Evidence map›Paper›PMID 41516442›Full record

ArticleSensors (Basel, Switzerland)2025

Lightweight Multimodal Fusion for Urban Tree Health and Ecosystem Services.

Abror Buriboev, Djamshid Sultanov, Ilhom Rahmatullaev, Ozod Yusupov, Erali Eshonqulov, Dilshod Bekmuradov, Nodir Egamberdiev, Andrew Jaeyong Choi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

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

8 authors.

Abror BuriboevDepartment of AI-Software, Gachon University, Sujeong-Gu, Seongnam-si 13120, Republic of Korea.ORCID 0000-0001-8024-6200
Djamshid SultanovDepartment of Infocommunication Engineering, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan.
Ilhom RahmatullaevDigital Technologies and Artificial Intelligence Development Research Institute, Tashkent 100125, Uzbekistan.ORCID 0000-0002-4872-4265
Ozod YusupovDepartment of Software Engineering, Samarkand State University, Samarkand 140104, Uzbekistan.ORCID 0009-0003-7146-2106
Erali EshonqulovDepartment of Software Engineering, Samarkand State University Named After Sharof Rashidov, Samarkand 140104, Uzbekistan.ORCID 0000-0002-8324-1816
Dilshod BekmuradovDepartment of Digital Technologies and AI, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent 100000, Uzbekistan.
Nodir EgamberdievDepartment of Convergence of Digital Technologies, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan.
Andrew Jaeyong ChoiDepartment of AI-Software, Gachon University, Sujeong-Gu, Seongnam-si 13120, Republic of Korea.ORCID 0000-0001-6507-3081

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid urban expansion has heightened the demand for accurate, scalable, and real-time methods to assess tree health and the provision of ecosystem services. Urban trees are the major contributors to air-quality improvement and climate change mitigation; however, their monitoring is mostly constrained to inherently subjective and inefficient manual inspections. In order to break this barrier, we put forward a lightweight multimodal deep-learning framework that fuses RGB imagery with environmental and biometric sensor data for a combined evaluation of tree-health condition as well as the estimation of the daily oxygen production and CO

Indexed as

EcosystemEnvironmental MonitoringTreesCarbon DioxideDeep LearningHumansMultilayer PerceptronsOxygenCarbon DioxideOxygencarbon sequestration estimationedge-efficient AIenvironmental intelligenceimage–sensor integrationmultimodal fusionmulti-task neural networksoxygen production modelingtree health monitoringurban ecology

Identifiers

PMID41516442
PMCPMC12787558

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.