用于玉米叶片病害分类的轻量级网络ICS-ResNet  

ICS-ResNet:A Lightweight Network for Maize Leaf Disease Classification

作　　者：姬正杰 魏霖静[1] JI Zhengjie;WEI Linjing(College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China)

机构地区：[1]甘肃农业大学信息科学技术学院,甘肃兰州730070

出　　处：《计算机与现代化》2025年第4期19-28,共10页Computer and Modernization

基　　金：甘肃省重点研发计划(23YFWA0013);科技部国家外专项目(G2022042005L)。

摘　　要：精准识别玉米叶片病害对于预防玉米疾病、提高玉米产量有着十分重要的作用。由于植物叶片图像易受到复杂背景、气候、光照和样本数据不平衡等各种因素影响,因此为提高识别精度,提出一种以ResNet50为主干网络,引入改进的空间注意力和通道注意力模块以及深度可分离残差结构的轻量级卷积神经网络ICS-ResNet。利用ResNet网络中的残差连接防止深层网络训练中梯度消失;通过改进的通道注意力模块(ICA)和空间注意力模块(ISA)充分利用不同特征层的语义信息,精准定位网络关键特征;同时为减少参数量,降低运算成本,使用深度可分离残差结构代替传统卷积运算;并使用余弦退火学习策略动态调整学习率,克服网络训练过程中的不稳定性,提高模型的收敛能力,防止陷入局部最优。最后在PlantVillage中的Corn数据集上进行实验,将提出的轻量级网络与CSPNet、InceptionNet_v3、EfficientNet、ShuffleNet、MobileNet和ResNet50等6种目前流行的网络做对比。实验结果表明,提出的ICS-ResNet网络准确率达到了98.87%,与其他6种网络相比,准确率分别提高了5.03个百分点、3.18个百分点、1.13个百分点、1.81个百分点、1.13个百分点、0.68个百分点,参数量和计算量与原始ResNet50网络相比,分别降低了16.27 MB和2.25 GB,显著提高了玉米叶片病害分类效率。Accurate identification of maize leaf diseases plays a crucial role in preventing crop diseases and improving maize yield.However,plant leaf images are often affected by various factors such as complex backgrounds,climate conditions,lighting,and imbalanced sample data.To enhance recognition accuracy,this study proposes a lightweight convolutional neural network named ICS-ResNet,which is based on the ResNet50 backbone network and incorporates improved spatial and channel attention modules along with depthwise separable residual structures.The residual connections in the ResNet architecture prevent gradient vanishing during deep network training.The improved channel attention module(ICA)and spatial attention module(ISA)fully leverage semantic information from different feature layers to precisely localize key network features.To reduce the number of parameters and computational costs,traditional convolution operations are replaced with depthwise separable residual structures.Additionally,a cosine annealing learning rate strategy is employed to dynamically adjust the learning rate,mitigating training instability,enhancing the model's convergence ability,and preventing it from getting trapped in local optima.Finally,experiments were conducted on the Corn dataset from PlantVillage,comparing the proposed lightweight network with six other popular networks,including CSPNet,InceptionNet_v3,EfficientNet,ShuffleNet,and MobileNet.The results demonstrate that the ICS-ResNet model achieves an accuracy of 98.87%,outperforming the other six networks by 5.03,3.18,1.13,1.81,1.13,and 0.68 percentage points,respectively.Moreover,compared to the original ResNet50,the parameter size and computational cost are reduced by 16.27 MB and 2.25 GB,respectively,significantly improving the efficiency of maize leaf disease classification.

关 键 词：玉米 叶片病害 注意力机制 卷积神经网络 深度可分离残差结构 图像识别 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]