应用Sentinel-2A NDVI时间序列和面向对象决策树方法的农作物分类
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摘要
农作物种植结构是农业生产活动对土地利用的表现形式。及时精确地获取农作物的空间分布信息对指导农业生产、合理分配资源以及解决粮食安全问题等具有重要意义。目前农作物信息提取研究大多局限于中低分辨率遥感影像的NDVI时间序列,影响了作物空间分布信息提取的准确性。随着Sentinel-2A卫星成功发射,为高分辨率NDVI时间序列的构建提供了可能。本文以黑龙江省北安市为研究区,基于覆盖完整生育期的Sentinel-2A多光谱数据,构建10 m分辨率的NDVI时间序列数据集,利用Savitzky Golay (S-G)滤波器对Sentinel-2A NDVI时间序列数据进行平滑。基于典型时相的多光谱数据和NDVI时间序列构建面向对象决策树分类模型进行作物类型遥感识别。通过对样本的NDVI时间序列曲线分析,可以得出NDVI时间序列能够清晰地区分作物物候差异。此外,本文还利用面向对象分类和支持向量机(Support Vector Machine, SVM)分类两种方法,对典型时相的多光谱数据进行了作物分类对比实验,并对结果进行了对比分析。研究结果表明:①典型时相多光谱数据引入平滑重构后的NDVI时间序列能够更好地描述作物的物候特性,能够准确刻画研究区作物发育情况,有效区分各类作物;②通过对比分类实验发现,典型时相多光谱数据引入NDVI时间序列特征,增强了不同作物之间的光谱差异,提高了作物分类精度,总体精度和kappa系数较典型时相多光谱数据进行分类的结果分别提高了7.7%和0.055;③基于面向对象的决策树分类模型在作物分类的结果中精度最高,总体精度为96.2%,kappa系数为0.892。本研究的方法为其他大区域农作物的分类提供了重要参考和借鉴价值。
Cropping pattern is a fundamental aspect of land use. Obtaining accurate and timely crop spatial distribution information is very important to guide agricultural production, rational allocation of resources, and help solve the problem of food security. NDVI(Normalized Difference Vegetation Index) time series have been widely used in collecting vegetation information. Identification and information extraction of different crops can be effectively achieved by analyzing the growth period of crops and the time-series spectrum characteristics of NDVI. Most existing studies on NDVI time series are limited to moderate or low resolution remote sensing imagery, which affects the accuracy of extracting crop spatial distribution information. With the successful launch of the satellite Sentinel-2A, more opportunities have emerged for the construction of NDVI time series with high temporal and spatial resolution. In this paper, by use of typical phase Sentinel-2A imagery for Beian city, a NDVI dataset with a spatial resolution of 10 m covering the whole growth period of April-October was generated based on the Savitaky-Golay filtering smoothing method, and crop identification was implemented based on decision tree model and object-oriented classification. By analyzing NDVI time series curves of crop samples, we concluded that NDVI time series was able to clearly distinguish crop phenological differences and capture crop specific features in the study area. Furthermore, we also discussed the classification accuracy based on the typical phase data by the methods of object-oriented classification and support vector machine. Taking the field sample survey datum as true value, we analyzed the results of the two classification methods. The results show that the processed NDVI time series with high resolution over the entire crop growth cycle represent different crop phenological characteristics appropriately. It is able to reflect the crops growth condition accurately and distinguish different crops effectively. The decision tree model integrated with the object-oriented classification method had the highest accuracy in crop classification as compared to other classification methods,with its overall accuracy and kappa coefficient being 96.2% and 0.892, respectively. This research show that Sentinel-2 A NDVI with high resolution can be used for crop mapping, and can be applied to crop classification over large areas, thanks to Sentinel-2 A imagery's wide coverage. Furthermore, the Savitzky Golay(S-G) method was used for NDVI time series smoothing, and the results indicate that the processed NDVI time series can better represent crop phenological characteristics. Then the decision tree model integrated with the objectoriented classification method was used for crop classification based on typical phase multi-spectral imagery and the smoothed NDVI time series, which improved the overall accuracy by 7.7% and the kappa coefficient by0.055. The approach proposed in this paper provides important reference for crop mapping in other agricultural regions.
