Evidence map›Paper›PMID 42528935›Full record

ArticleFrontiers in plant science2026

A lightweight attention-enhanced framework for crop pest and disease detection in desert greenhouses.

Yunsen Liang, Kewen Ouyang, Zijing Luo, Naimin Kong, Chi Qin, Yaoxue Xu, Chan Zhang, Zhiyuan Zou, Yining Zhang, Mingman Xu and 9 more

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PubMed Full text at PMC Publisher
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Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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

19 authors.

Yunsen Liang *College of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Kewen Ouyang *College of Horticulture, Sichuan Agricultural University, Chengdu, China.
Zijing LuoCollege of Horticulture, Sichuan Agricultural University, Chengdu, China.
Naimin KongCollege of Horticulture, Sichuan Agricultural University, Chengdu, China.
Chi QinCollege of Horticulture, Sichuan Agricultural University, Chengdu, China.
Yaoxue XuCollege of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Chan ZhangCollege of Horticulture, Sichuan Agricultural University, Chengdu, China.
Zhiyuan ZouCollege of Electrical and Mechanical Engineering, Sichuan Agricultural University, Ya'an, China.
Yining ZhangCollege of Horticulture, Sichuan Agricultural University, Chengdu, China.
Mingman XuCollege of Electrical and Mechanical Engineering, Sichuan Agricultural University, Ya'an, China.
Siqin ZhongCollege of Horticulture, Sichuan Agricultural University, Chengdu, China.
Chengyao JiangCollege of Horticulture, Sichuan Agricultural University, Chengdu, China.
Mengyao LiCollege of Horticulture, Sichuan Agricultural University, Chengdu, China.
Yangxia ZhengCollege of Horticulture, Sichuan Agricultural University, Chengdu, China.
Dawei ShiInstitute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.
Sen WangInstitute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.
Chengbo ZhouInstitute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.
Qichang YangInstitute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.
Wei LuCollege of Horticulture, Sichuan Agricultural University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Desert greenhouse environments present challenging visual conditions for crop pest and disease detection because of strong illumination, reflective backgrounds, and frequent leaf occlusion. To address these issues, we propose YOLOv11s-DesertAttn, a lightweight detection framework that integrates ADown, SimAM, and LSKAttention to improve feature preservation, background suppression, and high-level semantic representation. On the test set, the proposed model improves mAP@0.5-0.95 from 0.826 to 0.860 while reducing parameters from 9.43 M to 8.14 M and increasing inference speed from 96 FPS to 116 FPS. Additional evaluation on an independent real-world dataset collected from commercial greenhouses in Hotan, Xinjiang further demonstrates the model's robustness under varying illumination and occlusion conditions, indicating its potential for real-time deployment in intelligent greenhouse monitoring systems.

Indexed as

agricultural pest and disease detectionattention mechanismdeep learningdesert greenhouselightweight detectionYOLO-based framework

Identifiers

PMID42528935
PMCPMC13415763

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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.