LeafArea: an R package for rapid digital image analysis of leaf area

2015 ◽  
Vol 30 (6) ◽  
pp. 1073-1077 ◽  
Author(s):  
Masatoshi Katabuchi
Keyword(s):  
Image Analysis ◽  
Leaf Area ◽  
Digital Image ◽  
Digital Image Analysis ◽  
R Package

scholarly journals Using the conservative nature of fresh leaf surface density to measure foliar area

2014 ◽  
Vol 28 (4) ◽  
pp. 413-421
Author(s):  
Omar S. Castillo ◽  
Esther M. Zaragoza ◽  
Carlos J. Alvarado ◽  
Maria G. Barrera ◽  
Nabanita Dasgupta-Schubert
Keyword(s):  
Image Analysis ◽  
Leaf Area ◽  
Digital Image ◽  
Surface Density ◽  
Digital Image Analysis ◽  
Leaf Surface ◽  
Area Measurement ◽  
Fresh Leaf ◽  
The Masses

Abstract For a herbaceous species, the inverse of the fresh leaf surface density, the Hughes constant, is nearly conserved. We apply the Hughes constant to develop an absolute method of leafarea measurement that requires no regression fits, prior calibrations or oven-drying. The Hughes constant was determined in situ using a known geometry and weights of a sub-set obtained from the fresh leaves whose areas are desired. Subsequently, the leaf-areas (at any desired stratification level), were derived by utilizing the Hughes constant and the masses of the fresh leaves. The proof of concept was established for leaf-discs of the plants Mandevilla splendens and Spathiphyllum wallisii. The conservativeness of the Hughes constant over individual leaf-zones and different leaftypes from the leaves of each species was quantitatively validated. Using the globally averaged Hughes constant for each species, the leaf-area of these and additional co-species plants, were obtained. The leaf-area-measurement-by-mass was cross-checked with standard digital image analysis. There were no statistically significant differences between the leaf-area-measurement-by-mass and the digital image analysis measured leaf-areas and the linear correlation between the two methods was very good. Leaf-areameasurement- by-mass was found to be rapid and simple with accuracies comparable to the digital image analysis method. The greatly reduced cost of leaf-area-measurement-by-mass could be beneficial for small agri-businesses in developing countries.

Digital Image Analysis to Estimate Leaf Area

1996 ◽  
Vol 148 (5) ◽  
pp. 530-535 ◽  
Author(s):  
Brent Baker ◽  
David M. Olszyk ◽  
David Tingey
Keyword(s):  
Image Analysis ◽  
Leaf Area ◽  
Digital Image ◽  
Digital Image Analysis

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.

Validation of an Operator-Assisted Module to Measure Weed and Crop Leaf Cover by Digital Image Analysis

1998 ◽  
Vol 12 (3) ◽  
pp. 446-453 ◽  
Author(s):  
Mathieu Ngouajio ◽  
Claudel Lemieux ◽  
Jean-Jacques Fortier ◽  
Denis Careau ◽  
Gilles D. Leroux
Keyword(s):  
Image Analysis ◽  
Laboratory Experiment ◽  
Leaf Area ◽  
Plant Development ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Field Experiments ◽  
Area Measurement ◽  
Image Analysis System ◽  
Early Stages

The practical application of yield loss prediction models using relative leaf area of weeds is limited due to the lack of a quick and accurate method of leaf area estimation. Leaf cover (the vertical projection of plant canopy on the ground) can be used to approximate leaf area at early stages of plant development. An automated digital image analysis system for measuring leaf cover has been developed. The system has an operator-assisted module aimed at validating the automated functions. The objective of this research was to demonstrate the accuracy of the operator-assisted module under different weed–crop conditions. A laboratory experiment was conducted using simulated weed–crop populations. Two additional field experiments were conducted using corn in competition with: (1) common lambsquarters, barnyardgrass, or a mixture of both species, and (2) a natural weed community. In the laboratory experiment, a narrow linear relation was observed between leaf cover estimated with the operator-assisted module and leaf area measured with an optical area meter (r2> 0.98). In field experiments, the regression between corn leaf cover estimated by the operator-assisted module and corn leaf area measured with the optical area meter was not as good (r2< 0.55). The poor performance of the module was probably due to the overlapping and the architecture of corn leaves (especially unexpanded leaves). Nevertheless, the system showed high precision in estimating leaf area of both grassy weeds and broadleaf weeds (r2> 0.89). Generally, the accuracy of the estimates decreased as the growth stage became more advanced. Apart from its initial purpose as a calibration tool for the automated system, the operator-assisted module can have several potential research applications. It can be used: (1) as an alternative to destructive leaf area measurement at early stages of plant development, (2) as a tool in the study of plant competitive ability, and (3) as an objective and quantitative support to visual observations.

scholarly journals An Inexpensive, Accurate Method for Measuring Leaf Area and Defoliation Through Digital Image Analysis

2002 ◽  
Vol 95 (6) ◽  
pp. 1190-1194 ◽  
Author(s):  
Matthew E. O’Neal ◽  
Douglas A. Landis ◽  
Rufus Isaacs
Keyword(s):  
Image Analysis ◽  
Leaf Area ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Accurate Method

Digital Image Analysis of the Larynx

2000 ◽  
Vol 10 (2) ◽  
pp. 7-9
Author(s):  
Yaser Natour ◽  
Christine Sapienza ◽  
Mark Schmalz ◽  
Savita Collins
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis

scholarly journals Digital Image Analysis of BAP-1 Accurately Predicts Uveal Melanoma Metastasis

2019 ◽  
Vol 8 (3) ◽  
pp. 11 ◽  
Author(s):  
Gustav Stålhammar ◽  
Thonnie Rose O. See ◽  
Stephen Phillips ◽  
Stefan Seregard ◽  
Hans E. Grossniklaus
Keyword(s):  
Image Analysis ◽  
Uveal Melanoma ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Melanoma Metastasis
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