基于视觉图像识别的番茄表面农药残留量无损检测方法
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摘要
为了提高对番茄表面农药残留量的准确检测能力,提出一种基于视觉图像识别的番茄表面农药残留量无损检测方法。采用激光成像技术进行番茄表面农药残留区域视觉图像采集,对采集的番茄表面图像进行农药残留量的光谱特征分析,提取番茄表面农药残留区域的边缘轮廓特征,根据特征提取结果进行番茄表面农药残留区域视觉图像重构,在重构的区域图像中采用分块匹配技术进行番茄表面农药残留量区域分割,结合自适应分块特征匹配方法实现番茄表面农药残留量检测识别。仿真结果表明,采用该方法进行番茄表面农药残留量的无损性较好,输出图像的信息饱和度较高,提高了对番茄表面农药残留量的准确检测能力,在番茄病虫害防治和农药的去除等方面具有很好的应用价值。
In order to improve the ability of accurate detection of pesticide residues on tomato surface,a method of nondestructive detection of pesticide residues on tomato surface based on visual image recognition was proposed.The image of pesticide residues on tomato surface was collected by laser imaging,and the spectral feature of pesticide residue was analyzed to extract the edge profile of pesticide residue area on tomato surface.Based on the feature extraction results,the region of pesticide residue on tomato surface was reconstructed by visual image reconstruction,and the partition matching technique was used to segment the region of pesticide residue on tomato surface.The detection and recognition of pesticide residues on tomato surface are realized by using adaptive block feature matching method.The simulation results show that the method has good nondestructive effect on pesticide residue on tomato surface and high information saturation of output image,which improves the ability of accurate detection of pesticide residue on tomato surface.It has good application value in tomato pest control and pesticide removal.
In order to improve the ability of accurate detection of pesticide residues on tomato surface,a method of nondestructive detection of pesticide residues on tomato surface based on visual image recognition was proposed.The image of pesticide residues on tomato surface was collected by laser imaging,and the spectral feature of pesticide residue was analyzed to extract the edge profile of pesticide residue area on tomato surface.Based on the feature extraction results,the region of pesticide residue on tomato surface was reconstructed by visual image reconstruction,and the partition matching technique was used to segment the region of pesticide residue on tomato surface.The detection and recognition of pesticide residues on tomato surface are realized by using adaptive block feature matching method.The simulation results show that the method has good nondestructive effect on pesticide residue on tomato surface and high information saturation of output image,which improves the ability of accurate detection of pesticide residue on tomato surface.It has good application value in tomato pest control and pesticide removal.
引文
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[5]WANG Xin,NING Chen,XU Li-zhong.Spatiotemporal saliency model for small moving object detection in infrared videos[J].Infrared Physics&Technology,2015,69:111-117.
[6]张军国,冯文钊,胡春鹤,等.无人机航拍林业虫害图像分割复合梯度分水岭算法[J].农业工程学报,2017,33(14):93-99.
[7]王帅帅.基于高光谱成像的鲜桃虫害检测特征向量的选取[J].信阳农林学院学报,2015,25(4):119-123.
[8]田有文,程怡,王小奇,等.基于高光谱成像的苹果虫害检测特征向量的选取[J].农业工程学报,2014,30(12):132-139.
[9]PIPAUD I,LEHMKUHL F.Object-based delineation and classification of alluvial fans by application of mean-shift segmentation and support vector machines[J].Geomorphology,2017,293:178-200.
[10]LITJENS G,KOOI T,BEJNORDI B E,et al.A survey on deep learning in medical image analysis[J].Medical Image Analysis,2017,42(9):60-88.
[11]窦立君.激光雷达图像中的林区虫害区域分割与识别[J].激光杂志,2017,38(1):115-118.
[12]丁勇,李楠.基于高维度特征分析的非局部图像质量评价方法[J].电子与信息学报,2016,38(9):2 365-2 370.
[13]赵坤,史学舜,刘长明,等.用于探测器中红外绝对光谱响应度测量的激光源[J].红外与激光工程,2016,45(7):74-80.
[14]周靖鸿,周璀,朱建军,等.基于非下采样轮廓波变换遥感影像超分辨重建方法[J].光学学报,2015,35(1):106-114.
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[16]LV Du-liang,JIA Zhen-hong,YANG Jie,et al.Remote sensing image enhancement based on the combination of nonsubsampled shearlet transform and guided filtering[J].Optical Engineering,2016,55(10):103-104.