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Special Issue "Selected Papers from the Phenome 2019"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 August 2019).

Special Issue Editor

Dr. Joshua Peschel
E-Mail Website
Guest Editor
Agricultural and Biosystems Engineering, Electrical and Computer Engineering (Courtesy) Civil, Environmental and Construction Engineering (Courtesy), Iowa State University, Ames, Iowa 50011-3270, USA
Interests: robotics; computer vision; human–machine interactions

Special Issue Information

Dear Colleagues,

The annual Phenome 2019 conference will be held in Tucson, AZ, 6–10 February, 2019 (http://phenome2019.org). This is the third year for Phenome and the conference is becoming the preeminent North American meeting focused on the methodologies and technologies that enable the study of phenotyping, and the resulting insights into complex biological systems.

The Phenome 2019 conference features the following technical sessions: i) Systems and Sensor Development to Advance Phenomics, detailing systems and sensors being developed for the efficient, high-throughput measurement of traits, and ii) Data Crunching and New Analytics, including advanced techniques for extracting, interpreting, and maintaining meaningful data.

Authors of selected research contributions from the conference will be invited to submit their original papers and contributions under the following conference topics, which include, but are not limited to, the following:

  • Robotics and automated systems
  • Novel proximal and remote sensing technologies
  • Big data and computational phenomics
  • Automated phenotyping at different scales
  • Computer vision and machine learning

Dr. Peschel Joshua M.
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • phenomics
  • robotics
  • computer vision
  • data analytics
  • machine learning

Published Papers (1 paper)

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Research

Open AccessArticle
A Framework for Evaluating Field-Based, High-Throughput Phenotyping Systems: A Meta-Analysis
Sensors 2019, 19(16), 3582; https://doi.org/10.3390/s19163582 - 17 Aug 2019
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Selected Papers from the Phenome 2019)
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