Evidence map›Paper›PMID 32952245›Full record

ArticleComputers and electronics in agriculture2020

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

Evangelos Skotadis, Aris Kanaris, Evangelos Aslanidis, Panagiotis Michalis, Nikos Kalatzis, Fotis Chatzipapadopoulos, Nikos Marianos, Dimitris Tsoukalas

Open access · greenAbstract read
In one paragraph

Article in Computers and electronics in agriculture, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 32 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Evangelos SkotadisDepartment of Applied Physics, National Technical University of Athens, Athens 15780, Greece.
Aris KanarisDepartment of Applied Physics, National Technical University of Athens, Athens 15780, Greece.
Evangelos AslanidisDepartment of Applied Physics, National Technical University of Athens, Athens 15780, Greece.
Panagiotis MichalisNEUROPUBLIC S.A., 6 Methonis Street, Piraeus 18545, Greece.
Nikos KalatzisNEUROPUBLIC S.A., 6 Methonis Street, Piraeus 18545, Greece.
Fotis ChatzipapadopoulosNEUROPUBLIC S.A., 6 Methonis Street, Piraeus 18545, Greece.
Nikos MarianosNEUROPUBLIC S.A., 6 Methonis Street, Piraeus 18545, Greece.
Dimitris TsoukalasDepartment of Applied Physics, National Technical University of Athens, Athens 15780, Greece.
National Technical University of Athens · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ChlorpyrifosGas sensorsNanoparticlesOrganophosphatesSmart farming

Identifiers

PMID32952245
PMCPMC7485459
OpenAlexW3085992617

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.