基于机器视觉的玉米性状参数与近红外光谱的玉米组分含量检测方法研究
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摘要
玉米是世界三大作物之一,是我国重要的粮食和饲料作物。玉米产量和品质的好坏直接关系到玉米的利用率、等级及经济效益。开展基于机器视觉技术的玉米产量相关性状参数在线检测系统研究,为玉米育种、栽培及植物新品种特异性、一致性和稳定性测试等科研实践提供快速数据采集方法；开展基于近红外光谱的玉米籽粒组分含量检测方法研究,为玉米育种、改良等方面提供技术支持,有利于提高玉米籽粒的利用价值、促进玉米加工业的发展。
本文研究了玉米表型参数中的产量相关性状参数在线检测方法,并在此基础上研制玉米性状参数自动检测系统一套,用于提取穗重、穗长、穗粗、穗行数、行粒数、粒色、轴重、粒长、粒宽、粒厚、百粒重等参数；研究了基于近红外光谱的玉米籽粒3组分(氨基酸、蛋白质、直链淀粉)含量的定量分析方法,并建立了3组分含量检测的定量分析模型。主要研究结果如下：
1)设计并研制了基于机器视觉的玉米果穗和籽粒性状参数自动提取硬件系统,包括输送系统、基于PLC的检测控制系统、图像采集系统、PLC与PC串口通信系统；开发了基于Lab VIEW的果穗和籽粒图像特征提取系统,包括图像采集、图像处理、参数自动保存、穗重自动获取、PC机与PLC串口通信软件的开发；开发了基于PLC的输送线控制软件。
2)对玉米性状参数自动检测系统进行了试验研究,并验证了系统精度。通过对200个玉米果穗和200粒玉米籽粒的检测试验,结果表明：马齿型玉米穗长的测量相对误差都在5%范围内；91.2%马齿型玉米穗粗的测量相对误差在5%范围内；86.1%硬粒形玉米穗长的测量相对误差在5%范围内；97.4%硬粒形玉米穗粗的测量相对误差在5%范围内；80.6%穗行数的测量相对误差在10%范围内；82.0%行粒数测量相对误差在10%范围内。
3)基于近红外光谱分析技术研究了玉米籽粒组分含量检测的异常样本剔除方法。分别采用杠杆值法(Leverage)、半数重采样法(Resampling by Half-Mean,RHM)和蒙特卡洛采样(Monte-Carlo Sampling, MCS)法剔除玉米籽粒氨基酸、蛋白质和直链淀粉数据中的异常样本,并建立PLS模型。试验结果表明,依据不同原理的异常样本剔除法对玉米籽粒组分定量模型的预测结果有影响,确定RHM为玉米籽粒氨基酸含量的最佳异常样本剔除法,杠杆值法为玉米籽粒蛋白质含量的最佳异常值剔除法,MCS法为玉米籽粒直链淀粉的最佳异常样本剔除法。
4)基于近红外光谱分析技术研究了玉米籽粒组分含量检测的样本集划分方法,确定Sample set partitioning based on joint x-y distance (SPXY)法为玉米籽粒3组分含量的最佳样本集划分方法。分别采用随机法(RS)、Kennard-Stone法(KS)和SPXY法对玉米籽粒样本进行划分,并建立偏最小二乘(PLS)模型。结果表明：不同样本划分方法对模型的预测结果有影响,采用SPXY法划分样本集所建PLS模型的预测效果优于RS法和KS法。
5)基于近红外光谱分析技术研究了采用偏最小二乘支持向量机(LS-SVM)建模时参数的优化方法。分别采用小生境蚁群算法和网格搜索法优化LS-SVM径向基核函数的参数γ和σ2,结果表明,运用小生境蚁群算法优化模型的参数γ,和σ2能提高模型的预测精度和优化速度。
6)基于近红外光谱分析技术研究了玉米籽粒3组分含量检测的不同光谱预处理和建模方法。利用SPXY法分别对剔除异常样本后的氨基酸、蛋白质和直链淀粉样本进行样本划分,分别建立偏最小二乘(PLS)、偏最小二乘支持向量机(LS-SVM)模型。采用标准化、标准正态变量变换、正交信号校正、中心化、多元散射校正、Savizky-Golay平滑、一阶导数及它们的组合预处理方法与原始光谱的建模效果进行对比分析。结果表明：采用LS-SVM进行玉米籽粒氨基酸、蛋白质和直链淀粉含量检测效果较优。附加散射校正(MSC)+正交信号校正(OSC)+标准化组合为检测玉米氨基酸含量的最佳预处理方法,所建LS-SVM模型的预测值与实测值的相关系数R为0.997,标准偏差(RMSEP)为0.019；正交信号校正(OSC)+标准化为检测玉米蛋白质含量的最佳预处理方法,模型的预测值与实测值的相关系数R为0.999,RMSEP为0.019；Detrend+MC为检测玉米直链淀粉含量的最佳预处理方法,模型的预测值与实测值的相关系数R为0.999,RMSEP为0.068。
7)基于近红外光谱分析技术研究了玉米籽粒3组分含量的特征波长优选方法。分别在全谱(4000-10000cm-i)、合频波段(4000～5500cm-1)、一倍频波段(5500～7000cm-1)、二倍频(含高频)波段(7000～10000cm-1)、根据相关系数等优选的波段和采用遗传算法优选的波段建立玉米籽粒3组分含量的定量分析模型,试验结果表明,采用遗传算法优选变量所建模型的性能较差；当采用PLS法在各个波段建模时,模型的预测相关系数较小,RMSEP都较大,而采用LS-SVM建模则相关系数都高于0.990,RMSEP都较小。
Maize is one of the world's three major crops and is one of the most important food and feed crop in China. The maize's yield and quality is directly related to the utilization rate of maize, the grade of goods and economic benefits. Therefore, it has a scientific and practical value to find a new method for automatic extracting the maize yield-related traits and determination of main component content in maize simultaneously.
This article focused on the digital extraction methods for maize yield-related traits and design of a prototype system for extracting ear length, ear width, ear weight, spike rows, line grain number, cob weight, grain length, grain width, grain thickness, grain colors and100grains weight. The methods of measuring maize amino acid, protein and amylose based on near-infrared spectroscopy(NIR) were studied and the models for measuring maize amino acid, protein and amylase were built. The main results are as follows:
1) The automatic extraction system of maize yield-related traits were designed based on machine vision, including conveying module, PLC control module, image acquisition module, serial communication system between PLC and PC.
2) The performance of system for automatic extracting maize yield-related traits were studied and the accuracy of the system was verified.200maize ears and200grains were measured with the system and the results showed that the ear length relative errors of dent maize were all within5%and the maize ear width relative errors of91.2%dent maizes were within5%. For durum maize ear,86.1%of the maize ear length relative errors were within5%and97.4%of maize ear width relative errors were within5%.80.61%of maize spike rows relative errors were within10%and82%of maize line grain number were within10%.
3) The different methods of eliminating abnormal samples for measuring maize grain components content were studied based on near infrared spectroscopy(NIR). The leverage value method, resampling by half-mean(RHM) and monte carlo sampling (MCS) were used to eliminate abnormal samples from amino acid, protein and amylose in maize grains and their PLS models were built, respectively. Experimental results showed that the RHM was the optimum method for eliminating outlier for measuring amino acid in maize grains, leverage value method for protein content in maize grains and MCS for amylose content in maize grain.
4) The different sample set partitioning methods and modeling results were studied based on NIR and sample set partitioning based on joint X-Y distance algorithm (SPXY) was chosen as the optimal method. Random method (RS), Kennard-Stone (KS) and SPXY were used to divide maize grain samples into calibration set and validation set, respectively. The partial least squares (PLS) models were established for measuring amino acid, protein and amylose in maize grains. The results showed that SPXY was the optimal method for sample set partitioning for measuring maize component contents.
5) Different parameter optimization methods for partial least squares support vector machine(LS-SVM) modeling were studied. Niche ant colony algorithm(NACA) and grid search were used to optimize the parameters y and a2of LS-SVM, respectively. The results showed that using NACA optimization method could improve the accuracy of LS-SVM model and optimization speed.
6) The influence of different preprocessing methods and modeling method was compared for3component models of maize grains. Based on SPXY, models by PLS and LS-SVM were built. The influence of different spectral pretreatments on model performance was analyzed. The results showed that the performance of LS-SVM for measuring maize grain amino acid, protein and amylose content was much better than PLS. The performance of LS-SVM with multiplicative scatter correction (MSC) plus orthogonal signal correction (OSC) and plus autoscale was optimal for maize grain amino acid which the correlation coefficient R of validation set is0.997and root mean square error of prediction (RMSEP) is0.019. The performance of LS-SVM with orthogonal signal correction (OSC) plus autoscale was optimal for maize grain protein which the correlation coefficient R of validation set was0.999and root mean square error of prediction (RMSEP) was0.019. The performance of LS-SVM with detrend plus mean center(MC) was optimal for maize grain amylose which the correlation coefficient R of validation set was0.999and the RMSEP was0.068.
