ReviewSensors (Basel, Switzerland)2018
Plant Pest Detection Using an Artificial Nose System: A Review.
Review in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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.
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.
Who cites it
42 citing papers in PubMed, 202 citations in OpenAlex.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Volatile organic compounds associated with red-stripe pathogens inhibit mycelial growth ofMicrobiology spectrum · 2026Article
- Harnessing Microbial Volatile Organic Compounds for Crop Protection: Scientific Discovery, Bridging Ecological Function and On-Farm Application.Microbial biotechnology · 2026Review
- Attention-PestNet: hierarchical scaled dot-product attention for insect pest detection.Plant methods · 2025Article
- From Morphology to Multi-Omics: A New Age of Fusarium Research.Pathogens (Basel, Switzerland) · 2025Review
- Sequencing airborne DNA to monitor crop pathogens and pests.iScience · 2025Article
- Data Collection and Remote Control of an IoT Electronic Nose Using Web Services and the MQTT Protocol.Sensors (Basel, Switzerland) · 2025Article
- Use of the electronic nose to monitor the influences of modified atmosphere packaging on the storage of contaminated garlic.Heliyon · 2025Article
- Cutting-Edge Sensor Design: MIP Nanoparticle-Functionalized Nanofibers for Gas-Phase Detection of Limonene in Predictive Agriculture.Polymers · 2025Article
- Nanomaterial-based biosensors: a new frontier in plant pathogen detection and plant disease management.Frontiers in bioengineering and biotechnology · 2025Review
- A Comprehensive Review of Biomarker Sensors for a Breathalyzer Platform.Sensors (Basel, Switzerland) · 2024Review
- Electronic Noses: From Gas-Sensitive Components and Practical Applications to Data Processing.Sensors (Basel, Switzerland) · 2024Review
- Nanoparticles as a Tool for Alleviating Plant Stress: Mechanisms, Implications, and Challenges.Plants (Basel, Switzerland) · 2024Review
- Interactions ofPlants (Basel, Switzerland) · 2024Article
- Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review.Sensors (Basel, Switzerland) · 2023Review
- Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection ofSensors (Basel, Switzerland) · 2023Article
- Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose.Sensors (Basel, Switzerland) · 2022Article
- Early Detection of Grapevine (Sensors (Basel, Switzerland) · 2022Article
- Emerging Methods of Monitoring Volatile Organic Compounds for Detection of Plant Pests and Disease.Biosensors · 2022Review
- Recent applications of metabolomics in plant breeding.Breeding science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper reviews artificial intelligent noses (or electronic noses) as a fast and noninvasive approach for the diagnosis of insects and diseases that attack vegetables and fruit trees. The particular focus is on bacterial, fungal, and viral infections, and insect damage. Volatile organic compounds (VOCs) emitted from plants, which provide functional information about the plant's growth, defense, and health status, allow for the possibility of using noninvasive detection to monitor plants status. Electronic noses are comprised of a sensor array, signal conditioning circuit, and pattern recognition algorithms. Compared with traditional gas chromatography-mass spectrometry (GC-MS) techniques, electronic noses are noninvasive and can be a rapid, cost-effective option for several applications. However, using electronic noses for plant pest diagnosis is still in its early stages, and there are challenges regarding sensor performance, sampling and detection in open areas, and scaling up measurements. This review paper introduces each element of electronic nose systems, especially commonly used sensors and pattern recognition methods, along with their advantages and limitations. It includes a comprehensive comparison and summary of applications, possible challenges, and potential improvements of electronic nose systems for different plant pest diagnoses.
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What OpenQuestion holds
Registered trials
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.