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Special Issue "Low-Cost Sensors and Vectors for Plant Phenotyping"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors, Control, and Telemetry".

Deadline for manuscript submissions: 1 January 2020.

Special Issue Editors

Dr. David Rousseau
E-Mail Website
Guest Editor
Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS); Université d’Angers, 49000 Angers, France
Interests: plant imaging; data science; application in plant phenotyping
Dr. Mark Müller-Linow
E-Mail Website
Guest Editor
Forschungszentrum Jülich (FZJ), 52428 Jülich, Germany
Tel. 0049-2461-6196978
Interests: plant phenotyping; proximal sensing; 2d and 3d imaging; image processing

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect manuscripts (review and original research articles) associated with low-cost sensor and vector technologies for plant phenotyping in controlled or field conditions. Among others, original and innovative contributions that involve widely accessible and reproducible plant phenotyping technologies, such as smartphone-embedded sensors, Internet of Things technologies, credit card mini-computer systems, associated with low-cost vectors made available under 3D printable objects, do-it-yourself item lists, together with embedded artificial intelligence under open sources, are encouraged. 

Dr. David Rousseau
Dr. Mark Müller-Linow
Guest Editors

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

  • Plant phenotyping
  • Proximal sensing
  • Connected sensors
  • Smartphone applications
  • Credit-card mini computer
  • Embedded artificial intelligence

Published Papers (1 paper)

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Research

Open AccessArticle
Quantifying Variation in Soybean Due to Flood Using a Low-Cost 3D Imaging System
Sensors 2019, 19(12), 2682; https://doi.org/10.3390/s19122682 - 13 Jun 2019
Abstract
Flood has an important effect on plant growth by affecting their physiologic and biochemical properties. Soybean is one of the main cultivated crops in the world and the United States is one of the largest soybean producers. However, soybean plant is sensitive to [...] Read more.
Flood has an important effect on plant growth by affecting their physiologic and biochemical properties. Soybean is one of the main cultivated crops in the world and the United States is one of the largest soybean producers. However, soybean plant is sensitive to flood stress that may cause slow growth, low yield, small crop production and result in significant economic loss. Therefore, it is critical to develop soybean cultivars that are tolerant to flood. One of the current bottlenecks in developing new crop cultivars is slow and inaccurate plant phenotyping that limits the genetic gain. This study aimed to develop a low-cost 3D imaging system to quantify the variation in the growth and biomass of soybean due to flood at its early growth stages. Two cultivars of soybeans, i.e. flood tolerant and flood sensitive, were planted in plant pots in a controlled greenhouse. A low-cost 3D imaging system was developed to take measurements of plant architecture including plant height, plant canopy width, petiole length, and petiole angle. It was found that the measurement error of the 3D imaging system was 5.8% in length and 5.0% in angle, which was sufficiently accurate and useful in plant phenotyping. Collected data were used to monitor the development of soybean after flood treatment. Dry biomass of soybean plant was measured at the end of the vegetative stage (two months after emergence). Results show that four groups had a significant difference in plant height, plant canopy width, petiole length, and petiole angle. Flood stress at early stages of soybean accelerated the growth of the flood-resistant plants in height and the petiole angle, however, restrained the development in plant canopy width and the petiole length of flood-sensitive plants. The dry biomass of flood-sensitive plants was near two to three times lower than that of resistant plants at the end of the vegetative stage. The results indicate that the developed low-cost 3D imaging system has the potential for accurate measurements in plant architecture and dry biomass that may be used to improve the accuracy of plant phenotyping. Full article
(This article belongs to the Special Issue Low-Cost Sensors and Vectors for Plant Phenotyping)
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