7) Different variable selection methods for3component models were studed based on NIR. The models were built in the full spectrum (4000～10000cm-1), the combinaiton region (4000～5500cm-1), the first overtone region (5500～7000cm-1), the second overtone region(7000～10000cm-1), and the wavelengths selected by correlation coefficient method or genetic algorithm(GA). The results showed that the performance of models built with the wavelengths selected by GA were not good. The performance of LS-SVM models built in different regions were better than PLS models.
本文研究了玉米表型参数中的产量相关性状参数在线检测方法,并在此基础上研制玉米性状参数自动检测系统一套,用于提取穗重、穗长、穗粗、穗行数、行粒数、粒色、轴重、粒长、粒宽、粒厚、百粒重等参数；研究了基于近红外光谱的玉米籽粒3组分(氨基酸、蛋白质、直链淀粉)含量的定量分析方法,并建立了3组分含量检测的定量分析模型。主要研究结果如下：
1)设计并研制了基于机器视觉的玉米果穗和籽粒性状参数自动提取硬件系统,包括输送系统、基于PLC的检测控制系统、图像采集系统、PLC与PC串口通信系统；开发了基于Lab VIEW的果穗和籽粒图像特征提取系统,包括图像采集、图像处理、参数自动保存、穗重自动获取、PC机与PLC串口通信软件的开发；开发了基于PLC的输送线控制软件。
2)对玉米性状参数自动检测系统进行了试验研究,并验证了系统精度。通过对200个玉米果穗和200粒玉米籽粒的检测试验,结果表明：马齿型玉米穗长的测量相对误差都在5%范围内；91.2%马齿型玉米穗粗的测量相对误差在5%范围内；86.1%硬粒形玉米穗长的测量相对误差在5%范围内；97.4%硬粒形玉米穗粗的测量相对误差在5%范围内；80.6%穗行数的测量相对误差在10%范围内；82.0%行粒数测量相对误差在10%范围内。
3)基于近红外光谱分析技术研究了玉米籽粒组分含量检测的异常样本剔除方法。分别采用杠杆值法(Leverage)、半数重采样法(Resampling by Half-Mean,RHM)和蒙特卡洛采样(Monte-Carlo Sampling, MCS)法剔除玉米籽粒氨基酸、蛋白质和直链淀粉数据中的异常样本,并建立PLS模型。试验结果表明,依据不同原理的异常样本剔除法对玉米籽粒组分定量模型的预测结果有影响,确定RHM为玉米籽粒氨基酸含量的最佳异常样本剔除法,杠杆值法为玉米籽粒蛋白质含量的最佳异常值剔除法,MCS法为玉米籽粒直链淀粉的最佳异常样本剔除法。
4)基于近红外光谱分析技术研究了玉米籽粒组分含量检测的样本集划分方法,确定Sample set partitioning based on joint x-y distance (SPXY)法为玉米籽粒3组分含量的最佳样本集划分方法。分别采用随机法(RS)、Kennard-Stone法(KS)和SPXY法对玉米籽粒样本进行划分,并建立偏最小二乘(PLS)模型。结果表明：不同样本划分方法对模型的预测结果有影响,采用SPXY法划分样本集所建PLS模型的预测效果优于RS法和KS法。
5)基于近红外光谱分析技术研究了采用偏最小二乘支持向量机(LS-SVM)建模时参数的优化方法。分别采用小生境蚁群算法和网格搜索法优化LS-SVM径向基核函数的参数γ和σ2,结果表明,运用小生境蚁群算法优化模型的参数γ,和σ2能提高模型的预测精度和优化速度。
6)基于近红外光谱分析技术研究了玉米籽粒3组分含量检测的不同光谱预处理和建模方法。利用SPXY法分别对剔除异常样本后的氨基酸、蛋白质和直链淀粉样本进行样本划分,分别建立偏最小二乘(PLS)、偏最小二乘支持向量机(LS-SVM)模型。采用标准化、标准正态变量变换、正交信号校正、中心化、多元散射校正、Savizky-Golay平滑、一阶导数及它们的组合预处理方法与原始光谱的建模效果进行对比分析。结果表明：采用LS-SVM进行玉米籽粒氨基酸、蛋白质和直链淀粉含量检测效果较优。附加散射校正(MSC)+正交信号校正(OSC)+标准化组合为检测玉米氨基酸含量的最佳预处理方法,所建LS-SVM模型的预测值与实测值的相关系数R为0.997,标准偏差(RMSEP)为0.019；正交信号校正(OSC)+标准化为检测玉米蛋白质含量的最佳预处理方法,模型的预测值与实测值的相关系数R为0.999,RMSEP为0.019；Detrend+MC为检测玉米直链淀粉含量的最佳预处理方法,模型的预测值与实测值的相关系数R为0.999,RMSEP为0.068。
7)基于近红外光谱分析技术研究了玉米籽粒3组分含量的特征波长优选方法。分别在全谱(4000-10000cm-i)、合频波段(4000～5500cm-1)、一倍频波段(5500～7000cm-1)、二倍频(含高频)波段(7000～10000cm-1)、根据相关系数等优选的波段和采用遗传算法优选的波段建立玉米籽粒3组分含量的定量分析模型,试验结果表明,采用遗传算法优选变量所建模型的性能较差；当采用PLS法在各个波段建模时,模型的预测相关系数较小,RMSEP都较大,而采用LS-SVM建模则相关系数都高于0.990,RMSEP都较小。
Maize is one of the world's three major crops and is one of the most important food and feed crop in China. The maize's yield and quality is directly related to the utilization rate of maize, the grade of goods and economic benefits. Therefore, it has a scientific and practical value to find a new method for automatic extracting the maize yield-related traits and determination of main component content in maize simultaneously.
This article focused on the digital extraction methods for maize yield-related traits and design of a prototype system for extracting ear length, ear width, ear weight, spike rows, line grain number, cob weight, grain length, grain width, grain thickness, grain colors and100grains weight. The methods of measuring maize amino acid, protein and amylose based on near-infrared spectroscopy(NIR) were studied and the models for measuring maize amino acid, protein and amylase were built. The main results are as follows:
1) The automatic extraction system of maize yield-related traits were designed based on machine vision, including conveying module, PLC control module, image acquisition module, serial communication system between PLC and PC.
2) The performance of system for automatic extracting maize yield-related traits were studied and the accuracy of the system was verified.200maize ears and200grains were measured with the system and the results showed that the ear length relative errors of dent maize were all within5%and the maize ear width relative errors of91.2%dent maizes were within5%. For durum maize ear,86.1%of the maize ear length relative errors were within5%and97.4%of maize ear width relative errors were within5%.80.61%of maize spike rows relative errors were within10%and82%of maize line grain number were within10%.
3) The different methods of eliminating abnormal samples for measuring maize grain components content were studied based on near infrared spectroscopy(NIR). The leverage value method, resampling by half-mean(RHM) and monte carlo sampling (MCS) were used to eliminate abnormal samples from amino acid, protein and amylose in maize grains and their PLS models were built, respectively. Experimental results showed that the RHM was the optimum method for eliminating outlier for measuring amino acid in maize grains, leverage value method for protein content in maize grains and MCS for amylose content in maize grain.
4) The different sample set partitioning methods and modeling results were studied based on NIR and sample set partitioning based on joint X-Y distance algorithm (SPXY) was chosen as the optimal method. Random method (RS), Kennard-Stone (KS) and SPXY were used to divide maize grain samples into calibration set and validation set, respectively. The partial least squares (PLS) models were established for measuring amino acid, protein and amylose in maize grains. The results showed that SPXY was the optimal method for sample set partitioning for measuring maize component contents.
5) Different parameter optimization methods for partial least squares support vector machine(LS-SVM) modeling were studied. Niche ant colony algorithm(NACA) and grid search were used to optimize the parameters y and a2of LS-SVM, respectively. The results showed that using NACA optimization method could improve the accuracy of LS-SVM model and optimization speed.
