Apple disease detection algorithm based on improved RT-DETR

XIAO Yongshuai; CHEN Ruobing; SHI Lei; ZHENG Guang; YIN Fei

doi:10.13300/j.cnki.hnlkxb.2026.03.008

您当前的位置：

首页 >

文章列表页 >

Apple disease detection algorithm based on improved RT-DETR

Agricultural artificial intelligence | 更新时间：2026-11-05

- Apple disease detection algorithm based on improved RT-DETR
- Journal of Huazhong Agricultural University Vol. 45, Issue 3, Pages: 87-97(2026)
- 作者机构：
  
  河南农业大学信息与管理科学学院，郑州 450046
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2026.03.008
  CLC： TP391.41
- Received：10 October 2025，
  
  Published：25 May 2026
- 稿件说明：
移动端阅览
肖永帅，陈若冰，时雷，等.基于改进RT-DETR的苹果病害检测算法［J］.华中农业大学学报，2026，45（3）：87‐97.

XIAO Yongshuai,CHEN Ruobing,SHI Lei,et al.Apple disease detection algorithm based on improved RT-DETR[J].Journal of Huazhong Agricultural University,2026,45(03):87-97.
肖永帅，陈若冰，时雷，等.基于改进RT-DETR的苹果病害检测算法［J］.华中农业大学学报，2026，45（3）：87‐97. DOI： 10.13300/j.cnki.hnlkxb.2026.03.008.

XIAO Yongshuai,CHEN Ruobing,SHI Lei,et al.Apple disease detection algorithm based on improved RT-DETR[J].Journal of Huazhong Agricultural University,2026,45(03):87-97. DOI： 10.13300/j.cnki.hnlkxb.2026.03.008.

摘要

针对复杂果园环境下苹果病害检测中存在的背景干扰强、多尺度病斑识别困难以及模型轻量化部署需求，提出一种基于改进RT-DETR的轻量化苹果病害检测模型EGA-DETR。模型从3个方面进行了改进：设计RGLAN轻量化特征聚合模块，通过特征分流与重参数化卷积减少冗余计算并增强特征表达；构建BiFPN-GLSA多尺度特征融合模块，通过双向特征传递和全局-局部自注意力机制提升不同尺度病斑的表征能力；引入Inner-Shape-IoU损失函数，以增强模型对不规则病斑目标的定位能力。试验结果显示，EGA-DETR在苹果病害数据集上取得了91.8%的精确率、88.9%的召回率和92.1%的mAP

，较基线RT-DETR-18分别提升3.5、3.5、1.5百分点。同时，模型参数量降至11.8×10

，较基线模型减少40.4%，推理速度达到120帧/s。综上所述，EGA-DETR模型在检测精度与计算效率之间实现了较优平衡，可为苹果病害精准、实时检测提供技术支撑。

Abstract

To address the challenges of strong background interference，difficulty in recognizing multi-scale lesions，and the need for lightweight deployment in apple disease detection within complex orchard environments，an improved lightweight apple disease detection model named EGA-DETR was proposed based on RT-DETR. The model was enhanced in three key aspects. First，an RGLAN lightweight feature aggregation module was designed to reduce redundant computations and enhance feature representation through feature splitting and re-parameterized convolution. Second，a BiFPN-GLSA multi-scale feature fusion module was developed to improve the representation of lesions at various scales via b

idirectional feature transmission and a global–local self-attention mechanism. Third，an Inner-Shape-IoU loss function was introduced to improve model’s localization accuracy for irregularly shaped lesion targets. Experimental results showed that EGA-DETR achieved 91.8% precision，88.9% recall，and 92.1% mAP

on the apple disease dataset，with improvements of 3.5，3.5，and 1.5 percentage points over the baseline RT-DETR-18，respectively. Meanwhile，the number of model parameters was reduced to 11.8×10

，which is 40.4% fewer than that of the baseline model，and the inference speed reached 120 frames per second. In summary，the EGA-DETR model achieved a favorable balance between detection accuracy and computational efficiency，providing robust technical support for accurate and real-time apple disease detection.

关键词

Keywords

references

郑超杰，李少波，蒲睿强，等.基于轻量化卷积神经网络的番茄叶片病害识别［J］.江苏农业科学，2024，52（11）：225-231. ZHENG C J，LI S B，PU R Q，et al.Tomato leaf disease recognition based on lightweight convolutional neural network［J］.Jiangsu agricultural sciences，2024，52（11）：225-231 （in Chinese with English abstract） .

杜英杰，宗哲英，王祯，等.农作物病害诊断方法现状和展望［J］.江苏农业科学，2023，51（6）：16-23. DU Y J，ZONG Z Y，WANG Z，et al.Current situation and prospect of diagnostic methods for crop diseases［J］.Jiangsu agricultural sciences，2023，51（6）：16-23 （in Chinese with English abstract） .

