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Optical and Chemical Sensing in Agricultural and Environmental Systems
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Optical and Chemical Sensing in Agricultural and Environmental Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 7270

Special Issue Editors

Dr. Sindhuja Sankaran

E-Mail Website
Guest Editor
Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164-6120, USA
Interests: automated system development for agricultural applications; proximal and remote sensing (unmanned aerial vehicle/UAV and satellite) technologies for phenomics applications; biomarkers-based sensing techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Chongyuan Zhang

E-Mail Website
Guest Editor
Department of Botany & Plant Pathology, Purdue University, West Lafayette, IN 47907, USA
Interests: development of sensing tools/platforms for high-throughput crop phenotyping; applications of ground-, UAV-, and satellite-based sensing technologies for phenotyping in crop/tree fruit production and plant breeding; crop disease monitoring using non-invasive sensing techniques

Special Issue Information

Dear Colleagues,

This Special Issue is aimed to accept manuscripts (review and original research articles) associated with optical and chemical sensor technologies for agricultural and environmental applications. This Special Issue is open to contributions involving both pre- and post-harvest trait evaluation in controlled environment/field/storage conditions associated with agricultural production or applications associated with environmental systems. Original and innovative contributions that involve the sensor/system development and applications of optical, chemical and bio-sensors are encouraged. The applications can include crop sensing, crop stress detection, quality monitoring, disease monitoring, etc. The contributions can also include the development of new analytical methods – statistical and machine learning approaches – that involve data from such sensors.

Dr. Sindhuja Sankaran
Dr. Chongyuan Zhang
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 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. 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 2600 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

  • sensor and/or system development
  • agricultural and environmental applications of optical, chemical, and bio-sensors
  • data analytical methods involving data from such sensors
  • machine learning approaches involving data from such sensors
  • sensor based modeling approaches
  • crop or postharvest trait sensing
  • crop stress/disease or quality monitoring

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Published Papers (3 papers)

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14 pages, 7938 KiB  
Article
A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments
by James Reynolds, Evan Williams, Devon Martin, Caleb Readling, Parvez Ahmmed, Anders Huseth and Alper Bozkurt
Sensors 2022, 22(15), 5582; https://doi.org/10.3390/s22155582 - 26 Jul 2022
Cited by 1 | Viewed by 1917
Abstract
Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents [...] Read more.
Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents the design of a modular system to serve as a research platform for outdoor sensor development and deployment. The advantages of this system include low power consumption (enabling solar charging), the use of commercially available electronic parts for lower-cost and scaled up deployments, and the flexibility to include internal electronics and external sensors, allowing novel applications. In addition to tracking environmental parameters, the modularity of this system brings the capability to measure other non-traditional elements. This capability is demonstrated with two different agri- and aquacultural field applications: tracking moth phenology and monitoring bivalve gaping. Collection of these signals in conjunction with environmental parameters could provide a holistic and context-aware data analysis. Preliminary experiments generated promising results, demonstrating the reliability of the system. Idle power consumption of 27.2 mW and 16.6 mW for the moth- and bivalve-tracking systems, respectively, coupled with 2.5 W solar cells allows for indefinite deployment in remote locations. Full article
(This article belongs to the Special Issue Optical and Chemical Sensing in Agricultural and Environmental Systems)
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Other