6) The influence of different preprocessing methods and modeling method was compared for3component models of maize grains. Based on SPXY, models by PLS and LS-SVM were built. The influence of different spectral pretreatments on model performance was analyzed. The results showed that the performance of LS-SVM for measuring maize grain amino acid, protein and amylose content was much better than PLS. The performance of LS-SVM with multiplicative scatter correction (MSC) plus orthogonal signal correction (OSC) and plus autoscale was optimal for maize grain amino acid which the correlation coefficient R of validation set is0.997and root mean square error of prediction (RMSEP) is0.019. The performance of LS-SVM with orthogonal signal correction (OSC) plus autoscale was optimal for maize grain protein which the correlation coefficient R of validation set was0.999and root mean square error of prediction (RMSEP) was0.019. The performance of LS-SVM with detrend plus mean center(MC) was optimal for maize grain amylose which the correlation coefficient R of validation set was0.999and the RMSEP was0.068.
7) Different variable selection methods for3component models were studed based on NIR. The models were built in the full spectrum (4000～10000cm-1), the combinaiton region (4000～5500cm-1), the first overtone region (5500～7000cm-1), the second overtone region(7000～10000cm-1), and the wavelengths selected by correlation coefficient method or genetic algorithm(GA). The results showed that the performance of models built with the wavelengths selected by GA were not good. The performance of LS-SVM models built in different regions were better than PLS models.
引文
1. 白光红,张义荣,刘弋菊,等.ImageJ图象处理软件在测量玉米子粒大小中的应用[J].玉米科学,2009,17(1)：147～151
2. 蔡鑫茹,刘广新,焦仁海.近红外光谱仪测定玉米子粒淀粉含量的研究[J].吉林农业科学.2006,31(6)：10～11
3. 蔡行健,黄文钰,李娟.深入浅出西门子S7-200PLC[M].北京航空航天大学出版社,2011
4. 曹璞,潘涛,陈星旦.小型近红外玉米蛋白质成分分析仪器设计的波段选择[J].光学精密工程.2007,15(12)：1952-1958
5. 曹阳,卢翠,许冰,等.基于Lab VIEW与S7-200PLC的飞机电器装置检测系统[J].2011,19(23)：116～119
6. 陈斌,邹贤勇,朱文静.PCA结合马氏距离法剔除近红外异常样品[J].江苏大学学报(自然科学版),2008,29(4)：277～279,292
7. 陈兵旗,孙旭东,韩旭,等.基于机器视觉的水稻种子精选技术[J].农业机械学报,2010,41(7)：168～173,180
8. 程志颖,孔浩辉,张俊,柏文良,甘峰.粒子群算法结果支持向量机回归法用于近红外光谱建模[J].分析测试学报,2010,29(12)：1215～1219
9. 戴景瑞.我国玉米生产发展的前景及对策[J].作物杂志,1998,(5)：6～11
10.邓星钟.机电传动控制[M].华中科技大学出版社,2007
11.段民孝,范弘伟,王元东,等.利用近红外分析技术测定玉米子粒品质与进行品质育种的探讨[J].玉米科学,2006,14(3)：60-62,65
12. 高荣强,范世福,严衍禄,等.近红外光谱的数据预处理研究[J].光谱学与光谱分析.2004,24(12)：1563～1565
13.高文淑,景茂,严衍禄.付里叶变换近红外漫反射光谱法测定谷子、玉米中多种氨基酸含量[J].北京农业大学学报,1990,16(增刊)：88～93
14.郭婷婷,邬文锦,苏谦,等.近红外玉米品种鉴别系统预处理和波长选择方法[J].农业机械学报,2009,40(9增干U)：87～92
15.谷筱玉,徐可欣,汪燕.波长选择算法在近红外光谱法中药有效成分测量中的应用[J].光谱学与光谱分析,2006,26(9)：1618～1620
16.韩仲志,杨锦忠.计数玉米穗行数的机器视觉研究[J].玉米科：学,2010,18(2)：146-148,152
17郝勇,陈斌,朱锐.近红外光谱预处理中几种小波消噪方法的分析[J].光谱学与光谱分析,2006,26(10)：1838-1841
18.何平.剔除测量数据中异常值的若干方法[J].航空计测技术.1995,15(1)：19～22
19.何奇文.OPC技术在LabVIEW8.0 DSC模块中的运用[J].计算机工程与设计.2006,27(22)：4389～4391
20.何胜美,李仲来,何中虎.基于图像识别的小麦品种分类研究[J].中国农业科学,2005,38 (9)：1869-1875
21.黄星奕,吴守一,方如明,等.计算机视觉在大米胚芽识别中的应用[J].农业机械学报,2000,31(1)：62～65
22.黄星奕,李剑,姜松.基于计算机视觉的稻谷品种识别技术的研究[J].江苏大学学报(自然科学版),2004,25(2)：102～104
23.侯艳霞.混合式步进电机工作原理及其PLC控制[J].科技创新导报,2009,(28)：96～97
24.侯振雨,汤长青,姚树文,等.离散小波变换-支持向量回归方法及其在谷物分析中的应用[J].河南农业科学,2006,(8)：40"'42
25.李彦苍,索娟娟.基于熵的小生境蚁群算法及其应用[J].四川大学学报(工程科学版).2007,39 Supp:229-232
26.李红梁.基于OPC的PC与PLC实时通讯的LabVIEW实现[J].计算机应用研究.2003,12：115～118
27.李民赞.光谱分析技术及其应用口讧].北京：科学出版社,2006.
28.林静,林振宇,郑福仁.LabVIEW虚拟仪器程序设计从入门到精通[M].人民邮电出版社,2010
29.林敏,吕进.基于神经网络与近红外光谱的玉米成分检测方法[J].红外技术,2004,26(3)：78-81
30.刘建学.实用近红外光谱分析技术[M].北京：科学出版社,2008
31.刘建学,吴守一,方如明.大米直链淀粉含量的近红外光谱分析[J].农业工程学报,2000,16(3)：94～96
32.刘蓉,陈文亮,徐可欣,等.奇异点快速检测在牛奶成分近红外光谱测量中的应用[J].光谱学与光谱分析,2005,25(2)：207-210
33.刘燕德,彭彦颖,高荣杰,等.基于LED组合光源的水晶梨可溶性固形物和大小在线检测[J].农业工程学报,2010,26(22)：339～343
34.刘智超,蔡文生,邵学广.蒙特卡洛交叉验证用于近红外光谱奇异样本的识别[J].中国科学B辑：化学,2008,38(4)：316～323
35.陆婉珍.现代近红外光谱分析技术[M].北京：中国石化出版社,2007
36.马振锋,刘献礼,王鹏,等.基于7.1的PC机与PLC通信[J].哈尔滨理工大学学报,2005,10(5)：30～33,36
37.孟军,张振兴,刘波.二相步进电机细分驱动的设计与实现[J].电机技术,2007,26(12)：84～87
38.闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化[J].光谱学与光谱分析,2004,24(10)：1205-1209
39.宁纪锋,何东健,杨蜀秦.玉米籽粒的尖端和胚部的计算机视觉识别[J].农业工程学报,2004,20(3)：117～119
40.彭建,张正茂.小麦籽粒淀粉和直链淀粉含量的近红外漫反射光谱法快速检测[J].麦类作物学报,2010,30(2)：276～279
41.瞿海斌.基于PLS的建模方法[J].浙江大学学报(工学版),1999,33(5)：471-474
42.饶洪辉,刘燕德,孙旭东,等.基于机器视觉的水稻种子质量在线检测机[J].农机化研究,2009,(10)：79～81,88
43.饶秀勤,应义斌.水果按表面颜色分级的方法[J].浙江大学学报(工学版),2009,43(5)：869～871
44.史智兴,程洪,李江涛,等.图像处理识别玉米品种的特征参数研究[J].农业工程学报,2008,24(6)：193-195
45.时文飞,侯世英,张柯,等.PLC与计算机的串行通信程序设计[J].机床电器.2005,(4)：30N32,36
46.宋鹏,张俊雄,苟一,等.玉米种子自动精选系统开发[J].农业工程学报,2010,26(9)：124～127
47.苏谦,邬文锦,王红武,等.基于近红外光谱和仿生模式识别玉米品种快速鉴别方法[J].光谱学与光谱分析,2009,29(9)：2413～2416
48.田高友,袁洪福,刘慧颖,等.小波变换在近红外光谱分析中的应用进展[J].光谱学与光谱分析,2003,23(6)：1111～1114
49.王慧慧,孙永海,张贵林,等.基于压力和图像的鲜玉米果穗成熟度分级方法[J].农业工程学报,2010,26(7)：369～373
50.王慧慧,孙永海,张婷婷,等.鲜食玉米果穗外观品质分级的计算机视觉方法[J].农业机械学报,2010,41(8)：156-159,165
51.王龙.葡萄糖溶液浓度检测的预测模型研究[D].武汉：华中科技大学硕士研究生学位论文,2006
52.王鹏飞.西门子$7-200在步进电机定位控制中的应用[J].工控机与集散控制系统,2005,(5)：50～53
53.王铁固,刘新香,库丽霞,等.近红外反射光谱测定玉米完整子粒蛋白质和淀粉含量的校正模型[J].玉米科学,2008,16(3)：57-59,63
54.王文真,付翠真.利用近红外反射光谱快速测定大豆籽粒蛋白质、脂肪和部分氨基酸含量[J].作物品种资源,1994,(1)：31-32
55.王玉亮,刘贤喜,苏庆堂,等.多对象特征提取和优化神经网络的玉米种子品种识别[J].农业工程学报,2010,26(6)：199～204
56.魏良明,严衍禄,戴景瑞.近红外反射光谱测定玉米完整籽粒蛋白质和淀粉含量的研究[J].中国农业科学.2004,37(5)：630-633
57.吴继华,刘燕德,欧刚爱国.基丁机器视觉的种子品种实时检测系统研究[J].传感技术学报.2005,18(4)：742～744
58.吴建国,彳i春海,张小明,等.用近红外反射光谱法分析稻米3种必需氨基酸含量的研究[J].作物学报,2003,29(5)：688～692
59.夏俊芳,李小昱,李培武,等.基丁小波消噪柑橘内部品质近红外光谱的无损检测[J].华中农业大学学报,2007,26(1)：120-123
60.荀一,鲍官军,杨庆华,等.粘连玉米籽粒图像的自动分割方法[J].农业机械学报,2010,41(4)：163～167
61.严衍禄,赵龙莲,韩东海.近红外光谱分析基础与应用[M].北京：中国轻工业出版社,2005
62.杨杰.基于数字图像处理的玉米种子质量分级方法研究[D].武汉：武汉理工大学,2009
63.杨锦忠,张洪生,赵延明,等.玉米穗粒重与果穗三维儿何特征关系的定量研究[J].中国农业科学,2010,43(21)：4367～4374
64.杨锦忠,张洪生,郝建平,等.玉米果穗图像单一特征的品种鉴别力评价[J].农业工程学报,2011,27(1)：196～200
65.杨锦忠,郝建平,杜天庆,等.玉米图像处理技术及其评价初探[J].青岛农业大学学报(自然科学版),2009,26(3)：246～249
66.杨蜀秦,宁纪锋,何东健.BP人工神经网络识别玉米品种的研究[J].西北农林科技大学学报(自然科学版).2004,32 (Suppl.l):162-164
