摘要
针对单网络模型存在对数据产生学习偏好的缺陷,提出了一种基于多模型融合的农作物病害识别方法.该方法首先对4种主流卷积神经网络ResNet50、DenseNet121、Xception和MobileNetV2进行单模型性能评估,然后对这4种单模型分别进行特征级和决策级多模型融合,最终输出识别结果.特征级融合方法分别对每个子网络的最后输出特征层进行平均化、最大值化和拼接压缩融合,实现异质特征的高效互补;而决策级融合方法分别对每个子网络的输出概率进行最大化和平均化融合,实现概率分布决策的高效联合.在农作物病害数据集PDR2018上的试验结果表明:特征级融合明显优于决策级融合和单模型方法,且拼接压缩特征融合方法具有最高的识别准确率,达到了98.44%.此外该模型在PlantDoc数据子集和实际拍摄图像的跨库试验结果同样表明:特征融合方法比单模型方法具有更好的精度和泛化性能.
To solve the shortcomings of single-network model with learning preference for data,the crop disease identification method was proposed based on multi-model fusion.The single model performance of four mainstream convolutional neural networks of ResNet50,DenseNet121,Xception and MobileNetV2 was evaluated by the proposed method,and the four single models were respectively conducted by multi-model feature-level and decision-level fusion to obtain the identification output.The feature-level fusion method was used to average,maximize and splice the final output feature layer of each sub-network to achieve efficient complementarity of heterogeneous features.The decision-level fusion method was used to maximize and average the output probability of each sub-network to achieve efficient union of probability distribution decisions.The experimental results on the PDR2018 crop disease datasets show that feature-level fusion outperforms decision-level fusion and single-model methods significantly.Among the fusion methods,splicing compression feature fusion achieves the highest recognition accuracy of 98.44%.The cross-database experiments on PlantDoc data subset and the actual images confirm that the feature fusion method exhibits superior accuracy and generalization performance compared to the single-model method.
作者
王梓衡
沈继锋
左欣
武小红
孙俊
WANG Ziheng;SHEN Jifeng;ZUO Xin;WU Xiaohong;SUN Jun(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China;School of Computer,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China)
出处
《江苏大学学报（自然科学版）》
CAS
北大核心
2024年第3期286-294,共9页
Journal of Jiangsu University：Natural Science Edition
基金
国家自然科学基金资助项目(61903164)
江苏省自然科学基金资助项目(BK20191427)
江苏大学农业装备学部项目(NZXB20210210)
江苏高校优势学科建设工程(三期)项目(PAPD-2018-87)。
关键词
农作物病害
特征级融合
决策级融合
卷积神经网络
泛化性能
crop disease
feature-level fusion
decision-level fusion
convolutional neural network
generalization performance