Cropping pattern is a fundamental aspect of land use. Obtaining accurate and timely crop spatial distribution information is very important to guide agricultural production, rational allocation of resources, and help solve the problem of food security. NDVI(Normalized Difference Vegetation Index) time series have been widely used in collecting vegetation information. Identification and information extraction of different crops can be effectively achieved by analyzing the growth period of crops and the time-series spectrum characteristics of NDVI. Most existing studies on NDVI time series are limited to moderate or low resolution remote sensing imagery, which affects the accuracy of extracting crop spatial distribution information. With the successful launch of the satellite Sentinel-2A, more opportunities have emerged for the construction of NDVI time series with high temporal and spatial resolution. In this paper, by use of typical phase Sentinel-2A imagery for Beian city, a NDVI dataset with a spatial resolution of 10 m covering the whole growth period of April-October was generated based on the Savitaky-Golay filtering smoothing method, and crop identification was implemented based on decision tree model and object-oriented classification. By analyzing NDVI time series curves of crop samples, we concluded that NDVI time series was able to clearly distinguish crop phenological differences and capture crop specific features in the study area. Furthermore, we also discussed the classification accuracy based on the typical phase data by the methods of object-oriented classification and support vector machine. Taking the field sample survey datum as true value, we analyzed the results of the two classification methods. The results show that the processed NDVI time series with high resolution over the entire crop growth cycle represent different crop phenological characteristics appropriately. It is able to reflect the crops growth condition accurately and distinguish different crops effectively. The decision tree model integrated with the object-oriented classification method had the highest accuracy in crop classification as compared to other classification methods,with its overall accuracy and kappa coefficient being 96.2% and 0.892, respectively. This research show that Sentinel-2 A NDVI with high resolution can be used for crop mapping, and can be applied to crop classification over large areas, thanks to Sentinel-2 A imagery's wide coverage. Furthermore, the Savitzky Golay(S-G) method was used for NDVI time series smoothing, and the results indicate that the processed NDVI time series can better represent crop phenological characteristics. Then the decision tree model integrated with the objectoriented classification method was used for crop classification based on typical phase multi-spectral imagery and the smoothed NDVI time series, which improved the overall accuracy by 7.7% and the kappa coefficient by0.055. The approach proposed in this paper provides important reference for crop mapping in other agricultural regions.
引文
[1]胡琼,吴文斌,宋茜,等.农作物种植结构遥感提取研究进展[J].中国农业科学,2015,48(10):1900-1914.[Hu Q,Wu W B, Song Q, et al. Recent progresses in research of crop patterns mapping by using remote sensing[J]. Scientia Agricultura Sinica, 2015,48(10):1900-1914.]
[2]黄健熙,侯矞焯,苏伟,等.基于GF-1 WFV数据的玉米与大豆种植面积提取方法[J].农业工程学报,2017,33(7):164-170.[Huang J X, Hou Y Z, Su W, et al. Mapping corn and soybean cropped area with GF-1 WFV data[J].Transactions of the Chinese Society of Agricultural Engineering, 2017,33(7):164-170.]
[3]李冰,梁燕华,李丹丹,等.多时相GF-1卫星PMS影像提取农作物种植结构[J].中国农业资源与区划,2017,38(9):56-62.[Li B, Liang Y H, Li D D, et al. Crop planting structure extraction based on multi-temporal GF-1 PMS remote sensing images[J]. Chinese Journal Agricultural Resources and Regional Planning, 2017,38(9):56-62.]
[4]杨闫君,占玉林,田庆久,等.基于GF-1/WFV NDVI时间序列数据的作物分类[J].农业工程学报,2015,31(24):155-161.[Yang Y J, Zhan Y L, Tian Q J, et al. Crop classification based on GF-1/WFV NDVI time series[J].Transactions of the Chinese Society of Agricultural Engeering, 2015,31(24):155-161.]
[5] Wardlow B D, Egbert S L, Kastens J H. Analysis of timeseries modis 250 m vegetation index data for crop classification in the US. central great plains[J]. Remote Sensing of Environment, 2007,108(3):290-310.
[6] Atzberger C, Rembold F. Mapping the spatial distribution of winter crops at sub-pixel level using avhrr ndvi time series and neural nets[J]. Remote Sensing, 2013,5(3):1335-1354.
[7]许青云,杨贵军,龙慧灵,等.基于MODIS NDVI多年时序数据的农作物种植识别[J].农业工程学报,2014,30(11):134-144.[Xu Q Y, Yang G J, Long H L, et al. Crop information identification based on MODIS NDVI time-series data[J]. Transactions of the Chinese Society of Agricultural Engeering, 2014,30(11):134-144.]
[8]张荣群,王盛安,高万林,等.农作物种植格局对遥感分类精度的影响[J].农业机械学报,2016,47(10):318-324.[Zhang R Q, Wang S A, Gao W L, et al.Effects of crop planting structure on remote sensing classification accuracy[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016,47(10):318-324.]
