Index (DBI), the silhouette coefficient, and a silhouette analysis of the differences in corn growth. With four zones, the DBI and the silhouette coefficient were 0.569 and 0.537, respectively. Clusters 1 and 4 and clusters 2 and 3 in the silhouette map displayed similar thicknesses, with large differences between clusters and small differences within clusters. A comparison of the SOM-K-means algorithm, K-means algorithm, and SOM neural network algorithm revealed that the run time of the SOM-K-means clustering algorithm was 4.880 s, its inertia was 9.2118, and the run time of the SOM neural network was 4.715 s. The overall coefficient of variation of corn growth in the unzoned test area was 15.24%, and the overall coefficient of variation of corn growth after zoning was 6.94%. This method provides a new approach for understanding issues related to variable-rate fertilization zoning and online real-time zoning during corn intertillage based on the NDVI.

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Application of remote sensing by unmanned aerial vehicles to map variability in Ontario ‘Riesling’ and ‘Cabernet franc’ vineyards

Cited by 2 publications

References 19 publications

Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Method for Zoning Corn Based on the NDVI and the Improved SOM-K-Means Algorithm

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