农业工程2026，Vol.16Issue(4)：30-36,7.DOI:10.19998/j.cnki.2095-1795.202511011
基于改进EfficientNetV2-S模型的甘蔗叶片病害识别方法
Sugarcane leaf disease identification method based on improved EfficientNetV2-S model
摘要
Abstract
Addressing problems of insufficient feature extraction and low recognition accuracy caused by small target size and similar color characteristics among diseases in sugarcane leaf diseases,an improved EfficientNetV2-S model has been proposed.Firstly,5×5 convolutional kernel has been used to replace 3×3 convolution kernels from layers 12 to 17 in network,enlarging receptive field of con-volution kernels to improve feature extraction abilities.Secondly,a global attention mechanism(GAM)has been introduced between input end and backbone network to improve model's ability to pay attention to diseased areas,further improving feature extraction ability.Finally,confidence label smoothing(CLS)algorithm has been combined with a loss function to optimize model.Experimental results showed that average accuracy of improved model reached 99.61%on test set.Compared with basic model,Top-1 accuracy,average precision,and F1-score have increased by 2.04,1.97,and 0.87 percentage points respectively.Improved EfficientNetV2-S model per-formed well in task of identifying sugarcane leaf diseases,providing effective technical support for sugarcane leaf disease identification.关键词
甘蔗/叶片病害/EfficientNetV2-S/卷积核/深度学习/图像处理Key words
sugarcane/leaf disease/EfficientNetV2-S/convolution kernel/deep learning/image processing分类
农业科技引用本文复制引用
巢理,梁锦洪,王学松,王日凤,黄良楷,黄智鑫,胡程喜,宋敏..基于改进EfficientNetV2-S模型的甘蔗叶片病害识别方法[J].农业工程,2026,16(4):30-36,7.基金项目
广西科技计划项目(2022AC20024) （2022AC20024）
广西高校中青年教师科研基础能力提升项目(2024KY0870) （2024KY0870）
