摘要
枇杷果味甘酸,供生食、蜜饯、酿酒用,有化痰止咳、和胃降气之效,是春夏之交的度淡水果。枇杷皮薄、质细、松软多汁,在采摘及藏运过程中极易发生碰伤,造成经济损失,因此对碰伤枇杷的高精度快速分级检测处理至关重要。针对几种碰伤程度的枇杷可以选用不同的方法以减少经济损失,轻度碰伤的可以制作枇杷汁、枇杷膏等;中度碰伤的可以去除损伤部分制作枇杷罐头进行保存;重度碰伤的直接处理掉节约仓储成本。目前枇杷的碰伤程度主要通过操作员的肉眼进行损伤辨别,受到个人习惯、光线强度和主观心理因素影响,会对不同碰伤程度的枇杷造成误分类。故此提出基于高光谱成像技术图谱特征融合的方法对枇杷碰伤程度进行高精度、快速、无损分级。首先,利用自由落体碰撞装置制备轻度、中度、重度碰伤三组样品,并利用高光谱成像系统采集各样品数据;其次选用感兴区内100个像素点的平均光谱作为样本光谱并用多元散射校正(MSC)对光谱进行预处理,作为光谱特征用于后续模型使用;最后将光谱数据结合枇杷样品的颜色特征,利用随机森林(RF)、偏最小二乘法判别分析(PLS-DA)、极限学习机(ELM)、最小二乘支持向量机(LS-SVM)分别建立基于枇杷光谱特征、 RGB颜色特征结合光谱特征、 HSI颜色特征结合光谱特征、混合颜色特征结合光谱特征的枇杷碰伤程度模型,在所有模型中混合图像特征结合光谱特征的枇杷碰伤程度模型预测效果最好,利用RF、 PLS-DA、 ELM、 LS-SVM算法的模型整体识别准确率分别为91.11%、 86.67%、 95.56%、 100%,其中基于RBF核函数的LS-SVM碰伤枇杷模型准确率最高。研究结果说明:单一光谱特征模型准确率最低,结合RGB颜色特征、 HSI颜色特征后具有更高的准确率,光谱特征结合混合颜色特征建立的模型准确率最高,该研究为水果碰伤程度判别提�
Loquat is a freshwater fruit at the turn of spring and summer,it has a sour taste and can be eaten directly or made into candied fruit or wine,and it has the effect of resolving phlegm,relieving cough,harmonizing the stomach and lowering gas.The texture of loquat is soft and juicy,so it is prone to be bruised during picking,storage and transportation,resulting in economic losses.Therefore,detecting bruised loquats with high precision and rapid classification is essential.Meanwhile,we have used different methods to treat loquats with different bruising levels to reduce economic losses.The ones with light bruises can make loquat juice and paste.The ones with moderate bruises can be removed from damage region to make canned loquats for preservation.The ones with heavy bruises can be disposed of directly to save storage costs.At present,the bruise level of loquats is mainly discriminated by the operator’s naked eye.It is affected by personal habits,light intensity and subjective psychological factors,which will cause misclassification.In this paper,we propose a method based on hyperspectral imaging technology spectral combined with color features to classify loquat bruise level with high precision,rapidity and non-destructiveness.Firstly,we used a free-fall collision device to prepare light,moderate and heavy bruised loquat samples and used a hyperspectral imaging system to collect data.Secondly,we select the average spectrum of 100 pixels in the region of interest as the sample spectrum and preprocess the spectrum with MSC,which is used as the spectral feature for the subsequent model.Finally,we combined spectral features with color features and used RF,PLS-DA,ELM,and LS-SVM to build loquat bruising level models based on spectral features,RGB color features combined with spectral features,HSI color features combined with spectral features,and mixed color features combined with spectral features,respectively.Among all the above models,the loquat bruise level model based on mixed color features combined with spectra
作者
李斌
韩昭洋
王秋
孙赵祥
刘燕德
LI Bin;HAN Zhao-yang;WANG Qiu;SUN Zhao-xiang;LIU Yan-de(Institute of Optical-Electro-Mechatronics Technology and Application,East China Jiaotong University,National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment,Nanchang 330013,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2023年第6期1792-1799,共8页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(31760344)
国家科技奖后备项目培育计划项目(20192AEI91007)资助。
关键词
枇杷
高光谱成像
光谱特征
颜色特征
碰伤程度
最小二乘支持向量机
Loquat
Hyperspectral imaging
Spectral features
Color features
Bruising level
Least squares support vector machine