67杨万能.水稻产量相关性状参数自动提取的数学化技术研究[D].武汉：华中科技大学图书馆,2011
68.应义斌,景寒松,马俊福,等.机器视觉技术在黄花梨尺寸和果面缺陷检测中的应用[J].农业工程学报,1999,15(1)：197～200
69.应义斌,付峰.水果品质机器视觉检测中的图像颜色变换模型[J].农业机械学报,2004,35(1)：85～89
70.应以斌.水果尺寸和面积的机器视觉检测方法研究[J].浙江大学学报(农业与生命科学版),2000,26(3)：229～232
71.虞科,程翼宇.一种基于最小二乘支持向量机算法的近红外光谱判别分析方法[J].分析化学研究简报,2006,34(4)：561-564
72.闸建文,陈永艳.基于外部特征的玉米品种计算机识别系统[J].农业机械学报.2004,35(6)：115～117
73.展晓日,朱向荣,史新元,等.SPXY样本划分法及蒙特卡罗交叉验证结合近红外光谱用于橘叶中橙皮苷的含量测定[J].光谱学与光谱分析.2009,29(4)：964-968
74.张芙蓉.马氏距离、Savitzky-Golay平滑求导、SIMPLS结合天麻紫外光谱分析天麻素含量[J].分析测试学报,2012,31(11)：1431～1435
75.张华秀.近红外光谱法快速检测牛奶中蛋白质与脂肪含量[D].中南大学硕十研究生学位论文,2010
76.张俊,张义荣,卢宝红,等.高油玉米群体油分、蛋白质和淀粉含量近红外分析模型的建立[J].玉米科学,2007,15(3)：62～66
77.张军,郑咏梅,王芳荣,等.谷物近红外光谱分析中常用数据处理方法讨论[J].吉林大学学报(信息科学版),2003,21(1)：4-9
78.张巧杰,王一呜,吴静珠.相关成分分析法在大米直链淀粉波长选择中的应用[J].中国农业大学学报,2006,11(2)：74～77
79.赵春明,韩仲忠,杨锦忠,等.玉米果穗DUS性状测试的图像处理应用研究[J].中国农业科学,2009,42(11)：4100～4105
80.赵杰文,呼怀平,邹小波.支持向量机在苹果分类的近红外光谱模型中的应用[J].农业丁科学报,2007,23(4)：149-152
81.赵景波,阿伦,李杰臣等.两门子S7-200PLC实践与应用[M].机械工业山版社,2012
82.赵环环,严衍禄.利朋付里叶近红外漫反射光谱技术快速测定玉米籽粒中蛋白质的含量[J].玉米科学,1999,7(3)：77～79
83.郑冠楠,谭豫之,张俊雄,等.基丁计算机视觉的马铃薯自动检测分级[J].农业机械学报,2009,40(4)：156,166～168
84.郑咏梅,张铁强,张军,等.平滑、导数、基线校正对近红外光谱PLS定量分析的影响研究[J].光谱学与光谱分析,2004,24(12)：1546-1548
85.郑咏梅,张军,陈星旦,等.基于逐步回归法的近红外光谱信息提取及模型的研究[J].光谱学与光谱分析,2004,24(6)：675～678
86.郑咏梅,张军,李荣福,等.小麦近红外特征波长提取及蛋白质含量测定[J].激光与红外,2003,33(2)：125～127
87.周中汉,王汉江,李梅,等.利用DPS剔除测量数据中的异常值[J].计量技术.2007,(10)：61-63
88.周竹,黄懿,李小昱,等.基于机器视觉的马铃薯自动分级方法[J].农业工程学报,2012,28(7)：178～183
89.周竹.基于高光谱成像技术的马铃薯品质无损检测方法研究[D].周竹,华中农业大学图书馆,2012
90.褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展.2004,16(4)：528-540
91. Abbasgholipour M, Omid M, Keyhani A, et al. Color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions [J]. Expert Systems with Applications,2011,38(4):3671-3678
92. Agelet L E, Ellis D D, Duvick S, Goggi A S, Hurburgh C R, Gardner C A. Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels[J]. Journal of Cereal Science,2012,55(2):160-165
93. Ahmed F, Al-Mamun H A, Bari A S M H, et al. Classification of crops and weeds from digital images:A support vector machine approach[J]. Crop Protection,2012,40(10):98-104
94. Baye T M, Pearson T C, Settles A M. Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy [J]. Journal of Cereal Science,2006, (43):236-243
95. Campbell M, Brumm T J, Glover D V. Whole grain anylose analysis in maize using near-infrared transmittance spectroscopy[J]. Cereal Chemistry,1997,74:300-303
96. Cen H Y, He Y. Theory and application of near infrared reflectance spectroscopy in detemination of food quality[J]. Trends in Food Science & Technology,2007,18(2):72-83
97. Chauchard F, Cogdill R, Roussel S, Roger J M, Maurel-Bellon V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy development of a robust and portable sensor for acidity prediction in grapes[J]. Chemometrics and Intelligent Laboratory Systems,2004,71(2): 141-150
98. Chen G L, Zhang B, Wu J G, et al. Nondestructive assessment of amino acid composition in rapeseed meal based on intact seeds by near-infrared reflectance spectroscopy[J]. Animal Feed Science and Technology,2011,165:111-119
99. Cozzolino D, Faasio A, Fernandez E. Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy[J]. Animal Feed Science and Technology,2006,129:329-336
100. Delwiche S R, Yang L Ch, Graybosch R A. Multipe view image analysis of freefalling U.S. wheat grains for damage assessment[J]. Computer and Electronics in Agriculture,2013,98(10):62-73
101. Dowlati M, Mohtasebi S S, Omid M, et al. Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes [J]. Journal of Food Engineering, 2013,119(2):277-287
102. Dubey B P, Bhagwat S G, Shouche S P, et al. Potential of artificial neural networks in varietal identification using morphometry of wheat grains[J]. Bios Engin,2006,95(l):61-67
103. EIMasry G, Cubero S, Molto E, et al. In-line sorting of irregular potatoes by using automated computer-based machine vision systemfJ]. Journal of Food Engineering,2012,112(1-2):60-68
104. Fan L Zh, Liu Y. Automate fry counting using computer vision and multi-class least squares support vector machine[J]. Aquaculture,2013,380-383(3):91-98
105. Fassio A, Fernandez E Q Restaino E A, Manna A La, et al. Predicting the nutritive value of high moisture grain corn by near infrared reflectance spectroscopy[J]. Computers and Electronics in Agriculture,2009,67:59-63
106. Fernandez-lbanez V, Soldado A, Martinez-Fernandez A, et al. Application of near infrared spectroscopy for rapid detection of aflatoxin B1 in maize and barley as analytical quality assessment[J]. Food Chemistry,2009, (113):629-634
107. Fernwandez Pierna J A, Lecler B, Conzen J P, Niemoeller A, Baeten V, Dardenne P. Comparison of various chemometric approaches for large near infrared spectroscopic data of feed and feed products[J]. Analytica Chimica Acta,2011,705(1-2):30-34
108. Ferreira D S, Galao O F, Pallone J A L, Poppi R J. Comparison and application of near-infrared (NIR) and mid-infrared(MIR) spectroscopy for determination of quality parameters in soybean samples[J]. Food Control,2014,35(1):227-232
109. Fertig C C, Podczeck F, Jee R D, Smith M R. Feasibility study for the rapid determination of the amylase content in starch by near-infrared spectroscopy[J]. European Journal of Pharmaceutical Sciences,2004,21(2-3):155-159
110. Giacomo D R, Stefania D Z. A multivariate regression model for detection of fumonisins content in maize from near infrared spectra[J]. Food Chemistry,2013, (141):4289-4294
111. Igathinathane C, Pordesimo L O, Batchelor W D. Major orthogonal dimensions measurement of food grains by machine vision using ImageJ[J]. Food Research International,2009,42(1):76-84
112. Jaillais B, Pinto R, Barros A S, et al. Outer-product analysis (OPA) using PCA to study the influence of temperature on NIR spectra of water[J]. Vibrational Spectroscopy,2005, (39):50-58
