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A sensing approach for automated and real-time pesticide detection in the scope of smart-farming.
- Source :
-
Computers and electronics in agriculture [Comput Electron Agric] 2020 Nov; Vol. 178, pp. 105759. Date of Electronic Publication: 2020 Sep 11. - Publication Year :
- 2020
-
Abstract
- 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.<br />Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (© 2020 Elsevier B.V. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 0168-1699
- Volume :
- 178
- Database :
- MEDLINE
- Journal :
- Computers and electronics in agriculture
- Publication Type :
- Academic Journal
- Accession number :
- 32952245
- Full Text :
- https://doi.org/10.1016/j.compag.2020.105759