基于改进离散粒子群算法的青贮玉米原料含水率高光谱检测
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摘要
快速、无损和准确检测青贮玉米原料含水率,对确保青贮玉米发酵品质、推动青贮产业健康快速发展有重要现实意义。为探究高光谱技术在青贮玉米原料含水率检测方面的可行性,研究通过高光谱成像系统获取青贮玉米原料高光谱图像并利用烘箱加热法测定实际含水率。在粒子更新方式和惯性权重2个方面对传统离散粒子群算法(discretebinary particle swarm optimization,DBPSO)进行优化,提出基于改进型离散粒子群算法(modified discrete binary particle swarm optimization,MDBPSO)的特征波段优选方法,并利用相关系数分析法(correlation coefficient,CC)、DBPSO和MDBPSO法提取原料含水率高光谱特征变量,基于全波段反射光谱(total spectral reflectance,TSR)和特征波段反射光谱建立青贮玉米原料含水率预测模型。结果表明,MDBPSO优选特征波段适应度函数的收敛精度和收敛效率较DBPSO法均有明显改善,最优适应度值由0.761 6提高至0.812 3,函数收敛迭代次数由280次降低至79次。MDBPSO-PLSR预测模型的建模精度和预测精度均高于CC-PLSR、DBPSO-PLSR和TSR-PLSR预测模型,其校正集决定系数Rc2和均方根误差RMSEC(root mean square error of calibration)分别为0.81和0.032,预测集决定系数Rp2和均方根误差RMSEP(root mean square error of prediction)分别为0.80和0.045。该研究表明,利用高光谱图像技术检测青贮玉米原料含水率具有较高的精度,研究可为后续开发青贮玉米原料水分快速检测仪器提供借鉴方法。
Moisture content of silage maize raw material affects juice discharge, compaction degree and microbial activity during the whole silage process, and it has further influence on silage fermentation quality. Rapid, non-destructive and accurate detection of moisture content in silage maize raw material is significant for ensuring the silage maize quality and promoting the silage industry healthy and rapidly. Hyperspectral images of silage maize raw material in the visible and near infrared(383-1 004 nm) regions were acquired by the hyperspectral imaging system, and then corresponding moisture content in silage maize raw material were obtained by oven heating method successfully. The hyperspectral information was extracted from the images by selecting the region of interest(ROI) using the ENVI software. The standard normalized variate(SNV) was applied for eliminating or weakening the effect of particle scattering on original hyperspectral data. The hyperspectral imaging provides much more information including spectral and image information for all the samples of silage maize raw material, however, hyperspectral imagery contains more noise and redundancy. These disturbances made it difficult to meet the needs of fast and effective detection of certain objects. Therefore, it was difficult to apply online industrial applications in daily life directly, and the feature band effective selection for hyperspectral images was very critical. In view of the disadvantages as poor efficiency and easy premature, the traditional discrete particle swarm optimization(DBPSO) was optimized in terms of particle updating method and inertia weight. A modified discrete particle swarm optimization(MDBPSO) was proposed to extract the hyperspectral feature bands effectively. The hyperspectral characteristic variables of raw material moisture content were extracted using the correlation coefficient(CC), DBPSO and MDBPSO method. Partial least squares regression(PLSR) prediction model for silage maize moisture content was established by using full band and characteristic band. The results indicated that the convergence accuracy and efficiency of MDBPSO had a significantly improvement compared with the DBPSO method. When the population number was 40 and the program independent test ran 20 times, for DBPSO, the maximum value of optimal fitness(OFVmax), the minimum value of optimal fitness(OFVmin), and the mean value of optimal fitness(OFVave) were 0.761 6, 0.680 4 and 0.731 8 respectively, and the number of iterations corresponding to the OFVmax was 280 times. The OFVmax, OFVmin, and OFVave were 0.812 3, 0.711 2 and 0.752 2 for MDBPSO, respectively, and the number of iterations corresponding to the OFVmax was 79 times. After the improvement of DBPSO method, OFV of the fitness function was increased from 0.761 6 to 0.812 3, the number of iterations was reduced from 280 to 79, and the convergence efficiency was increased by 71.79%. 188 and 62 eigenvectors were extracted by DBPSO and MDBPSO respectively. The characteristic bands selected by the DBPSO method were mainly distributed in 421-520 nm, followed by 571-670 nm and 871-920 nm, and the number of bands was 51, 45 and 15 respectively. The characteristic bands selected by the MDBPSO method were also mainly distributed in the above band, and the number of the wave segments was 15, 11 and 12 respectively. It could be inferred that the sensitive bands of moisture content of silage maize in visible light region are 421-520, 571-670 nm and 871-920 nm in near infrared region. Comparing the performance of the 4 models, the fitting accuracies of TSR-PLSR and CC-PLSR were lower, and the verification set determination coefficients(R2c) were 0.69 and 0.70 respectively, and the prediction set determination coefficients(R2p) were 0.67 and 0.64, respectively. The DBPSO-PLSR model was improved significantly, and the R2 c and R2 p was 0.76 and 0.76 respectively. The DBPSO-PLSR model performed better than the other 3 model: TSR-PLSR, CC-PLSR and DBPSO-PLSR, achieving the highest accuracy with R2 c of 0.81, RMSEC of 0.032, R2 p of 0.80, RMSEP of 0.045. The study demonstrated that the application of hyperspectral image technology to the nondestructive testing of the moisture content of silage maize raw material content had high feasibility, and could provide efficient guidance for rapid detecting instrument development.
