农业大数据学报2026，Vol.8Issue(2)：141-154,14.DOI:10.19788/j.issn.2096-6369.000145
AGLU-YOLO:轻量化柑橘叶片病害实时检测算法研究
AGLU-YOLO:Research on Real-time Detection Algorithm of Lightweight Citrus Leaf Disease
摘要
Abstract
A lightweight detection algorithm(AGLU-YOLO)is proposed to solve the problems of insufficient accuracy,small lesions and complex orchard background in citrus leaf disease detection.This method fuses AdditiveBlock and Convolutional Gated Linear Unit(CGLU)in the C3k2 module of the backbone network to form the C3k2_AdditiveBlock_CGLU module.The former enhances long-range dependence and global context representation by additive modeling,and the latter realizes conditional gating by depthwise separable 3×3 convolution and point convolution to suppress false activation caused by complex texture and enhance small-scale lesion response.At the same time,the AFCA attention mechanism is added in the feature fusion stage to improve cross-layer semantic interaction and multi-scale robustness.Secondly,in order to meet the needs of edge deployment,the LAMP hierarchical importance pruning algorithm is used to jointly compress the channel/level,and a lightweight fine-tuning is performed to restore the accuracy;then the model is exported to ONNX and operator fusion and low-precision inference optimization are implemented through TensorRT to achieve real-time detection with low latency and high throughput.Through experimental verification on the self-made dataset,AGLU-YOLO improves the Precision,Recall and mAP@0.5 indexes by 3.1,4.4 and 5.1 percentage points,respectively,compared with the baseline YOLOv11n,and takes into account the volume and recognition speed,showing stronger robustness and multi-scale lesion adaptability,which can effectively meet the application requirements of rapid and accurate identification of citrus leaf disease.关键词
柑橘叶病检测/小目标检测/YOLOv11算法/深度学习/CGLUKey words
citrus leaf disease detection/small target detection/YOLOv11 algorithm/deep learning/CGLU引用本文复制引用
肖吟枫,杨抒..AGLU-YOLO:轻量化柑橘叶片病害实时检测算法研究[J].农业大数据学报,2026,8(2):141-154,14.基金项目
成都大学计算机学院科研细胞核项目(RCN—K026). （RCN—K026）
