scholarly journals Comparison of Visual Assessment and Digital Image Analysis for Canopy Cover Estimation

2018 ◽  
Vol 110 (4) ◽  
pp. 1289-1295 ◽  
Author(s):  
Lucie Büchi ◽  
Marina Wendling ◽  
Pauline Mouly ◽  
Raphaël Charles
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Canopy Cover ◽  
Visual Assessment

Inter-laboratory comparison of Ki-67 proliferating index detected by visual assessment and automated digital image analysis

2019 ◽  
Vol 38 (2) ◽  
pp. 73-79
Author(s):  
Snježana Tomić ◽  
Ivana Mrklić ◽  
Jasminka Jakić Razumović ◽  
Nives Jonjić ◽  
Božena Šarčević ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Visual Assessment ◽  
Ki 67

scholarly journals Assessment of Severity of Powdery Mildew Infection of Sweet Cherry Leaves by Digital Image Analysis

HortScience ◽  
2001 ◽  
Vol 36 (1) ◽  
pp. 107-111 ◽  
Author(s):  
James W. Olmstead ◽  
Gregory A. Lang ◽  
Gary G. Grove
Keyword(s):  
Image Analysis ◽  
Powdery Mildew ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Sweet Cherry ◽  
Prunus Avium ◽  
Leaf Tissue ◽  
Visual Assessment ◽  
Accurate Method ◽  
Leaf Disk

A personal computer-based method was compared with standard visual assessment for quantifying colonization of sweet cherry (Prunus avium L.) leaves by powdery mildew (PM) caused by Podosphaera clandestina (Wallr.:Fr.) Lev. Leaf disks from 14 cultivars were rated for PM severity (percentage of leaf area colonized) by three methods: 1) visual assessment; 2) digital image analysis; and 3) digital image analysis after painting PM colonies on the leaf disk. The third technique, in which PM colonies on each leaf disk were observed using a dissecting microscope and subsequently covered with white enamel paint, provided a standard for comparison of the first two methods. A digital image file for each leaf disk was created using a digital flatbed scanner. Image analysis was performed with a commercially available software package, which did not adequately detect slight differences in color between PM and sweet cherry leaf tissue. Consequently, two replicated experiments revealed a low correlation between PM image analysis and painted PM image analysis (r2 = 0.66 and 0.46, P ≤ 0.0001), whereas visual assessment was highly correlated with painted PM image analysis (r2 = 0.88 and 0.95, P ≤ 0.0001). Rank orders of the 14 cultivars differed significantly (P ≤ 0.05) when PM image analysis and painted PM image analysis were compared; however, rankings by visual assessment were not significantly different (P > 0.05) from those by painted PM image analysis. Thus, standard visual assessment is an accurate method for estimating disease severity in a leaf disk resistance assay for sweet cherry PM.

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

2002 ◽  
Vol 24 (2) ◽  
pp. 288 ◽  
Author(s):  
S. R. Murphy ◽  
G. M. Lodge
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Perennial Grass ◽  
Canopy Cover ◽  
Ground Cover ◽  
Estimation Methods ◽  
Visual Estimation ◽  
Pasture Production ◽  
Point Quadrat

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.

scholarly journals Nondestructive Measurement of Vegetative Cover Using Digital Image Analysis

HortScience ◽  
2004 ◽  
Vol 39 (1) ◽  
pp. 55-59 ◽  
Author(s):  
Mercy A. Olmstead ◽  
Robert Wample ◽  
Stephanie Greene ◽  
Julie Tarara
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Cover Crops ◽  
Cover Crop ◽  
Digital Image Analysis ◽  
Ground Surface ◽  
Visual Assessment ◽  
Computer Assisted ◽  
Percent Cover ◽  
Vegetative Cover

Traditionally, vegetative cover has been subjectively assessed by visual assessment. However, visual assessment is thought to overestimate percent vegetative cover. Thus, a repeatable method to objectively quantify percent cover is desirable. In two vineyards near Prosser, Wash., the percentage of ground surface covered by up to 15 different cover crops was assessed both visually and by computer-assisted digital image analysis. Quadrats in the cover crop were photographed digitally and the images analyzed with commercially available software. Areas of green vegetation in each image were identified and measured. Weeds in some images were differentiated from the cover crop by user-defined thresholds. Subjective visual estimates of percent vegetative cover were generally higher than those digitally estimated. Values for the visual estimates ranged from 5% to 70% in 1998 (mean = 52.4%) and 7.5% to 55% in 1999 (mean = 30.7%), compared to digital readings ranging from 0.5% to 24% (mean = 11.1%) and 10.3% to 36.6% cover (mean = 20.1%), respectively. The visual assessments had lower coefficients of variability in 1998 (cv 28.1) than the digital image analysis (cv 52.3), but in 1999, the values for the two techniques were similar (cv 41.2 vs. cv 45.7). Despite initial variations between the two methods, the accuracy of digital image analysis for measuring percentage vegetative cover is superior.

Comparison between digital image analysis and visual assessment of immunohistochemical HER2 expression in breast cancer

2018 ◽  
Vol 214 (12) ◽  
pp. 2087-2092 ◽  
Author(s):  
Morten Ragn Jakobsen ◽  
Chinachote Teerapakpinyo ◽  
Shanop Shuangshoti ◽  
Somboon Keelawat
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Visual Assessment ◽  
Her2 Expression

scholarly journals Practical approaches to automated digital image analysis of Ki-67 labeling index in 997 breast carcinomas and causes of discordance with visual assessment

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0212309 ◽  
Author(s):  
Ah-Young Kwon ◽  
Ha Young Park ◽  
Jiyeon Hyeon ◽  
Seok Jin Nam ◽  
Seok Won Kim ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Visual Assessment ◽  
Labeling Index ◽  
Breast Carcinomas ◽  
Ki 67

Comparison between visual assessment and digital image analysis in evaluating renal interstitial fibrosis in lupus nephritis

Pathology ◽  
2014 ◽  
Vol 46 ◽  
pp. S131
Author(s):  
Tananat Suebvongnirutn ◽  
Songkiat Suwansirikul ◽  
Kajohnsak Noppakun ◽  
Suree Lekawanvijit
Keyword(s):  
Image Analysis ◽  
Lupus Nephritis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Interstitial Fibrosis ◽  
Visual Assessment ◽  
Renal Interstitial Fibrosis

scholarly journals Digital Image Analysis to Supplement Direct Measures of Lettuce Biomass

2012 ◽  
Vol 22 (4) ◽  
pp. 547-555 ◽  
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.

scholarly journals A Comparison of Visual Assessment and Automated Digital Image Analysis of Ki67 Labeling Index in Breast Cancer

PLoS ONE ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e0150505 ◽  
Author(s):  
Fangfang Zhong ◽  
Rui Bi ◽  
Baohua Yu ◽  
Fei Yang ◽  
Wentao Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Visual Assessment ◽  
Labeling Index ◽  
Ki67 Labeling Index
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