Ground cover in temperate native perennial grass pastures. I. A comparison of four estimation methods

Studies were conducted to compare visual estimates of ground cover and canopy cover by both inexperienced and experienced observers and to compare those estimates with those from more objective methods in native pastures in the high rainfall, temperate rangelands of northern NSW. Ground cover and canopy cover of 60 quadrats was estimated using visual, mapped area, digital image analysis and photo point quadrat methods. Inexperienced observers were trained by estimating ground cover of reference quadrats. Differences between mean visual estimates of ground cover and canopy cover for experienced and inexperienced observers were not significant (P>0.05). Mean ground cover estimates by the mapped area, digital image analysis and point quadrat methods were also not different from each other. The overall relationship between mean visual estimate and mean objective estimate of ground cover was non-linear (second order polynomial, R2 = 0.93), observers tending to underestimate in the mid-range (20 to 80%) of cover compared with objective methods. Mean visual estimate of ground cover was 73.7% compared with the mean objective estimate of 83.7%. Visual estimates of canopy cover (mean 34.6%) were highly correlated (R2 = 0.90) with those of the mapped area method (mean 34.3%) and the relationship was linear. Measurement of ground cover is a standard technique used in many pasture ecology and management studies and is increasingly being used by land managers to monitor pasture production and sustainability. Inexperienced observers were trained quickly and easily to estimate ground cover and canopy cover with sufficient accuracy to identify ranges of cover using visual estimation, indicating that the visual estimation technique should be suitable for estimating ground cover in land management research.

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Quantifying Early Vigour and Ground Cover using Digital Image Analysis

Breeding strategies for sustainable forage and turf grass improvement ◽

10.1007/978-94-007-4555-1_18 ◽

2012 ◽

pp. 147-153

Author(s):

M. Cougnon ◽

J. Verhelst ◽

K. De Dauw ◽

D. Reheul

Keyword(s):

Image Analysis ◽

Digital Image ◽

Digital Image Analysis ◽

Ground Cover ◽

Early Vigour

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Tumour thickness, diameter, area or volume? The prognostic significance of conventional versus digital image analysis-based size estimation methods in uveal melanoma

Acta Ophthalmologica ◽

10.1111/aos.13668 ◽

2018 ◽

Vol 96 (5) ◽

pp. 510-518 ◽

Cited By ~ 4

Author(s):

Thorsteinn Snaebjörnsson Arnljots ◽

Zaid Al-Sharbaty ◽

Emma Lardner ◽

Charlotta All-Eriksson ◽

Stefan Seregard ◽

...

Keyword(s):

Image Analysis ◽

Uveal Melanoma ◽

Digital Image ◽

Digital Image Analysis ◽

Prognostic Significance ◽

Estimation Methods ◽

Size Estimation ◽

Tumour Thickness

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Digital Image Analysis to Supplement Direct Measures of Lettuce Biomass

HortTechnology ◽

10.21273/horttech.22.4.547 ◽

2012 ◽

Vol 22 (4) ◽

pp. 547-555 ◽

Cited By ~ 10

Author(s):

Natalie R. Bumgarner ◽

Whitney S. Miller ◽

Matthew D. Kleinhenz

Keyword(s):

Image Analysis ◽

Leaf Area ◽

Digital Image ◽

Digital Image Analysis ◽

Canopy Cover ◽

High Tunnel ◽

Green Leaves ◽

Rooting Medium ◽

Leaf Lettuce ◽

Direct Measures

Plant growth and biomass assessments are required in production and research. Such assessments are followed by major decisions (e.g., harvest timing) that channel resources and influence outcomes. In research, resources required to assess crop status affect other aspects of experimentation and, therefore, discovery. Destructive harvests are important because they influence treatment selection, replicate number and size, and the opportunity for true repeated measures. This work sought to establish the limits to which image acquisition and analysis may replace standard, destructive measures of fresh lettuce biomass. Outdoor, high tunnel, and greenhouse plantings of three cultivars of red and green leaf lettuce (Lactuca sativa) were direct-seeded in raised beds and plastic trays in spring, summer, and fall seasons in 2009–10 in Wooster, OH. Overhead images (624 in total) were captured at specific time points after seeding using handheld and tripod-mounted commercial digital cameras. Fresh weight and leaf area of destructive plant samples within the digital images were also collected. Images were analyzed using user-defined settings in WinCAM software (Regent Instruments, Quebec, QC, Canada). A reference grid captured within each image allowed for the calculation of crop canopy cover (percent of two-dimensional image area covered by leaves). Calculations of canopy cover require differentiating leaves and rooting medium by color. The rooting medium was dark in color, and differentiating red leaves against this background was less reliable than differentiating green leaves from background. Nevertheless, in samples collected in the greenhouse 7 to 16 days after sowing (DAS), significant correlations (r) of 0.85 to 0.96 (P < 0.05) were observed between measures of canopy cover calculated by image analysis software and leaf area obtained with a leaf area meter on harvested plant material. In outdoor and high tunnel plots 16 to 30 DAS, correlation coefficients between direct measures of plant biomass and WinCAM estimates of canopy cover were 0.71 to 0.95 (P < 0.0001). We conclude that digital image analysis may be useful in real-time, nondestructive assessments of early stage leaf lettuce canopy development, particularly when the leaf area index (LAI) is less than one and settings are dominated by green leaves.

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Evaluation of p53 Immunohistochemical Expression Using Open-Source Software for Digital Image Analysis: A Tissue Microarray Study of Penile Squamous Cell Carcinomas

10.1101/029850 ◽

2015 ◽

Author(s):

Alcides Chaux ◽

George J Netto ◽

Authur L Burnett

Keyword(s):

Image Analysis ◽

Open Source ◽

Open Source Software ◽

Digital Image ◽

Digital Image Analysis ◽

Squamous Cell Carcinomas ◽

Digital Analysis ◽

Visual Estimation ◽

Immunohistochemical Expression ◽

Software Packages

The addition of molecular biomarkers is needed to increase the accuracy of pathologic factors as prognosticators of outcome in penile squamous cell carcinomas (SCC). Evaluation of these biomarkers is usually carried out by immunohistochemistry. Herein we assess p53 immunohistochemical expression on tissue samples of penile SCC using freely-available, open-source software packages for digital image analysis. We also compared the results of digital analysis with standard visual estimation. Percentages of p53 positive cells were higher by visual estimation than by digital analysis. However, correlation was high between both methods. Our study shows that evaluation of p53 immunohistochemical expression is feasible using open-source software packages for digital image analysis. Although our analysis was limited to penile SCC, the rationale should also hold for other tumor types in which evaluation of p53 immunohistochemical expression is required. This approach would reduce interobserver variability, and would provide a standardized method for reporting the results of immunohistochemical stains. As these diagnostic tools are freely-available online, researchers and practicing pathologists could incorporate them in their daily practice without increasing diagnostic costs.

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