Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

A multi-scale parallel weighted fusion dynamic attention method for citrus leaf disease recognitions.

Baijing Wu¹, Ke Gao¹, Jiren Gu²

¹School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China.

Frontiers in Plant Science

|May 18, 2026

Summary

Related Concept Videos

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Case report of a giant hypervascular mediastinal mass with airway and SVC compromise managed without preoperative biopsy.

Frontiers in oncology·2026

Same author

"Steric-Locking" Polymer Acceptor Enabled 20.53% Efficiency With Suppressed Energetic Disorder and Enhanced Mechanical Robustness in Green-Solvent Processed All-Polymer Solar Cells.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

A lightweight LBM detection transformer with multi-scale feature fusion for citrus-picking robots.

Frontiers in plant science·2026

Same author

Study on Regulatory Mechanism of <i>Gastrodia elata</i> Specific microRNA Targeting JNK3 in Alzheimer's Disease.

Molecules (Basel, Switzerland)·2026

Same author

Primary pulmonary mesenchymal malignancy associated with paraneoplastic pemphigus: a case report.

Frontiers in medicine·2026

Same author

Coordinated sub-cycle modulation atomic layer deposition of atomically homogeneous GeTe<sub>9</sub> thin films for high-performance OTSs.

Materials horizons·2026

This study introduces DBG-DETR, an improved real-time detection transformer for citrus leaf diseases. The method enhances detection accuracy in complex backgrounds, offering reliable support for intelligent orchard management.

Area of Science:

Agricultural Science
Computer Vision
Artificial Intelligence

Background:

Citrus leaf disease detection faces challenges like low accuracy due to leaf occlusion, target loss, and complex backgrounds.
Existing methods struggle to effectively extract and fuse disease features in diverse environmental conditions.

Purpose of the Study:

To develop an advanced real-time detection transformer for improved citrus leaf disease identification.
To enhance accuracy and efficiency in disease detection within complex agricultural settings.

Main Methods:

Proposed DBG-DETR method incorporating DMGF-ResNet18 for feature extraction and GSDT for focusing on deep features.
Utilized multiscale parallel depthwise separable convolutions and dynamic gating with Top-K sparse attention.

Keywords:

RT-DETR citrus management disease detection dynamic sparse attention smart agriculture

Related Experiment Videos

Implemented a bi-directional dense feature fusion module (BDFF) for effective shallow and deep feature interaction.

Main Results:

DBG-DETR demonstrated significant improvements over the baseline model, with increases in P (3.31%), mAP (3.40%), mmAP (4.11%), R (3.89%), and F1 (3.59%).
The model achieved a reduction of 3.78 MB in parameters, indicating improved efficiency.
Experimental results confirmed enhanced disease detection performance in complex environments.

Conclusions:

The proposed DBG-DETR method effectively addresses challenges in citrus leaf disease detection.
The study provides a reliable technical foundation for intelligent citrus orchard management systems.
DBG-DETR offers a promising solution for accurate and efficient plant disease identification.