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A sensing approach for automated and real-time pesticide detection in the scope of smart-farming

The increased use of pesticides across the globe has a major impact on public health. Advanced sensing methods are considered of significant importance to ensure that pesticide use on agricultural products remains within safety limits. This study presents the experimental testing of a hybrid, nanoma...

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Autores principales: Skotadis, Evangelos, Kanaris, Aris, Aslanidis, Evangelos, Michalis, Panagiotis, Kalatzis, Nikos, Chatzipapadopoulos, Fotis, Marianos, Nikos, Tsoukalas, Dimitris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485459/
https://www.ncbi.nlm.nih.gov/pubmed/32952245
http://dx.doi.org/10.1016/j.compag.2020.105759
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author Skotadis, Evangelos
Kanaris, Aris
Aslanidis, Evangelos
Michalis, Panagiotis
Kalatzis, Nikos
Chatzipapadopoulos, Fotis
Marianos, Nikos
Tsoukalas, Dimitris
author_facet Skotadis, Evangelos
Kanaris, Aris
Aslanidis, Evangelos
Michalis, Panagiotis
Kalatzis, Nikos
Chatzipapadopoulos, Fotis
Marianos, Nikos
Tsoukalas, Dimitris
author_sort Skotadis, Evangelos
collection PubMed
description The increased use of pesticides across the globe has a major impact on public health. Advanced sensing methods are considered of significant importance to ensure that pesticide use on agricultural products remains within safety limits. This study presents the experimental testing of a hybrid, nanomaterial based gas-sensing array, for the detection of a commercial organophosphate pesticide, towards its integration in a holistic smart-farming tool such as the “gaiasense” system. The sensing array utilizes nanoparticles (NPs) as the conductive layer of the device while four distinctive polymeric layers (superimposed on top of the NP layer) act as the gas-sensitive layer. The sensing array is ultimately called to discern between two gas-analytes: Chloract 48 EC (a chlorpyrifos based insecticide) and Relative Humidity (R.H.) which acts as a reference analyte since is anticipated to be present in real-field conditions. The unique response patterns generated after the exposure of the sensing-array to the two gas-analytes were analysed using a common statistical analysis tool, namely Principal Component Analysis (PCA). PCA has validated the ability of the array to detect, quantify as well as to differentiate between R.H. and Chloract. The sensing array being compact, low-cost and highly sensitive (LOD in the order of ppb for chlorpyrifos) can be effectively integrated with pre-existing crop-monitoring solutions such as the gaiasense.
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spelling pubmed-74854592020-09-14 A sensing approach for automated and real-time pesticide detection in the scope of smart-farming Skotadis, Evangelos Kanaris, Aris Aslanidis, Evangelos Michalis, Panagiotis Kalatzis, Nikos Chatzipapadopoulos, Fotis Marianos, Nikos Tsoukalas, Dimitris Comput Electron Agric Article The increased use of pesticides across the globe has a major impact on public health. Advanced sensing methods are considered of significant importance to ensure that pesticide use on agricultural products remains within safety limits. This study presents the experimental testing of a hybrid, nanomaterial based gas-sensing array, for the detection of a commercial organophosphate pesticide, towards its integration in a holistic smart-farming tool such as the “gaiasense” system. The sensing array utilizes nanoparticles (NPs) as the conductive layer of the device while four distinctive polymeric layers (superimposed on top of the NP layer) act as the gas-sensitive layer. The sensing array is ultimately called to discern between two gas-analytes: Chloract 48 EC (a chlorpyrifos based insecticide) and Relative Humidity (R.H.) which acts as a reference analyte since is anticipated to be present in real-field conditions. The unique response patterns generated after the exposure of the sensing-array to the two gas-analytes were analysed using a common statistical analysis tool, namely Principal Component Analysis (PCA). PCA has validated the ability of the array to detect, quantify as well as to differentiate between R.H. and Chloract. The sensing array being compact, low-cost and highly sensitive (LOD in the order of ppb for chlorpyrifos) can be effectively integrated with pre-existing crop-monitoring solutions such as the gaiasense. Elsevier B.V. 2020-11 2020-09-11 /pmc/articles/PMC7485459/ /pubmed/32952245 http://dx.doi.org/10.1016/j.compag.2020.105759 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Skotadis, Evangelos
Kanaris, Aris
Aslanidis, Evangelos
Michalis, Panagiotis
Kalatzis, Nikos
Chatzipapadopoulos, Fotis
Marianos, Nikos
Tsoukalas, Dimitris
A sensing approach for automated and real-time pesticide detection in the scope of smart-farming
title A sensing approach for automated and real-time pesticide detection in the scope of smart-farming
title_full A sensing approach for automated and real-time pesticide detection in the scope of smart-farming
title_fullStr A sensing approach for automated and real-time pesticide detection in the scope of smart-farming
title_full_unstemmed A sensing approach for automated and real-time pesticide detection in the scope of smart-farming
title_short A sensing approach for automated and real-time pesticide detection in the scope of smart-farming
title_sort sensing approach for automated and real-time pesticide detection in the scope of smart-farming
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485459/
https://www.ncbi.nlm.nih.gov/pubmed/32952245
http://dx.doi.org/10.1016/j.compag.2020.105759
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