ReviewFrontiers in plant science2026
Detection technologies and sensing systems for crop pest identification and infestation severity prediction: a review.
Review in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Application of an Electronic Nose for Early Detection of Tephritidae Infestation in Fruits.Insects · 2026Article
- PestRefineDet: integrating DenseNet with RefineDet for precision pest detection in agriculture.Frontiers in plant science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development of precision agriculture technology, agricultural production is gradually shifting from traditional experience-based practices to data-driven decision-making. Pest species identification and scale prediction are crucial technologies in the field of pest detection. Compared with traditional pest monitoring methods, detection based on organic volatile gases released by crops under pest stress provides superior temporal and spatial resolution. The use of gas sensors in crop pest monitoring has great potential for application in future agricultural production. Infrared absorption spectroscopy-based gas sensors have gained widespread attention in crop pest monitoring due to their superior detection sensitivity and extensive scalability. A comprehensive overview of recent advances in intelligent detection methods and equipment for crop pest monitoring is provided. Emphasis is placed on the architecture, operating principles, sensing mechanisms, and fabrication materials of trace gas sensors based on infrared absorption spectroscopy for agricultural pest monitoring. In addition, key technologies involved in their fabrication processes are outlined. Finally, based on the specific characteristics of these sensors, the paper discusses in detail the application strategies of infrared absorption spectroscopy trace gas sensors in crop pest and disease monitoring, including transmission network design, platform integration, and the technical bottlenecks encountered in practical applications. The research will provide scientific foundations and innovative ideas for the development of future crop pest monitoring technologies, addressing the challenges faced by precision agriculture today.
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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.