ImageBreed: Open‐access plant breeding web–database for image‐based phenotyping

Morales, Nicolás; Kaczmar, Nicholas; Santantonio, Nicholas; Gore, Michael A.; Mueller, Lukas A.; Robbins, Kelly R.

doi:10.1002/ppj2.20004

Cited by 21 publications

(22 citation statements)

References 19 publications

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“…In particular, aerial HTP platforms have become favored overground platforms, as they are suitable for use in large breeding trials and show a good trade-off between time, data accuracy, and resolution ( Yang et al, 2017 ; Gracia-Romero et al, 2019 ; Zhao et al, 2019 ). The rapid development of sensors and unmanned aerial vehicles (UAVs), as well as that of image and data analyses algorithms and improved computer capacities observed in recent years, have enabled a broad range of possibilities for aerial HTP to measure plant traits such as PH, stand count, and vegetation indices, among others ( Maes and Steppe, 2019 ; Matias et al, 2020 ; Morales et al, 2020 ). These aerial images are used as a proxy for the characterization of quantitative plant traits.…”

Section: Introductionmentioning

confidence: 99%

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

et al. 2021

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Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (PlatFW) and multi-rotor (PlatMR) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using PlatFW were consistent with those obtained from PlatMR, showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06–0.97). Finally, we also observed high Spearman rank correlations (0.47–0.91) and R2 (0.63–0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.

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Section: Introductionmentioning

confidence: 99%

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

et al. 2021

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“…It also involved in the inhibition of the activity of the kinase that ultimately inhibited the aggregation of the platelets. Quercetin also the type of the flavonoid that directly involved in the inhibition of the cellular mechanism of the platelets [24,25].…”

Section: Fig-2: Shows the Gene Transformation Via Agrobacterium For Prediction Of Transgenic Plantmentioning

confidence: 99%

A Review on Plant Genetics, Gene Transformation and Role of Plant Active Compounds

Khakwani¹,

Ashraf²,

Ateeq³

et al. 2021

Scholars Bulletin

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Plant genetic engineering raises exaggerated expectations and induces fear in parts of the population. Plants, like all other known living organisms, pass on their traits using DNA. Plants however are unique from other living organisms in the fact that they have chloroplasts. Genetic modification has been the cause for much research into modern plant genetics, and has also lead to the sequencing of many plant genomes. The gene gun method is also referred to as "biolistics" (ballistics using biological components). This technique is used for in vivo (within a living organism) transformation and has been especially useful in monocot species like corn and rice. Transformation via Agrobacterium has been successfully practiced in dicots, i.e. broadleaf plants, such as soybeans and tomatoes, for many years. Extract of the plants has ability to protect the cells of the DNA from the attack of the chemicals, xenobiotics and other compounds that damages the cells. Free radicals when in the excess concentration, cause severe damage to nitrogen bases of the specific region of the DNA which ultimately damaged due to large chain of the free radicals.

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“…Another open source solution is ImageBreed. 8 ImageBreed uses ontologies and standards to facilitate interoperability. For example, it provides a BrAPI-compliant interface to its underlying relational database.…”

Section: Existing Solutionsmentioning

confidence: 99%

An open, scalable, and flexible framework for automated aerial measurement of field experiments

Schnaufer¹,

Pistorius²,

LeBauer³

2020

Preprint

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Unoccupied areal vehicles (UAVs or drones) are increasingly used in field research. Drones capable of routinely and consistently capturing high quality imagery of experimental fields have become relatively inexpensive. However, converting these images into scientifically useable data has become a bottleneck. A number of tools exist to support this workflow, but there is no framework for making these tools interopreable, sharable, and scalable.Here we present an initial draft of the Drone Processing Pipeline (DPP), a framework for processing agricultural research imagery that supports best practices and interoperability. DPP emphasizes open software and data that can be shared among and used in whole or part by the research community. We are building the DPP as a distributed, scalable, and flexible pipeline for converting drone imagery into orthomosaics, point clouds, and plot level statistics. Our intent is not to replace, but to integrate components from the emerging ecosystem of utilities with a focus on end-to-end automation and scalability.The initial focus of DPP is the measurements of experimental plots in field research. In the future we expect that standardization will enable new scientific discovery by facilitating collaboration and sharing of software and data. Our vision is to create a processing pipeline that is open, flexible, extensible, portable, and automated. With modern tools, deploying a pipeline on a laptop or HPC should only take a single command. Running a pipeline and publishing data should require only input data and a defined workflow.

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ImageBreed: Open‐access plant breeding web–database for image‐based phenotyping

Cited by 21 publications

References 19 publications

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

A Review on Plant Genetics, Gene Transformation and Role of Plant Active Compounds

An open, scalable, and flexible framework for automated aerial measurement of field experiments

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