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Application of Sensors in the Detection of Plant Biotic and...
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Application of Sensors in the Detection of Plant Biotic and Abiotic Stress

A special issue of Plants (ISSN 2223-7747).

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 8856

Special Issue Editor

Dr. Marisa Pérez-Bueno

E-Mail Website
Guest Editor
CSIC - Estación Experimental del Zaidín (EEZ), Department of Biochemistry and Molecular and Cellular Biology of Plants, 18008 Granada, Spain
Interests: plant phenotyping; chlorophyll fluorescence; hyperspectral reflectance; thermography; plant stress detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Crop yields are limited by abiotic and biotic stress factors, which are expected to be affected by the ongoing climate change. In a changing world, with growing population and needs for food, crop protection is crucial for the sustainability of our global system of food production.

Detection and diagnosis of plant stress are essential for the development of a more automated and efficient precision agriculture. Especially in the last years, the research community has made strong efforts towards the establishment of methods for stress detection in crop plants, using, particularly, remote sensing.

Plant phenotyping has become a powerful tool for stress detection, based, especially on imaging techniques that monitor physiological traits such as the activities of primary and secondary metabolism, stomatal activity, water content, plant growth by 3D analysis, leaf and canopy structure, etc. These traits can be analyzed by imaging RGB, multi- and hyperspectral reflectance, fluorescence, and thermography. The combination of several of these techniques together with deep learning is increasingly common, as the data complexity increases.

This Special Issue aims to collect papers providing a state-of-the art view of the applications of imaging techniques used in plant phenotyping for the detection of biotic and abiotic stress in plants. Papers addressing stress detection at different scales (greenhouse, field, ecosystem...) will be welcome.

Dr. Marisa Pérez-Bueno
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 submissions that pass pre-check are 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. Plants 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 2700 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

  • remote sensing
  • plant phenotyping
  • plant disease
  • imaging
  • stress detection
  • machine learning

Published Papers (2 papers)

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Research

27 pages, 4915 KiB  
Article
From Genome to Field—Observation of the Multimodal Nematicidal and Plant Growth-Promoting Effects of Bacillus firmus I-1582 on Tomatoes Using Hyperspectral Remote Sensing
by Nik Susič, Uroš Žibrat, Lovro Sinkovič, Andrej Vončina, Jaka Razinger, Matej Knapič, Aleš Sedlar, Saša Širca and Barbara Gerič Stare
Plants 2020, 9(5), 592; https://doi.org/10.3390/plants9050592 - 06 May 2020
Cited by 17 | Viewed by 2982
Abstract
Root-knot nematodes are considered the most important group of plant-parasitic nematodes due to their wide range of plant hosts and subsequent role in yield losses in agricultural production systems. Chemical nematicides are the primary control method, but ecotoxicity issues with some compounds has [...] Read more.
Root-knot nematodes are considered the most important group of plant-parasitic nematodes due to their wide range of plant hosts and subsequent role in yield losses in agricultural production systems. Chemical nematicides are the primary control method, but ecotoxicity issues with some compounds has led to their phasing-out and consequential development of new control strategies, including biological control. We evaluated the nematicidal activity of Bacillus firmus I-1582 in pot and microplot experiments against Meloidogyne luci. I-1582 reduced nematode counts by 51% and 53% compared to the untreated control in pot and microplot experiments, respectively. I-1582 presence in the rhizosphere had concurrent nematicidal and plant growth-promoting effects, measured using plant morphology, relative chlorophyll content, elemental composition and hyperspectral imaging. Hyperspectral imaging in the 400–2500 nm spectral range and supervised classification using partial least squares support vector machines successfully differentiated B. firmus-treated and untreated plants, with 97.4% and 96.3% accuracy in pot and microplot experiments, respectively. Visible and shortwave infrared spectral regions associated with chlorophyll, N–H and C–N stretches in proteins were most relevant for treatment discrimination. This study shows the ability of hyperspectral imaging to rapidly assess the success of biological measures for pest control. Full article
(This article belongs to the Special Issue Application of Sensors in the Detection of Plant Biotic and Abiotic Stress)
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19 pages, 3431 KiB  
Article
Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms
by Rei Sonobe, Yuhei Hirono and Ayako Oi
Plants 2020, 9(3), 368; https://doi.org/10.3390/plants9030368 - 17 Mar 2020
Cited by 40 | Viewed by 5166
Abstract
Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one [...] Read more.
Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms—random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)—in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 μg cm−2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves. Full article
(This article belongs to the Special Issue Application of Sensors in the Detection of Plant Biotic and Abiotic Stress)
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