摘要
目的:为了提高苹果等级判定模型的精度,建立苹果等级判定方法。方法:提出一种多信息融合和蜻蜓算法改进深度置信网络的苹果等级判定模型。对苹果图像进行数据增强、归一化、高斯滤波、灰度化等预处理,提取苹果图像的HSV颜色特征、LBP纹理特征和HOG形状特征。针对DBN模型性能受参数选择的影响,运用DA算法优化选择DBN模型的网络参数,提出一种多信息融合和DA-DBN的苹果等级判定模型。结果:与GA-DBN、PSO-DBN、GWO-DBN和DBN相比,基于DA-DBN的苹果等级判定模型的精度最高。结论:蜻蜓算法优化DBN模型可以有效提高苹果等级判定模型的精度。
Objective:In order to improve the precision of apple grade judgment model,the method of apple grade judgment was established.Methods:A decision model of apple rank based on multi-information fusion and dragonfly algorithm was proposed.Firstly,the HSV color feature,LBP texture feature and HOG shape feature of apple image were extracted by pre-processing such as data enhancement,normalization,Gauss filter and grayscale.Secondly,the performance of DBN model was affected by the selection of parameters,the network parameters of DBN model were optimized by DA algorithm,and a multi-information fusion and DA-DBN model for determining apple rank wws proposed.Results:Compared with GA-DBN,PSO-DBN,GWO-DBN and DBN,the model based on DA-DBN had the highest precision.Conclusion:The DBN model is optimized by dragonfly algorithm which can effectively improve the accuracy of apple rank determination model,which provides a new method for apple rank determination.
作者
陈海霞
贾志娟
赵云平
CHEN Haixia;JIA Zhijuan;ZHAO Yunping(Zhengzhou Vocational and Technical College,Zhengzhou,Henan 450000,China;Zhengzhou Normal University,Zhengzhou,Henan 450044,China;Henan University of Technology,Zhengzhou,Henan 450000,China)
出处
《食品与机械》
CSCD
北大核心
2023年第10期138-145,共8页
Food and Machinery
基金
河南省科技攻关计划项目(编号:212102210415)。
关键词
深度置信网络
蜻蜓算法
纹理特征
颜色特征
形状特征
苹果
deep belief network
dragonfly algorithm
texture feature
color feature
shape feature
apple
