VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine

Bei, Roberta De; Fuentes, Sigfredo; Gilliham, Matthew; Tyerman, Stephen D.; Edwards, Everard; Bianchini, Nicolo; Smith, Jason; Collins, Cassandra

doi:10.3390/s16040585

Cited by 87 publications

(78 citation statements)

References 24 publications

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“…The method developed specifically for avocado canopies was evolved from similar analysis calculating the leaf area index (LAI) of grapevine canopies [18,32] and Eucalyptus [31]. The predominant processing steps included segmentation of canopy from sky and image gap analysis (sky/leaf ratio) following methodologies described by Poblete-Echeverría et al [16] and Fuentes et al [31].…”

Section: Rgb Image Analysismentioning

confidence: 99%

“…Red, green, blue (RGB) photographs acquired by digital cameras have been successfully used to estimate canopy density, tree architecture, and growth in forest trees and grapevines [13][14][15][16]. Canopy porosity (crown porosity) or gap distribution was calculated from the proportion of sky area visible within the canopy extent [14,[17][18][19]. It is Figure 1.…”

Section: Visual Interpretation Of Canopy Ciba-geigy Simpsonmentioning

confidence: 99%

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Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

et al. 2018

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Phytophthora root rot (PRR) infects the roots of avocado trees, resulting in reduced uptake of water and nutrients, canopy decline, defoliation, and, eventually, tree mortality. Typically, the severity of PRR disease (proportion of canopy decline) is assessed by visually comparing the canopy health of infected trees to a standardised set of photographs and a corresponding disease rating. Although this visual method provides some indication of the spatial variability of PRR disease across orchards, the accuracy and repeatability of the ranking is influenced by the experience of the assessor, the visibility of tree canopies, and the timing of the assessment. This study evaluates two image analysis methods that may serve as surrogates to the visual assessment of canopy decline in large avocado orchards. A smartphone camera was used to collect red, green, and blue (RGB) colour images of individual trees with varying degrees of canopy decline, with the digital photographs then analysed to derive a canopy porosity percentage using a combination of 'Canny edge detection' and 'Otsu's' methods. Coinciding with the on-ground measure of canopy porosity, the canopy reflectance characteristics of the sampled trees measured by high resolution Worldview-3 (WV-3) satellite imagery was also correlated against the observed disease severity rankings. Canopy porosity values (ranging from 20-70%) derived from RGB images were found to be significantly different for most disease rankings (p < 0.05) and correlated well (R 2 = 0.89) with the differentiation of three disease severity levels identified to be optimal. From the WV-3 imagery, a multivariate stepwise regression of 18 structural and pigment-based vegetation indices found the simplified ratio vegetation index (SRVI) to be strongly correlated (R 2 = 0.96) with the disease rankings of PRR disease severity, with the differentiation of four levels of severity found to be optimal.

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Section: Rgb Image Analysismentioning

confidence: 99%

Section: Visual Interpretation Of Canopy Ciba-geigy Simpsonmentioning

confidence: 99%

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

et al. 2018

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“…Due to greater availability of cameras for capturing images, imaging sensor-based methods are becoming increasingly popular. Among imaging sensor-based methods, a new development that uses a smartphone camera sensor rather than a general-purpose digital camera has emerged recently [4][5][6][7][8][9]. Building on the high performance-price ratio and multi-sensor integration of the smartphone, this approach has attracted much attention to measuring LAI using smartphone camera sensors.…”

Section: Introductionmentioning

confidence: 99%

“…To adapt to different plant heights, LAISmart provides a scalable support system. De Bei et al [8] developed an iOS-based application called (APP)-VitiCanopy. Their algorithm originated from the code implemented in a MATLAB script [10,11] that extracted LAI from a general camera canopy image.…”

Section: Introductionmentioning

confidence: 99%

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone Camera Sensors

Wang

Song

2017

Preprint

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Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

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“…Recently, another smart-app (VitiCanopy) [34] was developed to estimate vine canopy vigor and porosity. VitiCanopy is based on the acquisition of images from below the canopy similarly to DHP, and on their automatic processing, analogously to PocketLAI.…”

Section: Introductionmentioning

confidence: 99%

Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App

Orlando

Movedi

Coduto

et al. 2016

Sensors

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Estimating leaf area index (LAI) of Vitis vinifera using indirect methods involves some critical issues, related to its discontinuous and non-homogeneous canopy. This study evaluates the smart app PocketLAI and hemispherical photography in vineyards against destructive LAI measurements. Data were collected during six surveys in an experimental site characterized by a high level of heterogeneity among plants, allowing us to explore a wide range of LAI values. During the last survey, the possibility to combine remote sensing data and in-situ PocketLAI estimates (smart scouting) was evaluated. Results showed a good agreement between PocketLAI data and direct measurements, especially for LAI ranging from 0.13 to 1.41 (R2 = 0.94, RRMSE = 17.27%), whereas the accuracy decreased when an outlying value (vineyard LAI = 2.84) was included (R2 = 0.77, RRMSE = 43.00%), due to the saturation effect in case of very dense canopies arising from lack of green pruning. The hemispherical photography showed very high values of R2, even in presence of the outlying value (R2 = 0.94), although it showed a marked and quite constant overestimation error (RRMSE = 99.46%), suggesting the need to introduce a correction factor specific for vineyards. During the smart scouting, PocketLAI showed its reliability to monitor the spatial-temporal variability of vine vigor in cordon-trained systems, and showed a potential for a wide range of applications, also in combination with remote sensing.

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VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine

Cited by 87 publications

References 24 publications

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone Camera Sensors

Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App

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