Moisture content of silage maize raw material affects juice discharge, compaction degree and microbial activity during the whole silage process, and it has further influence on silage fermentation quality. Rapid, non-destructive and accurate detection of moisture content in silage maize raw material is significant for ensuring the silage maize quality and promoting the silage industry healthy and rapidly. Hyperspectral images of silage maize raw material in the visible and near infrared(383-1 004 nm) regions were acquired by the hyperspectral imaging system, and then corresponding moisture content in silage maize raw material were obtained by oven heating method successfully. The hyperspectral information was extracted from the images by selecting the region of interest(ROI) using the ENVI software. The standard normalized variate(SNV) was applied for eliminating or weakening the effect of particle scattering on original hyperspectral data. The hyperspectral imaging provides much more information including spectral and image information for all the samples of silage maize raw material, however, hyperspectral imagery contains more noise and redundancy. These disturbances made it difficult to meet the needs of fast and effective detection of certain objects. Therefore, it was difficult to apply online industrial applications in daily life directly, and the feature band effective selection for hyperspectral images was very critical. In view of the disadvantages as poor efficiency and easy premature, the traditional discrete particle swarm optimization(DBPSO) was optimized in terms of particle updating method and inertia weight. A modified discrete particle swarm optimization(MDBPSO) was proposed to extract the hyperspectral feature bands effectively. The hyperspectral characteristic variables of raw material moisture content were extracted using the correlation coefficient(CC), DBPSO and MDBPSO method. Partial least squares regression(PLSR) prediction model for silage maize moisture content was established by using full band and characteristic band. The results indicated that the convergence accuracy and efficiency of MDBPSO had a significantly improvement compared with the DBPSO method. When the population number was 40 and the program independent test ran 20 times, for DBPSO, the maximum value of optimal fitness(OFVmax), the minimum value of optimal fitness(OFVmin), and the mean value of optimal fitness(OFVave) were 0.761 6, 0.680 4 and 0.731 8 respectively, and the number of iterations corresponding to the OFVmax was 280 times. The OFVmax, OFVmin, and OFVave were 0.812 3, 0.711 2 and 0.752 2 for MDBPSO, respectively, and the number of iterations corresponding to the OFVmax was 79 times. After the improvement of DBPSO method, OFV of the fitness function was increased from 0.761 6 to 0.812 3, the number of iterations was reduced from 280 to 79, and the convergence efficiency was increased by 71.79%. 188 and 62 eigenvectors were extracted by DBPSO and MDBPSO respectively. The characteristic bands selected by the DBPSO method were mainly distributed in 421-520 nm, followed by 571-670 nm and 871-920 nm, and the number of bands was 51, 45 and 15 respectively. The characteristic bands selected by the MDBPSO method were also mainly distributed in the above band, and the number of the wave segments was 15, 11 and 12 respectively. It could be inferred that the sensitive bands of moisture content of silage maize in visible light region are 421-520, 571-670 nm and 871-920 nm in near infrared region. Comparing the performance of the 4 models, the fitting accuracies of TSR-PLSR and CC-PLSR were lower, and the verification set determination coefficients(R2c) were 0.69 and 0.70 respectively, and the prediction set determination coefficients(R2p) were 0.67 and 0.64, respectively. The DBPSO-PLSR model was improved significantly, and the R2 c and R2 p was 0.76 and 0.76 respectively. The DBPSO-PLSR model performed better than the other 3 model: TSR-PLSR, CC-PLSR and DBPSO-PLSR, achieving the highest accuracy with R2 c of 0.81, RMSEC of 0.032, R2 p of 0.80, RMSEP of 0.045. The study demonstrated that the application of hyperspectral image technology to the nondestructive testing of the moisture content of silage maize raw material content had high feasibility, and could provide efficient guidance for rapid detecting instrument development.