113. Khazaei N B, Tavakoli T, Ghassemian H, et al. Applied machine vision and artificial neural network for modeling and controlling of the grape drying process[J]. Computers and Electronics in Agriculture,2013,98(10):205-213
114. Liang X Y, Li X Y, Lei T W, Wang W, Gao Y. Study of sample temperature compensation in the measurement of soil moisture content[J]. Measurement,2011,44(10):2200-2204
115. Liao K, Paulsen M R, Reid J F. Real-Time Detection of Colour and Surface Defects of Maize Kernels Using Machine Vision[J]. Journal of Agricultural Engineering Research,1994,59(4): 263-271
116. Liu Y D, Ying Y B, Fu X P, Lu H SH. Experiments on predicting sugar content in apples by FT-NIR Technique[J]. Journal of Food Engineering,2007,80(3):986-989
117. Luo X, Jayas D S, Symons S J. Identification of Damaged Kernels in Wheat using a Colour Machine Vision System[J]. Journal of Cereal Science,1999,30(1):49-59
118. Ma Q, Jiang J T, Zhu D H, et al. Rapid measurement for 3D geometric features of maize ear based on image processing[J]. Transactions of the Chinese Society of Agricultural Engineering,2012, 28(supp.2):208-212
119. Makky M, Soni P. Development of an automatic grading machine for oil palm fresh fruits bunches (FFBs) based on machine vision[J]. Computers and Electronics in Agriculture,2013,93(4): 129-139
120. Mebatsion H K, Paliwal J. A Fourier analysis based algorithm to separate touching kernels in digital images[J]. Biosystems Engineering,2011, (108):66-74
121. Mebatsion H K, Paliwal J, Jayas D S. Automatic classification of non-touching cereal grains in digital images using limited morphological and color features[J]. Computers and Electronics in Agriculture,2013,90(1):99-105
122. Mladenov M, Draganova T, Tsenkova R, Mustafa M. Quality assessment of grain samples using spectra analysis[J]. Biosystems Engineering,2012,111(3):251-260
123. Nashat S, Abdullah A, Abdullah M Z. Machine vision for crack inspection of biscuits featuring pyramid detection scheme[J]. Journal of Food Engineering,2014,120(1):233-247
124. Ni B, Paulsen M R, Reid J F. Corn kernel crown shape identification using image processing[J]. Trans of the AS AE,1997,40 (3):833-838
125. Orman B A, Schumann Jr R A. Comparison of near-infrared spectroscopy calibration methods for the prediction of protein, oil and starch in maize grain[J]. Journal of Agricultural Food Chemistry, 1991,39:883-888
126. Paulsen M R, Wigger W D, Litchfield J B, et al. Computer image analyses for detection of maize and soybean kernel quality factors[J]. Journal of Agricultural Engineering Research,1989, 43(5-8):93-101
127. Paliwal J, Visen N S, Jayas D S, et al. Cereal Grain and Dockage Identification using Machine Vision[J]. Biosystems Engineering,2003,85(1):51-57
128. Panigrahi S, Misra M K, Bern C, et al. Back-ground segmentation and dimensional measurement of corn germplasm[J]. Trans of the ASAE,1995,38(1):291-297
129. Panigrahi S, Misra M K, Willson S. Evaluations of fractal geometry and invariant moments for shape classification of corn germplasm[J]. Computers and Electronics in Agriculture,1998,20(1):1-20
130. Pasti L, Walczak B, Massart D L, et al. Optimization of signal denoising in discrete wavelet transform[J]. Chemometrics and Intelligent Laboratory Systems,1999, (48):21-34
131. Plans M, Simo J, Casanas F, et al. Characterization of common beans (Phaseolus vulgaris L.) by infrared spectroscopy:Comparison of MIR, FT-NIR and dispersive NIR using portable and benchtop instruments [J].2013,54(2):1643-1651
132. Pojic M P, Mastilovvic J, Palic D, et al. The development of near-infrared spectroscopy(NIRS) calibration for prediction of ash content in legumes on the basis of two different reference methods[J]. Food Chemistry,2010,123:800-805
133. Reid J F, et al. Computer Vision Sensing of Stress Cracks in Corn Kernels[J]. Trans of the ASAE, 1991,34(5):2226-2244
134. Roberto Kawakami Harrop Galvao, Mario Cesar Ugulino Araujo, Gledson Emidio Jose,et al. A method for calibration and validation subset partitioning [J]. Talanta.2005,67:736-740
135. Sabzi S, Javadikia P, Rabani H, et al. Mass modeling of Bam orange with ANFIS and SPSS methods for using in machine vision[J]. Measurement,2013,46(9):3333-3341
136. Salgo A, Gergely S. Analysis of wheat grain development using NIR spectroscopy[J]. Journal of Cereal Science,2012,56(1):31-38
137. Santos R N F D, Galvao R K H, Araujo M C U, et al. Improvement of prediction ability of PLS models employing the wavelet packet transform:A case study concerning FT-IR determination of gasoline parameters [J]. Talanta,2007, (71):1136-1143
138. Shao Y N, Cen Y L, He Y, Liu F. Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice[J]. Food Chemistry,2011,126(4):1856-1861
139. Shiroma C, Rodriguez-Saona L. Application of NIR and MIR spectroscopy in quality control of potato chips[J]. Journal of Food Composition and Analysis,2009,22(6):596-605
140. Sinelli N, Pagani M A, Lucisano M, et al. Prediction of semolina technological quality by FT-NIR spectroscopy[J]. Journal of Cereal Science,2011,54(2):218-223
141. Szczypinski P M, Zapotoczny P. Computer vision algorithm for barley kernel identification, orientation estimation and surface structure assessment J]. Computers and Electronics in Agriculture,2012,87(9):32-38
142. Tallada J G, Palacios-Rojas N, Armstrong P R. Prediction of maize seed attributes using a rapid single kernel near infrared instrument[J]. Journal of Cereal Science,2009,50:381-387
143. Trygg T, Wold S. PLS regression on wavelet compressed NIR spectra[J]. Chemometrics and Intelligent Laboratory Systems,1998, (42):209-220
144. UPOV. General introduction to the examination of distinctness, uniformity and stability and the development of harmonized descriptions of new varieties of plants (TG/1/3). Geneva (Switzerland):The International Union for the Protection of New Varieties of Plants,2002, ppll-11
