CoverageTool: A semi-automated graphic software: applications for plant phenotyping

Merchuk-Ovnat, Lianne; Ovnat, Zev; Amir-Segev, Orit; Kutsher, Yaarit; Saranga, Yehoshua; Reuveni, Moshe

doi:10.1186/s13007-019-0472-2

Cited by 7 publications

(9 citation statements)

References 14 publications

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“…Quantification of damage in the LiMu program: Uniformity of image acquisition and maintaining the same settings, especially light and camera distance from the object, are crucial when calculating damage as a percentage of leaf area [70]. We included an object of known dimensions (1 cm 2 ) in the image, which enabled calibration of the dataset.…”

Section: Methods Development and Associated Issuesmentioning

confidence: 99%

High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis

et al. 2020

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Background: Field-grown leafy vegetables can be damaged by biotic and abiotic factors, or mechanically damaged by farming practices. Available methods to evaluate leaf tissue damage mainly rely on colour differentiation between healthy and damaged tissues. Alternatively, sophisticated equipment such as microscopy and hyperspectral cameras can be employed. Depending on the causal factor, colour change in the wounded area is not always induced and, by the time symptoms become visible, a plant can already be severely affected. To accurately detect and quantify damage on leaf scale, including microlesions, reliable differentiation between healthy and damaged tissue is essential. We stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale. Results: We present a robust, fast and sensitive method for leaf-scale visualisation, accurate automated extraction and measurement of damaged area on leaves of leafy vegetables. The image analysis pipeline we developed automatically identifies leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantification on two field-grown leafy vegetable species, spinach and Swiss chard. Conclusions: Our novel lesion quantification method can be used for detection of large (macro) or single-cell (micro) lesions on leaf scale, enabling quantification of lesions at any stage and without requiring symptoms to be in the visible spectrum. Quantifying the wounded area on leaf scale is necessary for generating prediction models for economic losses and produce shelf-life. In addition, risk assessments are based on accurate prediction of the relationship between leaf damage and infection rates by opportunistic pathogens and our method helps determine the severity of leaf damage at fine resolution.

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Section: Methods Development and Associated Issuesmentioning

confidence: 99%

High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis

et al. 2020

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“…For example, Easy Leaf Area can be used to batch process images of shoots to generate projected area measurements from a collection of images [11]. Likewise, ImageJ is still suited for fully manual measurements of area, height, and diameter of the shoot in the image [23,25]. CoverageTool [25] and HTPheno [23] are more specialized tools for whole shoot phenotyping.…”

Section: Phenotyping Of Individual Shootsmentioning

confidence: 99%

“…Likewise, ImageJ is still suited for fully manual measurements of area, height, and diameter of the shoot in the image [23,25]. CoverageTool [25] and HTPheno [23] are more specialized tools for whole shoot phenotyping. CoverageTool allows users to partition shoot images into regions by color and quickly quantify the projected area occupied by each color [25].…”

Section: Phenotyping Of Individual Shootsmentioning

confidence: 99%

“…CoverageTool [25] and HTPheno [23] are more specialized tools for whole shoot phenotyping. CoverageTool allows users to partition shoot images into regions by color and quickly quantify the projected area occupied by each color [25]. This enables rapid measurements of not only projected area, but also of senescence or tip dieback.…”

Section: Phenotyping Of Individual Shootsmentioning

confidence: 99%

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Image-Based, Organ-Level Plant Phenotyping for Wheat Improvement

2020

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Wheat was one of the first grain crops domesticated by humans and remains among the major contributors to the global calorie and protein budget. The rapidly expanding world population demands further enhancement of yield and performance of wheat. Phenotypic information has historically been instrumental in wheat breeding for improved traits. In the last two decades, a steadily growing collection of tools and imaging software have given us the ability to quantify shoot, root, and seed traits with progressively increasing accuracy and throughput. This review discusses challenges and advancements in image analysis platforms for wheat phenotyping at the organ level. Perspectives on how these collective phenotypes can inform basic research on understanding wheat physiology and breeding for wheat improvement are also provided.

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“…Early growth cover (EGC%) was measured at 33 DAS. Coverage Tool [15] was used to quantify ground coverage by canopy, in percent, for each plot. A set of photos was taken vertically from above the plots, under the same settings (operator shoulder height of camera from the ground, light, same camera, Figure S4).…”

Section: Early Ground Covermentioning

confidence: 99%

In-Depth Field Characterization of Teff [Eragrostis tef (Zucc.) Trotter] Variation: From Agronomic to Sensory Traits

et al. 2020

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Teff is an important food crop that serves to prepare Injera-flat-bread. It is cultivated worldwide and is particularly susceptible to lodging. A diverse collection of teff [Eragrostis tef (Zucc.) Trotter] populations was characterized for a wide range of traits, ranging from agronomic to final Injera sensory parameters, under well-irrigated Mediterranean spring conditions. The populations tested were collected from single plants presenting lodging resistance at the site of collection and their traits were characterized herein. An early type of lodging was observed, which was most likely triggered by a fast and sharp inflorescence weight increase. Other populations were ‘strong’ enough to carry the inflorescence during most of the grain-filling period, up to a point where strong lodging occurred and plants where totally bent to the ground. Three mixed color seed populations were established from a single plant. These were separated into ‘white’ and ‘brown’ seeds and were characterized separately under field conditions. The newly ‘brown’ populations appear to be the result of a rather recent non-self (external) airborne fertilization from a dark pollen donor. Some of these hybrids were found to be promising in terms of Injera sensory traits. The population of these studies might serve as breeding material. Integration between a wide range of parameters and the correlations obtained between agronomic and sensory traits might improve our ability to breed towards a “real world” better end-product.

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CoverageTool: A semi-automated graphic software: applications for plant phenotyping

Cited by 7 publications

References 14 publications

High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis

High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis

Image-Based, Organ-Level Plant Phenotyping for Wheat Improvement

In-Depth Field Characterization of Teff [Eragrostis tef (Zucc.) Trotter] Variation: From Agronomic to Sensory Traits

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