引文
[1]Ferraretto L F,Shaver R D,Luck B D,et al.Silage review:Recent advances and future technologies for whole-plant and fractionated corn silage harvesting[J].Journal of Dairy Science,2018,101(5):3937-3951.
[2]Bruning D,Gerlach K,Wei?K,et al.Effect of compaction,delayed sealing and aerobic exposure on maize silage quality and on formation of volatile organic compounds[J].Grass Forage Sci,2018,73:53-66.
[3]Pu Yuanyuan,Sun Dawen.Vis-NIR hyperspectral imaging in visualizing moisture distribution of mango slices during microwave-vacuum drying[J].Food Chemistry,2015,188:271-278.
[4]刘志刚,徐勤超.基于高光谱技术的基质含水率快速测定方法[J].灌溉排水学报,2017,36(10):82-86.Liu Zhigang,Xu Qinchao.Rapid determination of matrix moisture content based on hyperspectral technology[J].Journal of Irrigation and Drainage,2017,36(10):82-86.(in Chinese with English abstract)
[5]Zhu Yaodi,Zou Xiaobo,Shen Tingting,et al.Determination of total acid content and moisture content during solid-state fermentation processes using hyperspectral imaging[J].Journal of Food Engineering,2016,174:75-84.
[6]Deng Shuiguang,Xu Yifei,Li Xiaoli,et al.Moisture content prediction in teal leaf with near infrared hyperspectral imaging[J].Computers and Electronics in Agriculture,2015,118:38-46.
[7]Kobori H,Gorretta N,Rabatel G,et al.Applicability of VisNIR hyperspectral imaging for monitoring wood moisture content(MC)[J].Holzforschung,2013,67(3):307-314.
[8]Caporaso N,Whitworth M B,Grebby S,et al.Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging[J].Journal of Food Engineering,2018,227:18-29.
[9]吴见,谭靖,邓凯,等.基于优化指数的玉米冠层含水量遥感估测[J].湖南农业大学学报:自然科学版,2015,41(6):685-690.Wu Jian,Tan Jing,Deng Kai,et al.Remote sensing monitoring of the corn canopy water content based on the optimized index[J].Journal of Hunan Agricultural University:Natural Sciences,2015,41(6):685-690.(in Chinese with English abstract)
[10]孙红,陈香,孙梓淳,等.基于透射光谱的玉米叶片含水率快速检测仪研究[J].农业机械学报,2018,49(3):173-178.Sun Hong,Chen Xiang,Sun Zichun,et al.Rapid detection of moisture content in maize leaves based on transmission spectrum[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(3):173-178.(in Chinese with English abstract)
[11]Yang Chen,Tan Yulei,Bruzzone L,et al.Discriminative feature metric learning in the affinity propagation model for band selection in hyperspectral images[J].Remote Sensing,2017,9(8):782-798.
[12]孙俊,丛孙丽,毛罕平,等.基于高光谱的油麦菜叶片水分CARS-ABC-SVR预测模型[J].农业工程学报,2017,33(5):178-184.Sun Jun,Cong Sunli,Mao Hanping,et al.CARS-ABC-SVRmodel for predicting leaf moisture of leaf-used lettuce based on hyperspectral[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(5):178-184.(in Chinese with English abstract)
[13]Digman M F,Shinners K J.Real-time moisture measurement on a forage harvester using near-infrared reflectance spectroscopy[J].Transactions of the ASABE,2008,51(5):1801-1810.
