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. 2019 Aug 17;19(16):3582.
doi: 10.3390/s19163582.

A Framework for Evaluating Field-Based, High-Throughput Phenotyping Systems: A Meta-Analysis

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Free PMC article

A Framework for Evaluating Field-Based, High-Throughput Phenotyping Systems: A Meta-Analysis

Sierra N Young. Sensors (Basel). .
Free PMC article

Abstract

This paper presents a framework for the evaluation of system complexity and utility and the identification of bottlenecks in the deployment of field-based, high-throughput phenotyping (FB-HTP) systems. Although the capabilities of technology used for high-throughput phenotyping has improved and costs decreased, there have been few, if any, successful attempts at developing turnkey field-based phenotyping systems. To identify areas for future improvement in developing turnkey FB-HTP solutions, a framework for evaluating their complexity and utility was developed and applied to total of 10 case studies to highlight potential barriers in their development and adoption. The framework performs system factorization and rates the complexity and utility of subsystem factors, as well as each FB-HTP system as a whole, and provides data related to the trends and relationships within the complexity and utility factors. This work suggests that additional research and development are needed focused around the following areas: (i) data handling and management, specifically data transfer from the field to the data processing pipeline, (ii) improved human-machine interaction to facilitate usability across multiple users, and (iii) design standardization of the factors common across all FB-HTP systems to limit the competing drivers of system complexity and utility. This framework can be used to evaluate both previously developed and future proposed systems to approximate the overall system complexity and identify areas for improvement prior to implementation.

Keywords: complexity analysis; human-machine interaction; systems analysis; technology adoption.

Conflict of interest statement

The author declares no conflict of interest.

Figures

Figure 1
Figure 1
(a) The total number of records matching search criteria for “field AND phenotyping”, and (b) total number of records matching search criteria for “field AND phenotyping AND high throughput”. To eliminate irrelevant topics, search results were filtered to include the following fields: agriculture, plant sciences, science technology other topics, imaging science photographic technology, computer science, engineering, instrumentation, remote sensing, automation control systems, and robotics.
Figure 2
Figure 2
The red dotted line highlights the system boundary of the field-based, high-throughput phenotyping (FB-HTP) systems included in this analysis.
Figure 3
Figure 3
Illustration of the subsystem dimensions, factors, and their relationships included in this framework. A brief description of each factor is included in the panels on the right. Definitions for each subsystem and factor are included in Section 2.2. Factors in the gray shaded boxes are associated with system utility, and factors in white boxes are associated with system complexity.
Figure 4
Figure 4
The intercorrelations between the complexity scores for the subsystem factors. Only scores that were significant at the p<0.05 level were shaded in the matrix. Note that the trait data management factor was not included in this correlation analysis due to the high number of missing data points, as can be seen from Table 6.
Figure 5
Figure 5
Scatter plots of the significant complexity correlations from the analysis presented in Figure 4: Team complexity and publication year (r2=-0.852); raw data management and data transfer (r2=0.840); raw data management and environmental constraints (r2=0.679); and data transfer and team complexity (r2=-0.676).
Figure 6
Figure 6
The intercorrelations between the utility scores for the subsystem factors. Only scores that were significant at the p<0.05 level were shaded in the matrix. Note that the system from [16] was not included in these data due to missing data points for multiple factors (see Table 7).
Figure 7
Figure 7
Scatter plots for the significant utility correlations from the analysis presented in Figure 6: Accessibility and publication year (r2=0.716); environmental variability control and phenotype measurements (r2=0.697); accuracy and precision and analysis types (r2=0.779); and phenotype measurements and sensor integration (r2=0.795).
Figure 8
Figure 8
Scatter plot of the total utility scores and total complexity scores for each system. The most and least optimal regions on this graph are also highlighted.
Figure 9
Figure 9
Subsystems, factors, and interface connection types of a FB-HTP system.

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References

    1. Roitsch T., Cabrera-Bosquet L., Fournier A., Ghamkhar K., Jiménez-Berni J., Pinto F., Ober E.S. Review: New sensors and data-driven approaches—A path to next generation phenomics. Plant Sci. 2019;282:2–10. doi: 10.1016/j.plantsci.2019.01.011. - DOI - PMC - PubMed
    1. Araus J.L., Kefauver S.C., Zaman-Allah M., Olsen M.S., Cairns J.E. Translating high-throughput phenotyping into genetic gain. Trends Plant Sci. 2018;23:451–466. doi: 10.1016/j.tplants.2018.02.001. - DOI - PMC - PubMed
    1. Pauli D., Chapman S.C., Bart R., Topp C.N., Lawrence-Dill C.J., Poland J., Gore M.A. The quest for understanding phenotypic variation via integrated approaches in the field environment. Plant Physiol. 2016;172:622. doi: 10.1104/pp.16.00592. - DOI - PMC - PubMed
    1. Zhang C., Pumphrey M.O., Zhou J., Zhang Q., Sankaran S. Development of an automated high-throughput phenotyping system for wheat evaluation in a controlled environment. Trans. Am. Soc. Agric. Biol. Eng. 2019;62:61–74. doi: 10.13031/trans.12856. - DOI
    1. Boloix G., Robillard P.N. A software system evaluation framework. Computer. 1995;28:17–26. doi: 10.1109/2.476196. - DOI
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