145. Utku H, Koksel H. Use of statistical filters in the classification of wheat by image analysis[J]. J Food Engin,1998,36(4):385-394
146. Wu D, He Y, Feng Sh J, Sun D W. Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM[J]. Journal of Food Engineering,2008, 84(1):124-131
147. Wu J G, Shi C H. Prediction of grain weight, brown rice weight and amylase content in single rice grains using near-infrared reflectance spectroscopy[J]. Field Crops Research,2004,83(1):13-21
148. Xie L H, Tang S Q, Chen N, Luo J, et al. Optimisation of near-infrared reflectance model in measuring protein and amylase content of rice flour[J]. Food Chemistry,2014,142 (1):92-100
149. Yang W, Winter P, Sokhansanj S, et al. Discrimination of Hard-to-pop Popcorn Kernels by Machine Vision and Neural Networks[J]. Biosystems Engineering,2005,91(1):1-8
2. 蔡鑫茹,刘广新,焦仁海.近红外光谱仪测定玉米子粒淀粉含量的研究[J].吉林农业科学.2006,31(6)：10～11
3. 蔡行健,黄文钰,李娟.深入浅出西门子S7-200PLC[M].北京航空航天大学出版社,2011
4. 曹璞,潘涛,陈星旦.小型近红外玉米蛋白质成分分析仪器设计的波段选择[J].光学精密工程.2007,15(12)：1952-1958
5. 曹阳,卢翠,许冰,等.基于Lab VIEW与S7-200PLC的飞机电器装置检测系统[J].2011,19(23)：116～119
6. 陈斌,邹贤勇,朱文静.PCA结合马氏距离法剔除近红外异常样品[J].江苏大学学报(自然科学版),2008,29(4)：277～279,292
7. 陈兵旗,孙旭东,韩旭,等.基于机器视觉的水稻种子精选技术[J].农业机械学报,2010,41(7)：168～173,180
8. 程志颖,孔浩辉,张俊,柏文良,甘峰.粒子群算法结果支持向量机回归法用于近红外光谱建模[J].分析测试学报,2010,29(12)：1215～1219
9. 戴景瑞.我国玉米生产发展的前景及对策[J].作物杂志,1998,(5)：6～11
10.邓星钟.机电传动控制[M].华中科技大学出版社,2007
11.段民孝,范弘伟,王元东,等.利用近红外分析技术测定玉米子粒品质与进行品质育种的探讨[J].玉米科学,2006,14(3)：60-62,65
12. 高荣强,范世福,严衍禄,等.近红外光谱的数据预处理研究[J].光谱学与光谱分析.2004,24(12)：1563～1565
13.高文淑,景茂,严衍禄.付里叶变换近红外漫反射光谱法测定谷子、玉米中多种氨基酸含量[J].北京农业大学学报,1990,16(增刊)：88～93
14.郭婷婷,邬文锦,苏谦,等.近红外玉米品种鉴别系统预处理和波长选择方法[J].农业机械学报,2009,40(9增干U)：87～92
15.谷筱玉,徐可欣,汪燕.波长选择算法在近红外光谱法中药有效成分测量中的应用[J].光谱学与光谱分析,2006,26(9)：1618～1620
16.韩仲志,杨锦忠.计数玉米穗行数的机器视觉研究[J].玉米科：学,2010,18(2)：146-148,152
17郝勇,陈斌,朱锐.近红外光谱预处理中几种小波消噪方法的分析[J].光谱学与光谱分析,2006,26(10)：1838-1841
18.何平.剔除测量数据中异常值的若干方法[J].航空计测技术.1995,15(1)：19～22
19.何奇文.OPC技术在LabVIEW8.0 DSC模块中的运用[J].计算机工程与设计.2006,27(22)：4389～4391
20.何胜美,李仲来,何中虎.基于图像识别的小麦品种分类研究[J].中国农业科学,2005,38 (9)：1869-1875
21.黄星奕,吴守一,方如明,等.计算机视觉在大米胚芽识别中的应用[J].农业机械学报,2000,31(1)：62～65
22.黄星奕,李剑,姜松.基于计算机视觉的稻谷品种识别技术的研究[J].江苏大学学报(自然科学版),2004,25(2)：102～104
23.侯艳霞.混合式步进电机工作原理及其PLC控制[J].科技创新导报,2009,(28)：96～97
24.侯振雨,汤长青,姚树文,等.离散小波变换-支持向量回归方法及其在谷物分析中的应用[J].河南农业科学,2006,(8)：40"'42
25.李彦苍,索娟娟.基于熵的小生境蚁群算法及其应用[J].四川大学学报(工程科学版).2007,39 Supp:229-232
26.李红梁.基于OPC的PC与PLC实时通讯的LabVIEW实现[J].计算机应用研究.2003,12：115～118
27.李民赞.光谱分析技术及其应用口讧].北京：科学出版社,2006.
28.林静,林振宇,郑福仁.LabVIEW虚拟仪器程序设计从入门到精通[M].人民邮电出版社,2010
29.林敏,吕进.基于神经网络与近红外光谱的玉米成分检测方法[J].红外技术,2004,26(3)：78-81
30.刘建学.实用近红外光谱分析技术[M].北京：科学出版社,2008
31.刘建学,吴守一,方如明.大米直链淀粉含量的近红外光谱分析[J].农业工程学报,2000,16(3)：94～96
32.刘蓉,陈文亮,徐可欣,等.奇异点快速检测在牛奶成分近红外光谱测量中的应用[J].光谱学与光谱分析,2005,25(2)：207-210
33.刘燕德,彭彦颖,高荣杰,等.基于LED组合光源的水晶梨可溶性固形物和大小在线检测[J].农业工程学报,2010,26(22)：339～343
34.刘智超,蔡文生,邵学广.蒙特卡洛交叉验证用于近红外光谱奇异样本的识别[J].中国科学B辑：化学,2008,38(4)：316～323
35.陆婉珍.现代近红外光谱分析技术[M].北京：中国石化出版社,2007
36.马振锋,刘献礼,王鹏,等.基于7.1的PC机与PLC通信[J].哈尔滨理工大学学报,2005,10(5)：30～33,36
37.孟军,张振兴,刘波.二相步进电机细分驱动的设计与实现[J].电机技术,2007,26(12)：84～87
38.闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化[J].光谱学与光谱分析,2004,24(10)：1205-1209
39.宁纪锋,何东健,杨蜀秦.玉米籽粒的尖端和胚部的计算机视觉识别[J].农业工程学报,2004,20(3)：117～119
40.彭建,张正茂.小麦籽粒淀粉和直链淀粉含量的近红外漫反射光谱法快速检测[J].麦类作物学报,2010,30(2)：276～279
41.瞿海斌.基于PLS的建模方法[J].浙江大学学报(工学版),1999,33(5)：471-474
42.饶洪辉,刘燕德,孙旭东,等.基于机器视觉的水稻种子质量在线检测机[J].农机化研究,2009,(10)：79～81,88
43.饶秀勤,应义斌.水果按表面颜色分级的方法[J].浙江大学学报(工学版),2009,43(5)：869～871
44.史智兴,程洪,李江涛,等.图像处理识别玉米品种的特征参数研究[J].农业工程学报,2008,24(6)：193-195
45.时文飞,侯世英,张柯,等.PLC与计算机的串行通信程序设计[J].机床电器.2005,(4)：30N32,36
46.宋鹏,张俊雄,苟一,等.玉米种子自动精选系统开发[J].农业工程学报,2010,26(9)：124～127
47.苏谦,邬文锦,王红武,等.基于近红外光谱和仿生模式识别玉米品种快速鉴别方法[J].光谱学与光谱分析,2009,29(9)：2413～2416
48.田高友,袁洪福,刘慧颖,等.小波变换在近红外光谱分析中的应用进展[J].光谱学与光谱分析,2003,23(6)：1111～1114
49.王慧慧,孙永海,张贵林,等.基于压力和图像的鲜玉米果穗成熟度分级方法[J].农业工程学报,2010,26(7)：369～373
50.王慧慧,孙永海,张婷婷,等.鲜食玉米果穗外观品质分级的计算机视觉方法[J].农业机械学报,2010,41(8)：156-159,165
51.王龙.葡萄糖溶液浓度检测的预测模型研究[D].武汉：华中科技大学硕士研究生学位论文,2006
52.王鹏飞.西门子$7-200在步进电机定位控制中的应用[J].工控机与集散控制系统,2005,(5)：50～53
53.王铁固,刘新香,库丽霞,等.近红外反射光谱测定玉米完整子粒蛋白质和淀粉含量的校正模型[J].玉米科学,2008,16(3)：57-59,63
54.王文真,付翠真.利用近红外反射光谱快速测定大豆籽粒蛋白质、脂肪和部分氨基酸含量[J].作物品种资源,1994,(1)：31-32
55.王玉亮,刘贤喜,苏庆堂,等.多对象特征提取和优化神经网络的玉米种子品种识别[J].农业工程学报,2010,26(6)：199～204
56.魏良明,严衍禄,戴景瑞.近红外反射光谱测定玉米完整籽粒蛋白质和淀粉含量的研究[J].中国农业科学.2004,37(5)：630-633
57.吴继华,刘燕德,欧刚爱国.基丁机器视觉的种子品种实时检测系统研究[J].传感技术学报.2005,18(4)：742～744
58.吴建国,彳i春海,张小明,等.用近红外反射光谱法分析稻米3种必需氨基酸含量的研究[J].作物学报,2003,29(5)：688～692
59.夏俊芳,李小昱,李培武,等.基丁小波消噪柑橘内部品质近红外光谱的无损检测[J].华中农业大学学报,2007,26(1)：120-123
60.荀一,鲍官军,杨庆华,等.粘连玉米籽粒图像的自动分割方法[J].农业机械学报,2010,41(4)：163～167
61.严衍禄,赵龙莲,韩东海.近红外光谱分析基础与应用[M].北京：中国轻工业出版社,2005
62.杨杰.基于数字图像处理的玉米种子质量分级方法研究[D].武汉：武汉理工大学,2009
63.杨锦忠,张洪生,赵延明,等.玉米穗粒重与果穗三维儿何特征关系的定量研究[J].中国农业科学,2010,43(21)：4367～4374
64.杨锦忠,张洪生,郝建平,等.玉米果穗图像单一特征的品种鉴别力评价[J].农业工程学报,2011,27(1)：196～200
65.杨锦忠,郝建平,杜天庆,等.玉米图像处理技术及其评价初探[J].青岛农业大学学报(自然科学版),2009,26(3)：246～249
66.杨蜀秦,宁纪锋,何东健.BP人工神经网络识别玉米品种的研究[J].西北农林科技大学学报(自然科学版).2004,32 (Suppl.l):162-164
67杨万能.水稻产量相关性状参数自动提取的数学化技术研究[D].武汉：华中科技大学图书馆,2011
68.应义斌,景寒松,马俊福,等.机器视觉技术在黄花梨尺寸和果面缺陷检测中的应用[J].农业工程学报,1999,15(1)：197～200
69.应义斌,付峰.水果品质机器视觉检测中的图像颜色变换模型[J].农业机械学报,2004,35(1)：85～89
70.应以斌.水果尺寸和面积的机器视觉检测方法研究[J].浙江大学学报(农业与生命科学版),2000,26(3)：229～232
71.虞科,程翼宇.一种基于最小二乘支持向量机算法的近红外光谱判别分析方法[J].分析化学研究简报,2006,34(4)：561-564
72.闸建文,陈永艳.基于外部特征的玉米品种计算机识别系统[J].农业机械学报.2004,35(6)：115～117
73.展晓日,朱向荣,史新元,等.SPXY样本划分法及蒙特卡罗交叉验证结合近红外光谱用于橘叶中橙皮苷的含量测定[J].光谱学与光谱分析.2009,29(4)：964-968
74.张芙蓉.马氏距离、Savitzky-Golay平滑求导、SIMPLS结合天麻紫外光谱分析天麻素含量[J].分析测试学报,2012,31(11)：1431～1435
75.张华秀.近红外光谱法快速检测牛奶中蛋白质与脂肪含量[D].中南大学硕十研究生学位论文,2010
76.张俊,张义荣,卢宝红,等.高油玉米群体油分、蛋白质和淀粉含量近红外分析模型的建立[J].玉米科学,2007,15(3)：62～66
77.张军,郑咏梅,王芳荣,等.谷物近红外光谱分析中常用数据处理方法讨论[J].吉林大学学报(信息科学版),2003,21(1)：4-9
78.张巧杰,王一呜,吴静珠.相关成分分析法在大米直链淀粉波长选择中的应用[J].中国农业大学学报,2006,11(2)：74～77
79.赵春明,韩仲忠,杨锦忠,等.玉米果穗DUS性状测试的图像处理应用研究[J].中国农业科学,2009,42(11)：4100～4105
80.赵杰文,呼怀平,邹小波.支持向量机在苹果分类的近红外光谱模型中的应用[J].农业丁科学报,2007,23(4)：149-152