[14]Cozzolino D,Fassio A,FernáNdez E,et al.Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy[J].Animal Feed Science&Technology,2006,129(3):329-336.
[15]Zhou Xin,Sun Jun,Mao Hanping,et al.Visualization research of moisture content in leaf lettuce leaves based on WT-PLSR and hyperspectral imaging technology[J].Journal of Food Process Engineering,2017,41(2):1-7.
[16]Waseem Amjad,Crichton S O J,Munir A,et al.Hyperspectral imaging for the determination of potato slice moisture content and chromaticity during the convective hot air drying process[J].Biosystems Engineering,2018,166:180-183.
[17]中华人民共和国国家质量监督检验检疫总局.饲料中水分的测定:GB/T 6435-2014[S].北京:标准出版社,2014:7.
[18]Brook A,Polinova M,Bendor E.Fine tuning of the SVCmethod for airborne hyperspectral sensors:The BRDFcorrection of the calibration nets targets[J].Remote Sensing of Environment,2018,204:861-871.
[19]赵茂程,杨君荣,陆丹丹,等.基于高光谱成像的青梅酸度检测方法[J].农业机械学报,2017,48(9):318-323.Zhao Maocheng,Yang Junrong,Lu Dandan,et al.Detection methods of greengage acidity based on hyperspectral imaging[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(9):318-323.(in Chinese with English abstract)
[20]孙红,郑涛,刘宁,等.高光谱图像检测马铃薯植株叶绿素含量垂直分布[J].农业工程学报,2018,34(1):149-156.Sun Hong,Zheng Tao,Liu Ning,et al.Vertical distribution of chlorophyll in potato plants based on hyperspectral imaging[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(1):149-156.(in Chinese with English abstract)
[21]Sun Jun,Lu Xinzi,Mao Hanping,et al.Quantitative determination of rice moisture based on hyperspectral imaging technology and BCC-LS-SVR algorithm[J].Journal of Food Process Engineering,2017,40(3):1-8.
[22]Kennedy J,Eberhart R.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,Perth,Australia,Proceedings,IEEE,1995:1942-1948.
[23]Kennedy J,Eberhart R.A discrete binary version of the particle swarm algorithm[C]//IEEE International Conference on Systems,Man,and Cybernetics,Computational Cybernetics and Simulation,IEEE,1997,5:4104-4108.
[24]Yang Jun,Zhang Hesheng,Ling Yun,et al.Task allocation for wireless sensor network using modified binary particle swarm optimization[J].IEEE Sensor Journal,2014,14(3):882-891.
[25]孟常亮,李卫忠,廖勇,等.基于改进离散二进制粒子群的SVM选择集成算法[J].计算机工程与应用,2011,47(28):166-169.Meng Changliang,Li Weizhong,Liao Yong,et al.SVMselection ensemble algorithm based on improved binary particle swarm optimization[J].Computer Engineering and Applications,2011,47(28):166-169.(in Chinese with English abstract)
[26]胡清,张强.基于改进二进制粒子群算法的配电网故障定位[J].南京工程学院学报:自然科学版,2016,14(3):77-81.Hu Qing,Zhang Qiang.Fault location of distribution networks based on improved binary particle swarm optimization algorithm[J].Journal of Nanjing Institute of Technology:Natural Science Edition,2016,14(3):77-81.(in Chinese with English abstract)
[27]刘朔,周康,张杰,等.基于二进制粒子群的图像分割算法[J].武汉工业学院学报,2011,30(4):42-44.Liu Suo,Zhou Kang,Zhang Jie,et al.Image segmentation algorithm based on binary PSO[J].Journal of Wuhan Polytechnic University,2011,30(4):42-44.(in Chinese with English abstract)
[28]曹引,冶运涛,赵红莉,等.基于离散粒子群和偏最小二乘的水源地浊度高光谱反演[J].农业机械学报,2018,49(1):173-182.Cao Yin,Ye Yuntao,Zhao Hongli,et al.Satellite hyperspectral retrieval of turbidity for water source based on discrete particle swarm and partial least squares[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):173-182.(in Chinese with English abstract)
[29]郭勇庆,曹志军,李胜利,等.全株玉米青贮生产与品质评定关键技术[J].中国畜牧杂志,2012,48(18):39-43.Guo Yongqing,Cao Zhijun,Li Shengli,et al.Key technologies for silage production and quality evaluation of whole plant corn[J].Journal of Animal Science Chinese,2012,48(18):39-43.(in Chinese with English abstract)
[30]Huart F,Malumba P,Odjo S,et al.In vitro and in vivo assessment of the effect of initial moisture content and drying temperature on the feeding value of maize grain[J/OL].British Poultry Science,2018,1-11.http://www.tandfonline.com/loi/cbps20.