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16 pages, 8595 KiB  
Brief Report
Comparison of Individual Sensors in the Electronic Nose for Stress Detection in Forest Stands
by Tereza Hüttnerová, Sebastian Paczkowski, Tarek Neubert, Anna Jirošová and Peter Surový
Sensors 2023, 23(4), 2001; https://doi.org/10.3390/s23042001 - 10 Feb 2023
Cited by 4 | Viewed by 2107
Abstract
Forests are increasingly exposed to natural disturbances, including drought, wildfires, pest outbreaks, and windthrow events. Due to prolonged droughts in the last years in Europe, European forest stands significantly lost vitality, and their health condition deteriorated, leading to high mortality rates, especially, but [...] Read more.
Forests are increasingly exposed to natural disturbances, including drought, wildfires, pest outbreaks, and windthrow events. Due to prolonged droughts in the last years in Europe, European forest stands significantly lost vitality, and their health condition deteriorated, leading to high mortality rates, especially, but not limited to, Norway spruce. This phenomenon is growing, and new regions are being affected; thus, it is necessary to identify stress in the early stages when actions can be taken to protect the forest and living trees. Current detection methods are based on field walks by forest workers or deploying remote sensing methods for coverage of the larger territory. These methods are based on changes in spectral reflectance that can detect attacks only at an advanced stage after the significant changes in the canopy. An innovative approach appears to be a method based on odor mapping, specifically detecting chemical substances which are present in the forest stands and indicate triggering of constitutive defense of stressed trees. The bark beetle attacking a tree, for example, produces a several times higher amount of defense-related volatile organic compounds. At the same time, the bark beetle has an aggregation pheromone to attract conspecifics to overcome the tree defense by mass attack. These substances can be detected using conventional chemical methods (solid-phase microextraction fibers and cartridges), and it is proven that they are detectable by dogs. The disadvantage of classic chemical analysis methods is the long sampling time in the forest, and at the same time, the results must be analyzed in the laboratory using a gas chromatograph. A potential alternative novel device appears to be an electronic nose, which is designed to detect chemical substances online (for example, dangerous gas leaks or measure concentrations above landfills, volcanic activity, etc.). We tested the possibility of early-stage stress detection in the forest stands using an electronic nose Sniffer4D and compared the individual sensors in it for detecting the presence of attacked and dead trees. Our results indicate the promising applicability of the electronic nose for stress mapping in the forest ecosystem, and more data collection could prove this approach. Full article
(This article belongs to the Special Issue Optical and Chemical Sensing in Agricultural and Environmental Systems)
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11 pages, 3833 KiB  
Brief Report
Biogenic VOCs Emission Profiles Associated with Plant-Pest Interaction for Phenotyping Applications
by Milton Valencia-Ortiz, Afef Marzougui, Chongyuan Zhang, Sapinder Bali, Steven Odubiyi, Vidyasagar Sathuvalli, Nilsa A. Bosque-Pérez, Michael O. Pumphrey and Sindhuja Sankaran
Sensors 2022, 22(13), 4870; https://doi.org/10.3390/s22134870 - 28 Jun 2022
Cited by 7 | Viewed by 2243
Abstract
Pest attacks on plants can substantially change plants’ volatile organic compounds (VOCs) emission profiles. Comparison of VOC emission profiles between non-infected/non-infested and infected/infested plants, as well as resistant and susceptible plant cultivars, may provide cues for a deeper understanding of plant-pest interactions and [...] Read more.
Pest attacks on plants can substantially change plants’ volatile organic compounds (VOCs) emission profiles. Comparison of VOC emission profiles between non-infected/non-infested and infected/infested plants, as well as resistant and susceptible plant cultivars, may provide cues for a deeper understanding of plant-pest interactions and associated resistance. Furthermore, the identification of biomarkers—specific biogenic VOCs—associated with the resistance can serve as a non-destructive and rapid tool for phenotyping applications. This research aims to compare the VOCs emission profiles under diverse conditions to identify constitutive (also referred to as green VOCs) and induced (resulting from biotic/abiotic stress) VOCs released in potatoes and wheat. In the first study, wild potato Solanum bulbocastanum (accession# 22; SB22) was inoculated with Meloidogyne chitwoodi race 1 (Mc1), and Mc1 pathotype Roza (SB22 is resistant to Mc1 and susceptible to pathotype Roza), and VOCs emission profiles were collected using gas chromatography-flame ionization detection (GC-FID) at different time points. Similarly, in the second study, the VOCs emission profiles of resistant (‘Hollis’) and susceptible (‘Alturas’) wheat cultivars infested with Hessian fly insects were evaluated using the GC-FID system. In both studies, in addition to variable plant responses (susceptibility to pests), control treatments (non-inoculated or non-infested) were used to compare the VOCs emission profiles resulting from differences in stress conditions. The common VOC peaks (constitutive VOCs) between control and infected/infested samples, and unique VOC peaks (induced VOCs) presented only in infected/infested samples were analyzed. In the potato-nematode study, the highest unique peak was found two days after inoculation (DAI) for SB22 inoculated with Mc1 (resistance response). The most common VOC peaks in SB22 inoculated with both Mc1 and Roza were found at 5 and 10 DAI. In the wheat-insect study, only the Hollis showed unique VOC peaks. Interestingly, both cultivars released the same common VOCs between control and infected samples, with only a difference in VOC average peak intensity at 22.4 min retention time where the average intensity was 4.3 times higher in the infested samples of Hollis than infested samples of Alturas. These studies demonstrate the potential of plant VOCs to serve as a rapid phenotyping tool to assess resistance levels in different crops. Full article
(This article belongs to the Special Issue Optical and Chemical Sensing in Agricultural and Environmental Systems)
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