BOCHKOVSKIY A，WANG C Y，LIAO H M.YOLOv4：optimal speed and accuracy of object detection［PP/OL］.arXiv， 2020 ：2004.10934［2025-10-10］.https：//arxiv.org/abs/2004.10934.

赵晓，杨梦婷，张懿丹.基于改进YOLO v10n的苹果叶片病害检测方法［J］.农业机械学报，2025，56（9）：657-666，728. ZHAO X，YANG M T，ZHANG Y D.Apple leaf disease detection method based on improved YOLO v10n［J］.Transactions of the CSAM，2025，56（9）：657-666，728 （in Chinese with English abstract） .

曾林涛，马嘉昕，丁羽，等.基于改进YOLO v8的苹果叶部病害检测方法［J］.江苏农业科学，2025，53（5）：147-156. ZENG L T，MA J X，DING Y，et al.An apple leaf disease detection method based on improved YOLO v8［J］.Jiangsu agricultural sciences，2025，53（5）：147-156 （in Chinese with English abstract） .

LIU L，OUYANG W L，WANG X G，et al. Deep learning for generic object detection： a survey［J］. International journal of computer vision，2020，128（2）： 261-318.

VASWANI A，SHAZEER N，PARMAR N，et al. Attention is all you need［C］//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach： Curran Associates，2017： 6000-6010.

CARION N，MASSA F，SYNNAEVE G，et al.End-to-end object detection with transformers［C］//Computer Vision–ECCV 2020.Cham：Springer，2020：213-229.

JI W，ZHAI K L，XU B，et al. Green apple detection method based on multidimensional feature extraction network model and transformer module［J/OL］. Journal of food protection，2025，88（1）： 100397［2025-10-10］. https：//doi.org/10.1016/j.jfp.2024.100397.

ZHANG L X，ZHAO Y，ZHOU C C，et al.JuDifformer：multimodal fusion model with transformer and diffusion for jujube disease detection［J/OL］.Computers and electronics in agriculture，2025，232：110008［2025-10-10］.https：//doi.org/10.1016/j.compag.2025.110008.

ZHAO Y A，LV W Y，XU S L，et al.DETRs beat YOLOs on real-time object detection［C］//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）.Seattle：IEEE，2024：16965-16974.

PFE. Apple disease detection computer vision model［DS/OL］. （2022）［2025-10-10］. https：//universe.roboflow.com/pfe-ns5wl/apple-disease-detection-jspkx.

TAN M X，PANG R M，LE Q V.EfficientDet：scalable and efficient object detection［C］//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）.Seattle：IEEE，2020：10778-10787.

TANG F L，XU Z X，HUANG Q M，et al.DuAT：dual-aggregation transformer network forMedical image segmentation［C］//Pattern Recognition and Computer Vision.Singapore：Springer，2024：343-356.

REZATOFIGHI H，TSOI N，GWAK J，et al.Generalized intersection over union：a metric and a loss for bounding box regression［C］//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）.Long Beach：IEEE，2020：658-666.

ZHANG H，XU C，ZHANG S J.Inner-IoU：more effective intersection over union loss with auxiliary bounding box［PP/OL］.2023：arXiv：2311.02877［2025-10-10］.https：//arxiv.org/abs/2311.02877.

ZHANG H，ZHANG S J.Shape-IoU：more accurate metric considering bounding box shape and scale［PP/OL］.arXiv，2023：2312.17663［2025-10-10］.https：//arxiv.org/abs/2312.17663.

XIAO J H，GUO H Q，ZHOU J，et al. Tiny object detection with context enhancement and feature purification［J/OL］. Expert systems with applications，2023，211：118665［2025-10-10］. https：//doi.org/10.1016/j.eswa.2022.118665.

ZHENG Z H，WANG P，LIU W，et al.Distance-IoU loss：faster and better learning for bounding box regression［J］.Proceedings of the AAAI conference on artificial intelligence，2020，34（7）：12993-13000.

SELVARAJU R R，COGSWELL M，DAS A，et al.Grad-CAM：visual explanations from deep networks via gradient-based localization［J］.International journal of computer vision，2020，128（2）：336-359.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Isolation， denitrification characteristics and biosafety evaluation of a heterotrophic nitrification-aerobic denitrification bacterium NF-1

农业生物质增值化利用技术与装备团队

Effects of alternate wetting and drying irrigation on root morphological structure，root activity and grain yield of ratoon rice

Heterologous expression，enzymatic characterization，and substrate selectivity of a CotA laccase from livestock wastewater-derived Bacillus licheniformis WS4

Genome⁃wide identification and expression analysis of the CoSAD gene family in Camellia oleifera plants with three levels of ploidy

Related Author

No data

Related Institution

No data

AI问答

⁰