[2]Bruning D,Gerlach K,Wei?K,et al.Effect of compaction,delayed sealing and aerobic exposure on maize silage quality and on formation of volatile organic compounds[J].Grass Forage Sci,2018,73:53-66.
[3]Pu Yuanyuan,Sun Dawen.Vis-NIR hyperspectral imaging in visualizing moisture distribution of mango slices during microwave-vacuum drying[J].Food Chemistry,2015,188:271-278.
[4]刘志刚,徐勤超.基于高光谱技术的基质含水率快速测定方法[J].灌溉排水学报,2017,36(10):82-86.Liu Zhigang,Xu Qinchao.Rapid determination of matrix moisture content based on hyperspectral technology[J].Journal of Irrigation and Drainage,2017,36(10):82-86.(in Chinese with English abstract)
[5]Zhu Yaodi,Zou Xiaobo,Shen Tingting,et al.Determination of total acid content and moisture content during solid-state fermentation processes using hyperspectral imaging[J].Journal of Food Engineering,2016,174:75-84.
[6]Deng Shuiguang,Xu Yifei,Li Xiaoli,et al.Moisture content prediction in teal leaf with near infrared hyperspectral imaging[J].Computers and Electronics in Agriculture,2015,118:38-46.
[7]Kobori H,Gorretta N,Rabatel G,et al.Applicability of VisNIR hyperspectral imaging for monitoring wood moisture content(MC)[J].Holzforschung,2013,67(3):307-314.
[8]Caporaso N,Whitworth M B,Grebby S,et al.Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging[J].Journal of Food Engineering,2018,227:18-29.
[9]吴见,谭靖,邓凯,等.基于优化指数的玉米冠层含水量遥感估测[J].湖南农业大学学报:自然科学版,2015,41(6):685-690.Wu Jian,Tan Jing,Deng Kai,et al.Remote sensing monitoring of the corn canopy water content based on the optimized index[J].Journal of Hunan Agricultural University:Natural Sciences,2015,41(6):685-690.(in Chinese with English abstract)
[10]孙红,陈香,孙梓淳,等.基于透射光谱的玉米叶片含水率快速检测仪研究[J].农业机械学报,2018,49(3):173-178.Sun Hong,Chen Xiang,Sun Zichun,et al.Rapid detection of moisture content in maize leaves based on transmission spectrum[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(3):173-178.(in Chinese with English abstract)
[11]Yang Chen,Tan Yulei,Bruzzone L,et al.Discriminative feature metric learning in the affinity propagation model for band selection in hyperspectral images[J].Remote Sensing,2017,9(8):782-798.
[12]孙俊,丛孙丽,毛罕平,等.基于高光谱的油麦菜叶片水分CARS-ABC-SVR预测模型[J].农业工程学报,2017,33(5):178-184.Sun Jun,Cong Sunli,Mao Hanping,et al.CARS-ABC-SVRmodel for predicting leaf moisture of leaf-used lettuce based on hyperspectral[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(5):178-184.(in Chinese with English abstract)
[13]Digman M F,Shinners K J.Real-time moisture measurement on a forage harvester using near-infrared reflectance spectroscopy[J].Transactions of the ASABE,2008,51(5):1801-1810.
[14]Cozzolino D,Fassio A,FernáNdez E,et al.Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy[J].Animal Feed Science&Technology,2006,129(3):329-336.
[15]Zhou Xin,Sun Jun,Mao Hanping,et al.Visualization research of moisture content in leaf lettuce leaves based on WT-PLSR and hyperspectral imaging technology[J].Journal of Food Process Engineering,2017,41(2):1-7.