81.赵景波,阿伦,李杰臣等.两门子S7-200PLC实践与应用[M].机械工业山版社,2012
82.赵环环,严衍禄.利朋付里叶近红外漫反射光谱技术快速测定玉米籽粒中蛋白质的含量[J].玉米科学,1999,7(3)：77～79
83.郑冠楠,谭豫之,张俊雄,等.基丁计算机视觉的马铃薯自动检测分级[J].农业机械学报,2009,40(4)：156,166～168
84.郑咏梅,张铁强,张军,等.平滑、导数、基线校正对近红外光谱PLS定量分析的影响研究[J].光谱学与光谱分析,2004,24(12)：1546-1548
85.郑咏梅,张军,陈星旦,等.基于逐步回归法的近红外光谱信息提取及模型的研究[J].光谱学与光谱分析,2004,24(6)：675～678
86.郑咏梅,张军,李荣福,等.小麦近红外特征波长提取及蛋白质含量测定[J].激光与红外,2003,33(2)：125～127
87.周中汉,王汉江,李梅,等.利用DPS剔除测量数据中的异常值[J].计量技术.2007,(10)：61-63
88.周竹,黄懿,李小昱,等.基于机器视觉的马铃薯自动分级方法[J].农业工程学报,2012,28(7)：178～183
89.周竹.基于高光谱成像技术的马铃薯品质无损检测方法研究[D].周竹,华中农业大学图书馆,2012
90.褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展.2004,16(4)：528-540
91. Abbasgholipour M, Omid M, Keyhani A, et al. Color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions [J]. Expert Systems with Applications,2011,38(4):3671-3678
92. Agelet L E, Ellis D D, Duvick S, Goggi A S, Hurburgh C R, Gardner C A. Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels[J]. Journal of Cereal Science,2012,55(2):160-165
93. Ahmed F, Al-Mamun H A, Bari A S M H, et al. Classification of crops and weeds from digital images:A support vector machine approach[J]. Crop Protection,2012,40(10):98-104
94. Baye T M, Pearson T C, Settles A M. Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy [J]. Journal of Cereal Science,2006, (43):236-243
95. Campbell M, Brumm T J, Glover D V. Whole grain anylose analysis in maize using near-infrared transmittance spectroscopy[J]. Cereal Chemistry,1997,74:300-303
96. Cen H Y, He Y. Theory and application of near infrared reflectance spectroscopy in detemination of food quality[J]. Trends in Food Science & Technology,2007,18(2):72-83
97. Chauchard F, Cogdill R, Roussel S, Roger J M, Maurel-Bellon V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy development of a robust and portable sensor for acidity prediction in grapes[J]. Chemometrics and Intelligent Laboratory Systems,2004,71(2): 141-150
98. Chen G L, Zhang B, Wu J G, et al. Nondestructive assessment of amino acid composition in rapeseed meal based on intact seeds by near-infrared reflectance spectroscopy[J]. Animal Feed Science and Technology,2011,165:111-119
99. Cozzolino D, Faasio A, Fernandez E. Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy[J]. Animal Feed Science and Technology,2006,129:329-336
100. Delwiche S R, Yang L Ch, Graybosch R A. Multipe view image analysis of freefalling U.S. wheat grains for damage assessment[J]. Computer and Electronics in Agriculture,2013,98(10):62-73
101. Dowlati M, Mohtasebi S S, Omid M, et al. Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes [J]. Journal of Food Engineering, 2013,119(2):277-287
102. Dubey B P, Bhagwat S G, Shouche S P, et al. Potential of artificial neural networks in varietal identification using morphometry of wheat grains[J]. Bios Engin,2006,95(l):61-67
103. EIMasry G, Cubero S, Molto E, et al. In-line sorting of irregular potatoes by using automated computer-based machine vision systemfJ]. Journal of Food Engineering,2012,112(1-2):60-68
104. Fan L Zh, Liu Y. Automate fry counting using computer vision and multi-class least squares support vector machine[J]. Aquaculture,2013,380-383(3):91-98
105. Fassio A, Fernandez E Q Restaino E A, Manna A La, et al. Predicting the nutritive value of high moisture grain corn by near infrared reflectance spectroscopy[J]. Computers and Electronics in Agriculture,2009,67:59-63
106. Fernandez-lbanez V, Soldado A, Martinez-Fernandez A, et al. Application of near infrared spectroscopy for rapid detection of aflatoxin B1 in maize and barley as analytical quality assessment[J]. Food Chemistry,2009, (113):629-634
107. Fernwandez Pierna J A, Lecler B, Conzen J P, Niemoeller A, Baeten V, Dardenne P. Comparison of various chemometric approaches for large near infrared spectroscopic data of feed and feed products[J]. Analytica Chimica Acta,2011,705(1-2):30-34
108. Ferreira D S, Galao O F, Pallone J A L, Poppi R J. Comparison and application of near-infrared (NIR) and mid-infrared(MIR) spectroscopy for determination of quality parameters in soybean samples[J]. Food Control,2014,35(1):227-232
109. Fertig C C, Podczeck F, Jee R D, Smith M R. Feasibility study for the rapid determination of the amylase content in starch by near-infrared spectroscopy[J]. European Journal of Pharmaceutical Sciences,2004,21(2-3):155-159
110. Giacomo D R, Stefania D Z. A multivariate regression model for detection of fumonisins content in maize from near infrared spectra[J]. Food Chemistry,2013, (141):4289-4294
111. Igathinathane C, Pordesimo L O, Batchelor W D. Major orthogonal dimensions measurement of food grains by machine vision using ImageJ[J]. Food Research International,2009,42(1):76-84
112. Jaillais B, Pinto R, Barros A S, et al. Outer-product analysis (OPA) using PCA to study the influence of temperature on NIR spectra of water[J]. Vibrational Spectroscopy,2005, (39):50-58
113. Khazaei N B, Tavakoli T, Ghassemian H, et al. Applied machine vision and artificial neural network for modeling and controlling of the grape drying process[J]. Computers and Electronics in Agriculture,2013,98(10):205-213
114. Liang X Y, Li X Y, Lei T W, Wang W, Gao Y. Study of sample temperature compensation in the measurement of soil moisture content[J]. Measurement,2011,44(10):2200-2204
115. Liao K, Paulsen M R, Reid J F. Real-Time Detection of Colour and Surface Defects of Maize Kernels Using Machine Vision[J]. Journal of Agricultural Engineering Research,1994,59(4): 263-271
116. Liu Y D, Ying Y B, Fu X P, Lu H SH. Experiments on predicting sugar content in apples by FT-NIR Technique[J]. Journal of Food Engineering,2007,80(3):986-989
117. Luo X, Jayas D S, Symons S J. Identification of Damaged Kernels in Wheat using a Colour Machine Vision System[J]. Journal of Cereal Science,1999,30(1):49-59
