I-GhostNetV3: A Lightweight Deep Learning Framework for Vision-Sensor-Based Rice Leaf Disease Detection in Smart Agriculture.

Puyu Zhang, Rui Li, Yuxuan Liu, Guoxi Sun, Chenglin Wen

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

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

5 authors.

Puyu ZhangCollege of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China.ORCID 0009-0002-5606-1817

Rui LiCollege of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China.

Yuxuan LiuGuangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China.

Guoxi SunCollege of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China.

Chenglin WenGuangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China.

Funding

the Guangdong Provincial Key Area Research and Development Program 2021B0707010003-1the National Key Research and Development Program 2023YFB4704000the Sci-Tech Innovation Project of the Graduate School of Guangdong University of Petrochemical Technolog 2024KJCX038

6 · The paper itself

Abstract

Accurate and timely diagnosis of rice leaf diseases is crucial for smart agriculture leveraging vision sensors. However, existing lightweight convolutional neural networks (CNNs) often struggle in complex field environments, where small lesions, cluttered backgrounds, and varying illumination complicate recognition. This paper presents I-GhostNetV3, an incrementally improved GhostNetV3-based network for RGB rice leaf disease recognition. I-GhostNetV3 introduces two modular enhancements with controlled overhead: (1) Adaptive Parallel Attention (APA), which integrates edge-guided spatial and channel cues and is

Indexed as

AgricultureDeep LearningOryzaPlant DiseasesPlant LeavesConvolutional Neural Networksattention mechanismsI-GhostNetV3lightweight convolutional networksrice disease classificationsmart agriculturevision sensors

Identifiers

PMID41682541

DOI10.3390/s26031025

PMCPMC12900142

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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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.