[16]Waseem Amjad,Crichton S O J,Munir A,et al.Hyperspectral imaging for the determination of potato slice moisture content and chromaticity during the convective hot air drying process[J].Biosystems Engineering,2018,166:180-183.
[17]中华人民共和国国家质量监督检验检疫总局.饲料中水分的测定:GB/T 6435-2014[S].北京:标准出版社,2014:7.
[18]Brook A,Polinova M,Bendor E.Fine tuning of the SVCmethod for airborne hyperspectral sensors:The BRDFcorrection of the calibration nets targets[J].Remote Sensing of Environment,2018,204:861-871.
[19]赵茂程,杨君荣,陆丹丹,等.基于高光谱成像的青梅酸度检测方法[J].农业机械学报,2017,48(9):318-323.Zhao Maocheng,Yang Junrong,Lu Dandan,et al.Detection methods of greengage acidity based on hyperspectral imaging[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(9):318-323.(in Chinese with English abstract)
[20]孙红,郑涛,刘宁,等.高光谱图像检测马铃薯植株叶绿素含量垂直分布[J].农业工程学报,2018,34(1):149-156.Sun Hong,Zheng Tao,Liu Ning,et al.Vertical distribution of chlorophyll in potato plants based on hyperspectral imaging[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(1):149-156.(in Chinese with English abstract)
[21]Sun Jun,Lu Xinzi,Mao Hanping,et al.Quantitative determination of rice moisture based on hyperspectral imaging technology and BCC-LS-SVR algorithm[J].Journal of Food Process Engineering,2017,40(3):1-8.
[22]Kennedy J,Eberhart R.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,Perth,Australia,Proceedings,IEEE,1995:1942-1948.
[23]Kennedy J,Eberhart R.A discrete binary version of the particle swarm algorithm[C]//IEEE International Conference on Systems,Man,and Cybernetics,Computational Cybernetics and Simulation,IEEE,1997,5:4104-4108.
[24]Yang Jun,Zhang Hesheng,Ling Yun,et al.Task allocation for wireless sensor network using modified binary particle swarm optimization[J].IEEE Sensor Journal,2014,14(3):882-891.
[25]孟常亮,李卫忠,廖勇,等.基于改进离散二进制粒子群的SVM选择集成算法[J].计算机工程与应用,2011,47(28):166-169.Meng Changliang,Li Weizhong,Liao Yong,et al.SVMselection ensemble algorithm based on improved binary particle swarm optimization[J].Computer Engineering and Applications,2011,47(28):166-169.(in Chinese with English abstract)
[26]胡清,张强.基于改进二进制粒子群算法的配电网故障定位[J].南京工程学院学报:自然科学版,2016,14(3):77-81.Hu Qing,Zhang Qiang.Fault location of distribution networks based on improved binary particle swarm optimization algorithm[J].Journal of Nanjing Institute of Technology:Natural Science Edition,2016,14(3):77-81.(in Chinese with English abstract)
[27]刘朔,周康,张杰,等.基于二进制粒子群的图像分割算法[J].武汉工业学院学报,2011,30(4):42-44.Liu Suo,Zhou Kang,Zhang Jie,et al.Image segmentation algorithm based on binary PSO[J].Journal of Wuhan Polytechnic University,2011,30(4):42-44.(in Chinese with English abstract)
[28]曹引,冶运涛,赵红莉,等.基于离散粒子群和偏最小二乘的水源地浊度高光谱反演[J].农业机械学报,2018,49(1):173-182.Cao Yin,Ye Yuntao,Zhao Hongli,et al.Satellite hyperspectral retrieval of turbidity for water source based on discrete particle swarm and partial least squares[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):173-182.(in Chinese with English abstract)
[29]郭勇庆,曹志军,李胜利,等.全株玉米青贮生产与品质评定关键技术[J].中国畜牧杂志,2012,48(18):39-43.Guo Yongqing,Cao Zhijun,Li Shengli,et al.Key technologies for silage production and quality evaluation of whole plant corn[J].Journal of Animal Science Chinese,2012,48(18):39-43.(in Chinese with English abstract)
[30]Huart F,Malumba P,Odjo S,et al.In vitro and in vivo assessment of the effect of initial moisture content and drying temperature on the feeding value of maize grain[J/OL].British Poultry Science,2018,1-11.http://www.tandfonline.com/loi/cbps20.
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