118. Ma Q, Jiang J T, Zhu D H, et al. Rapid measurement for 3D geometric features of maize ear based on image processing[J]. Transactions of the Chinese Society of Agricultural Engineering,2012, 28(supp.2):208-212
119. Makky M, Soni P. Development of an automatic grading machine for oil palm fresh fruits bunches (FFBs) based on machine vision[J]. Computers and Electronics in Agriculture,2013,93(4): 129-139
120. Mebatsion H K, Paliwal J. A Fourier analysis based algorithm to separate touching kernels in digital images[J]. Biosystems Engineering,2011, (108):66-74
121. Mebatsion H K, Paliwal J, Jayas D S. Automatic classification of non-touching cereal grains in digital images using limited morphological and color features[J]. Computers and Electronics in Agriculture,2013,90(1):99-105
122. Mladenov M, Draganova T, Tsenkova R, Mustafa M. Quality assessment of grain samples using spectra analysis[J]. Biosystems Engineering,2012,111(3):251-260
123. Nashat S, Abdullah A, Abdullah M Z. Machine vision for crack inspection of biscuits featuring pyramid detection scheme[J]. Journal of Food Engineering,2014,120(1):233-247
124. Ni B, Paulsen M R, Reid J F. Corn kernel crown shape identification using image processing[J]. Trans of the AS AE,1997,40 (3):833-838
125. Orman B A, Schumann Jr R A. Comparison of near-infrared spectroscopy calibration methods for the prediction of protein, oil and starch in maize grain[J]. Journal of Agricultural Food Chemistry, 1991,39:883-888
126. Paulsen M R, Wigger W D, Litchfield J B, et al. Computer image analyses for detection of maize and soybean kernel quality factors[J]. Journal of Agricultural Engineering Research,1989, 43(5-8):93-101
127. Paliwal J, Visen N S, Jayas D S, et al. Cereal Grain and Dockage Identification using Machine Vision[J]. Biosystems Engineering,2003,85(1):51-57
128. Panigrahi S, Misra M K, Bern C, et al. Back-ground segmentation and dimensional measurement of corn germplasm[J]. Trans of the ASAE,1995,38(1):291-297
129. Panigrahi S, Misra M K, Willson S. Evaluations of fractal geometry and invariant moments for shape classification of corn germplasm[J]. Computers and Electronics in Agriculture,1998,20(1):1-20
130. Pasti L, Walczak B, Massart D L, et al. Optimization of signal denoising in discrete wavelet transform[J]. Chemometrics and Intelligent Laboratory Systems,1999, (48):21-34
131. Plans M, Simo J, Casanas F, et al. Characterization of common beans (Phaseolus vulgaris L.) by infrared spectroscopy:Comparison of MIR, FT-NIR and dispersive NIR using portable and benchtop instruments [J].2013,54(2):1643-1651
132. Pojic M P, Mastilovvic J, Palic D, et al. The development of near-infrared spectroscopy(NIRS) calibration for prediction of ash content in legumes on the basis of two different reference methods[J]. Food Chemistry,2010,123:800-805
133. Reid J F, et al. Computer Vision Sensing of Stress Cracks in Corn Kernels[J]. Trans of the ASAE, 1991,34(5):2226-2244
134. Roberto Kawakami Harrop Galvao, Mario Cesar Ugulino Araujo, Gledson Emidio Jose,et al. A method for calibration and validation subset partitioning [J]. Talanta.2005,67:736-740
135. Sabzi S, Javadikia P, Rabani H, et al. Mass modeling of Bam orange with ANFIS and SPSS methods for using in machine vision[J]. Measurement,2013,46(9):3333-3341
136. Salgo A, Gergely S. Analysis of wheat grain development using NIR spectroscopy[J]. Journal of Cereal Science,2012,56(1):31-38
137. Santos R N F D, Galvao R K H, Araujo M C U, et al. Improvement of prediction ability of PLS models employing the wavelet packet transform:A case study concerning FT-IR determination of gasoline parameters [J]. Talanta,2007, (71):1136-1143
138. Shao Y N, Cen Y L, He Y, Liu F. Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice[J]. Food Chemistry,2011,126(4):1856-1861
139. Shiroma C, Rodriguez-Saona L. Application of NIR and MIR spectroscopy in quality control of potato chips[J]. Journal of Food Composition and Analysis,2009,22(6):596-605
140. Sinelli N, Pagani M A, Lucisano M, et al. Prediction of semolina technological quality by FT-NIR spectroscopy[J]. Journal of Cereal Science,2011,54(2):218-223
141. Szczypinski P M, Zapotoczny P. Computer vision algorithm for barley kernel identification, orientation estimation and surface structure assessment J]. Computers and Electronics in Agriculture,2012,87(9):32-38
142. Tallada J G, Palacios-Rojas N, Armstrong P R. Prediction of maize seed attributes using a rapid single kernel near infrared instrument[J]. Journal of Cereal Science,2009,50:381-387
143. Trygg T, Wold S. PLS regression on wavelet compressed NIR spectra[J]. Chemometrics and Intelligent Laboratory Systems,1998, (42):209-220
144. UPOV. General introduction to the examination of distinctness, uniformity and stability and the development of harmonized descriptions of new varieties of plants (TG/1/3). Geneva (Switzerland):The International Union for the Protection of New Varieties of Plants,2002, ppll-11
145. Utku H, Koksel H. Use of statistical filters in the classification of wheat by image analysis[J]. J Food Engin,1998,36(4):385-394
146. Wu D, He Y, Feng Sh J, Sun D W. Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM[J]. Journal of Food Engineering,2008, 84(1):124-131
147. Wu J G, Shi C H. Prediction of grain weight, brown rice weight and amylase content in single rice grains using near-infrared reflectance spectroscopy[J]. Field Crops Research,2004,83(1):13-21
148. Xie L H, Tang S Q, Chen N, Luo J, et al. Optimisation of near-infrared reflectance model in measuring protein and amylase content of rice flour[J]. Food Chemistry,2014,142 (1):92-100
149. Yang W, Winter P, Sokhansanj S, et al. Discrimination of Hard-to-pop Popcorn Kernels by Machine Vision and Neural Networks[J]. Biosystems Engineering,2005,91(1):1-8
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