Crop Pest Identification and Real-Time Monitoring System Design Based on Improved YOLOv8s.

Qiang Gao, Chongchong Shi, Yu Ji, Meili Wang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Qiang GaoSchool of Information Engineering, Xi'an University, Xi'an 710065, China.

Chongchong ShiSchool of Information Engineering, Xi'an University, Xi'an 710065, China.

Yu JiShaanxi Joint Laboratory of Artificial Intelligence, Xi'an University, Xi'an 710065, China.

Meili WangShaanxi Joint Laboratory of Artificial Intelligence, Xi'an University, Xi'an 710065, China.ORCID 0000-0001-7901-1789

Funding

2025 Xi'an Science and Technology Program Project - Agricultural Key Technology Research Project (General Project) 2025JH-NJSYB-0064AI-driven Smart Agriculture Discipline Cross-innovation Team, Xi'an College of Arts and Sciences 25WTJX01Project of Young Talent Support Program of Xi'an Science and Technology Association 959202413080Shaanxi Provincial Key R&D Program-Key Projects-Qinchuangyuan Industrial Innovation Aggregation Zone "Four Chains" Integration 2024CY-JJQ-35Special Project of the Third Batch of Discipline Cross-Building Points (Agricultural Numerical Intelligence) of Xi'an College of Arts and Sciences XY2024JC03Xi'an Science and Technology Program-University Institutes Science and Technology Personnel Serving Enterprises Project Arts and Sciences Special 24GXFW0081-20

6 · The paper itself

Abstract

This study aims to address the limitations of the YOLOv8 model in terms of low detection accuracy and poor deployment adaptability in the context of crop pest detection. To this end, a lightweight attention mechanism and a feature enhancement module were incorporated into the structure of YOLOv8s, with a view to optimizing its detection performance across a range of insects. Based on this improved model, a real-time pest monitoring system was further developed. Results showed that on the self-constructed pest dataset, the proposed improved model increased mAP

Indexed as

Crops, AgriculturalInsectaAlgorithmsAnimalsDetection Algorithmscrop pest detectionfeature enhancement moduleimproved YOLOv8s modellightweight attention mechanismsystem design

Identifiers

PMID41600200

DOI10.3390/s26020404

PMCPMC12846215

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.