Characterising and mapping vineyard canopy using high-spatial-resolution aerial multispectral images

Hall, Andrew; Louis, John; Lamb, David

doi:10.1016/s0098-3004(03)00082-7

Cited by 114 publications

(93 citation statements)

References 6 publications

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“…Once the VI have been calculated, they are classified into a pseudo-color index images, whereby distinct color classes represent manageable differences in vine variability [9]. The use of VI maps has proven to be an invaluable tool to viticulturists interested in evaluating spatial variability in canopy vigor and subsequent crop performance [10]. However, in practical terms, the applicability of satellite or airborne imaging in PV has been limited by poor revisiting frequency, low spatial resolutions, high operational costs and complexity, and lengthy delivery of analyzed images [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…This aspect is very important when pixels are large in relation to the surfaces or objects. Under these conditions, a large proportion of pixels are mixed, as they include canopy, soil, and shadow [10]. In commercial vineyards, the use of images with resolutions higher than 25 cm presents problems associated with the misclassification of the plant, soil, and especially shadow proportion (very small size in images acquired at midday).…”

Section: Introductionmentioning

confidence: 99%

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Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard

et al. 2017

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Abstract:The use of Unmanned Aerial Vehicles (UAVs) in viticulture permits the capture of aerial Red-Green-Blue (RGB) images with an ultra-high spatial resolution. Recent studies have demonstrated that RGB images can be used to monitor spatial variability of vine biophysical parameters. However, for estimating these parameters, accurate and automated segmentation methods are required to extract relevant information from RGB images. Manual segmentation of aerial images is a laborious and time-consuming process. Traditional classification methods have shown satisfactory results in the segmentation of RGB images for diverse applications and surfaces, however, in the case of commercial vineyards, it is necessary to consider some particularities inherent to canopy size in the vertical trellis systems (VSP) such as shadow effect and different soil conditions in inter-rows (mixed information of soil and weeds). Therefore, the objective of this study was to compare the performance of four classification methods (K-means, Artificial Neural Networks (ANN), Random Forest (RForest) and Spectral Indices (SI)) to detect canopy in a vineyard trained on VSP. Six flights were carried out from post-flowering to harvest in a commercial vineyard cv. Carménère using a low-cost UAV equipped with a conventional RGB camera. The results show that the ANN and the simple SI method complemented with the Otsu method for thresholding presented the best performance for the detection of the vine canopy with high overall accuracy values for all study days. Spectral indices presented the best performance in the detection of Plant class (Vine canopy) with an overall accuracy of around 0.99. However, considering the performance pixel by pixel, the Spectral indices are not able to discriminate between Soil and Shadow class. The best performance in the classification of three classes (Plant, Soil, and Shadow) of vineyard RGB images, was obtained when the SI values were used as input data in trained methods (ANN and RForest), reaching overall accuracy values around 0.98 with high sensitivity values for the three classes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard

et al. 2017

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“…High spatial and temporal resolution datasets of environmental indicators are crucial requirements for detailed analyses in various fields of research [1][2][3][4][5][6]. Important environmental indicators are derived from temperature time series [7], since temperature is a main driver for most ecological and environmental processes [8].…”

Section: Introductionmentioning

confidence: 99%

Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail

2014

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Abstract:Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications.

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“…Some other examples of methods that employ texture analysis in agriculture are provided in Wassenaar et al (2002), Ranchin et al (2001) and Franklin et al (2000). Examples of an edge-based technique and region growing technique are provided by Bobillet et al (2003) and Hall et al (2003) respectively. A recent study to determine vineyard area Rodríguez-Pérez et al (2008) highlights the use of supervised classification (with analyst specified "training" sites to identify vineyard components of the image) of Landsat imagery using vegetation indices, and proved to be useful at estimating vineyard areas at large scale.…”

Section: Introductionmentioning

confidence: 99%

Vine Signal Extraction – an Application of Remote Sensing in Precision Viticulture

Smit

Sithole

Strever

2016

SAJEV

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This paper presents a study of precision agriculture in the wine industry. While precision viticulture mostly aims to maximise yields by delivering the right inputs to appropriate places on a farm in the correct doses and at the right time, the objective of this study was rather to assess vine biomass differences. The solution proposed in this paper uses aerial imagery as the primary source of data for vine analysis. The first objective to be achieved by the solution is to automatically identify vineyards blocks, vine rows, and individual vines within rows. This is made possible through a series of enhancements and hierarchical segmentations of the aerial images. The second objective is to determine the correlation of image data with the biophysical data (yield and pruning mass) of each vine. A multispectral aerial image is used to compute vegetation indices, which serve as indicators of biophysical measures. The results of the automatic detection are compared against a test field, to verify both vine location and vegetation index correlation with relevant vine parameters. The advantage of this technique is that it functions in environments where active cover crop growth between vines is evident and where variable vine canopy conditions are present within a vineyard block.

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Characterising and mapping vineyard canopy using high-spatial-resolution aerial multispectral images

Cited by 114 publications

References 6 publications

Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard

Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard

Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail

Vine Signal Extraction – an Application of Remote Sensing in Precision Viticulture

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