背景:植物病虫害的准确识别和预警是植物病虫害智能防控的前提。由于病虫害发生后,濒危植物的表型相似,以及外部环境的干扰,传统的深度学习模型在植物病虫害表型识别中经常面临过拟合问题,这不仅导致网络的收敛速度慢,而且识别准确率低。
结果:由于上述问题,本研究提出了一种深度学习模型EResNet-支持向量机(SVM)来缓解植物病虫害识别和分类的过拟合问题。首先,在卷积神经网络中增加特征提取层,提高了模型的特征提取能力。第二,嵌入降阶模块,引入稀疏激活函数,降低模型复杂度,减轻过拟合。最后,引入SVM和全连通层融合的分类器,将原来的非线性分类问题转化为高维空间的线性分类问题,进一步缓解了过拟合现象,提高了病虫害的识别精度。消融实验进一步证明,融合结构能有效缓解过拟合现象,提高识别精度。对典型植物病虫害的实验识别结果表明,所提出的EResNet-SVM模型对8种情况(7种植物病害和1种正常情况)具有99.30%的测试准确率,比原来的ResNet18高出5.90%。与经典的AlexNet相比,GoogLeNet,Xception,SqueezeNet和DenseNet201型号,EResNet-SVM模型的精度提高了5.10%,7%,8.10%,6.20%和1.90%,分别。EResNet-SVM模型对6种害虫的检测准确率为100%,比原始ResNet18型号高出3.90%。
结论:这项研究不仅为缓解深度学习中的过拟合问题提供了有益的参考,为病虫害的智能检测和控制提供了理论和技术支持。©2024化学工业学会。
BACKGROUND: The accurate recognition and early warning for plant diseases and pests are a prerequisite of intelligent prevention and control for plant diseases and pests. As a result of the phenotype similarity of the hazarded plant after plant diseases and pests occur, as well as the interference of the external environment, traditional deep learning models often face the overfitting problem in phenotype recognition of plant diseases and pests, which leads to not only the slow convergence speed of the network, but also low recognition accuracy.
RESULTS: Motivated by the above problems, the present study proposes a deep learning model EResNet-support vector machine (SVM) to alleviate the overfitting for the recognition and classification of plant diseases and pests. First, the feature extraction capability of the model is improved by increasing feature extraction layers in the convolutional neural network. Second, the order-reduced modules are embedded and a sparsely activated function is introduced to reduce model complexity and alleviate overfitting. Finally, a classifier fused by SVM and fully connected layers are introduced to transforms the original non-linear classification problem into a linear classification problem in high-dimensional space to further alleviate the overfitting and improve the recognition accuracy of plant diseases and pests. The ablation experiments further demonstrate that the fused structure can effectively alleviate the overfitting and improve the recognition accuracy. The experimental recognition results for typical plant diseases and pests show that the proposed EResNet-SVM model has 99.30% test accuracy for eight conditions (seven plant diseases and one normal), which is 5.90% higher than the original ResNet18. Compared with the classic AlexNet, GoogLeNet, Xception, SqueezeNet and DenseNet201 models, the accuracy of the EResNet-SVM model has improved by 5.10%, 7%, 8.10%, 6.20% and 1.90%, respectively. The testing accuracy of the EResNet-SVM model for 6 insect pests is 100%, which is 3.90% higher than that of the original ResNet18 model.
CONCLUSIONS: This research provides not only useful references for alleviating the overfitting problem in deep learning, but also a theoretical and technical support for the intelligent detection and control of plant diseases and pests. © 2024 Society of Chemical Industry.