基于图像融合特征的番茄叶部病害的识别
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摘要
为了提高基于数字图像识别番茄叶部病害的准确率,适应不同分辨率条件下的应用需求,幵满足实践拍摄条件的不确定性,以番茄晚疫病、花叶病、早疫病叶片图像为研究对象,选择HSV模型中的4维H分量等量分割波段作为颜色特征,基于灰度差分统计的均值、对比度和熵3维特征作为纹理特征,融合7维特征向量作为支持向量机(SVM)分类器的输入,用粒子群算法(PSO)优化SVM模型参数。试验结果表明,融合灰度差分统计与H分量4维特征的病害识别模型准确率可达90%。
In order to improve the accuracy of tomato disease recognition based on digital image,to meet the requirements under different resolution when application,and satisfy the uncertainty of actual shooting conditions,a recognition model of tomato leaf disease was built based on image fusion feature.By using the tomato late blight,Mosaic,and early blight leaf image as the research object,four H-components of HSV color model were selected as color features,and the mean,contrast and entropy of gray difference statistics were used as texture features.The seven dimension feature vector is used as the input of SVM classifier,and particle swarm optimization algorithm is used to optimize the SVM model parameters.The test results show that the accuracy of the model is up to 90%,which recognised tomato leaf disease based on image fusion feature.
In order to improve the accuracy of tomato disease recognition based on digital image,to meet the requirements under different resolution when application,and satisfy the uncertainty of actual shooting conditions,a recognition model of tomato leaf disease was built based on image fusion feature.By using the tomato late blight,Mosaic,and early blight leaf image as the research object,four H-components of HSV color model were selected as color features,and the mean,contrast and entropy of gray difference statistics were used as texture features.The seven dimension feature vector is used as the input of SVM classifier,and particle swarm optimization algorithm is used to optimize the SVM model parameters.The test results show that the accuracy of the model is up to 90%,which recognised tomato leaf disease based on image fusion feature.
引文
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[2]王献锋,张善文,王震,等.基于叶片图像和环境信息的黄瓜病害识别方法[J].农业工程学报,2014,30(14):148-153.
[3]张建华,祁力钧,冀荣华,等.基于粗糙集和BP神经网络的棉花病害识别[J].农业工程学报,2012,28(7):161-167.
[4]秦丰,刘东霞,孙炳达,等.基于图像处理技术的四种苜蓿叶部病害的识别[J].中国农业大学学报,2016,21(10):65-75.
[5]刘涛,仲晓春,孙成明,等.基于计算机视觉的水稻叶部病害识别研究[J].中国农业科学,2014,47(4):664-674.
[6]杨倩,高晓阳,武季玱,等.基于颜色和纹理特征的大麦主要病害识别研究[J].中国农业大学学报,2013,18(5):129-135.
[7]孙俊,谭文军,毛罕平,等.基于改迚卷积神经网络的多种植物叶片病害识别[J].农业工程学报,2017,33(19):209-215.
[8]刘辉,张云生,张印辉,等.基于灰度差分统计的火焰图像纹理特征提取[J].控制工程,2013,20(2):213-218.
[9]刘卫芳,夏翃,周震,等.多发性硬化患者脑磁共振图像的纹理分析[J].中国医学影像学杂志,2013,21(4):297-300.
[10]鹿丼鹏,王彬,刘辉,等.基于图像灰度差分统计的雾霾污染等级检测方法[J].计算机工程,2016,42(1):225-230.
[11]刘媛,冯全.葡萄病害的计算机识别方法[J].中国农机化学报,2017,38(4):99-104.
[12]白文斌,白帆,贺文文,等.基于SVM的高粱叶片病斑图像自动分割提取方法研究[J].农学学报,2014,4(6):101-106.
[13]黄曜,许华虎,欧阳杰臣,等.针对图像来源鉴别中支持向量机的研究[J].计算机技术与収展,2016,26(10):1-5.
[14]徐洪平,刘洋,易航,等.运载火箭测収网络异常流量识别技术[J].清华大学学报(自然科学版),2018,58(1):20-26,34.
[15]CORTES C,VAPNIK V.Support-vector networks[J].Machine Learning,1995,20(3):273-297.
[16]刘向东,朱美琳,陈兆乾,等.支持向量机及其在模式识别中的应用[J].计算机科学,2003,30(6):113-117.
[17]吴忠强,康晓华,于丹琦.基于小波消噪和优化支持向量机的板形模式识别[J].中国机械工程,2018,29(1):95-103.
[18]EBERHART R C,SHI Y H.Particle swarm optimization:development,applications and resources[C]//Proceedings of the Congress on Evolutionary Computation.Piscataway:IEEE,2001:81-86.
[19]巩敦卫,张勇,张建化,等.新型粒子群优化算法[J].控制理论与应用,2008,25(1):111-114,119.
[20]冯远静,俞立,冯祖仁,等.采样粒子群优化模型及其动力学行为分析[J].控制理论与应用,2009,26(1):28-34.