[9]熊勤学,黄敬峰.利用NDVI指数时序特征监测秋收作物种植面积[J].农业工程学报,2009,25(1):144-148.[Xiong Q X, Huang J F. Estimation of autumn harvest crop planting area based on NDVI sequential characteristics[J].Transactions of the Chinese Society of Agricultural Engineering, 2009,25(1):144-148.]
[10]张焕雪,曹新,李强子,等.基于多时相环境星NDVI时间序列的农作物分类研究[J].遥感技术与应用,2015,30(2):304-311.[Zhang H X, Cao X, Li Q Z, et al. Research on crop identification using multi-temporal NDVI HJ images[J]. Remote Sensing Technology and Application, 2015,30(2):304-311.]
[11]刘吉凯,钟仕全,徐雅,等.基于多时相GF-1 WFV数据的南方丘陵地区甘蔗种植面积提取[J].广东农业科学2014,41(18):149-154.[Liu J K, Zhong S Q, Xu Y, et al.Sugarcane extraction in the southern hills using multitemporal GF-1 WFV data[J]. Guangdong Agricultural Sciences, 2014,41(18):149-154.]
[12]马丽,徐新刚,贾建华,等.利用多时相TM影像进行作物分类方法[J].农业工程学报,2008,24(2):191-195.[Ma L,Xu X G, Jia J H, et al. Crop classification method using multi-temporal TM images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2008,24(2):191-195.]
[13]岳桢干.欧洲Sentinel-2A卫星即将大显身手—“哥白尼”对地观测计划简介(上)[J].红外,2015,36(8):32-36.[Yue Z G. The Sentinel-2A satellite will display skills to the full-"Copernicus"on earth observation program[J]. Infrared, 2015,36(8):32-36.]
[14] Fernández M A, Fernández M O, Quintano C. Sentinel-2A red-edge spectral indices suitability for discriminating burn severity[J]. International Journal of Applied Earth Observation and Geoinformation, 2016,50:170-175.
[15] Katja D, Anna G, Peter G, et al. Water constituents and water depth retrieval from Sentinel-2A:A first evaluation in an oligotrophic lake[J]. Remote Sensing, 2016,8(11):941-965.
[16] Lefebvre A, Sannier C, Corpetti T. Monitoring urban areas with Sentinel-2A data:Application to the update of the copernicus high resolution layer imperviousness degree[J]. Remote Sensing, 2016,8(7):606-621.
[17] Korhonen L, Hadi, Packalen P, et al. Comparison of Sentinel-2 and landsat 8 in the estimation of boreal forest canopy cover and leaf area index[J]. Remote Sensing of Environment, 2017,195:259-274.
[18]郑阳,吴炳方,张淼. Sentinel-2数据的冬小麦地上干生物量估算及评价[J].遥感学报,2017,21(2):318-328.[Zheng Y, Wu B F, Zhang M. Estimating the aboveground biomass of winter wheat using the Sentinel-2 data[J]. Journal of Remote Sensing, 2017,21(2):318-328.]
[19]彭光雄,宫阿都,崔伟宏,等.多时相影像的典型区农作物识别分类方法对比研究[J].地球信息科学学报,2009,11(2):225-230.[Peng G X, Gong A D, Cui W H, et al.Study on methods comparison of typical remote sensing classification based on multi-temporal images[J]. Journal of geo-information science, 2009,11(2):225-230.]
[20]李晓东,姜琦刚.基于多时相遥感数据的农田分类提取[J].农业工程学报,2015,31(7):145-150.[Li X D, Jiang Q G. Extraction of farmland classification based on multitemporal remote sensing data[J]. Transactions of the Chinese Society of Agricultural Engine-ering, 2015,31(7):145-150.]
[21]贾明明,王宗明,张柏,等.综合环境卫星与MODIS数据的面向对象土地覆盖分类方法[J].武汉大学学报·信息科学版,2014,39(3):305-310.[Jia M M, Wang Z M, Zhang B, et al. Land cover classification of compositing HJ-1and MODIS data base on object-based method[J]. Geomatics and Information Science of Wuhan University,2014,39(3):305-310.]
[22]刘明月,王宗明,满卫东,等.基于MODIS时序数据的Landsat8影像选取及面向对象分类方法的农作物分类[J].土壤与作物,2017,6(2):104-112.[Liu Mingyue Y,Wang Zongming M, Man Weidong D, et al. Crop Classification classification Based based on Multimulti-temporal Landsat8 OLI Imagery imagery and MODIS NDVI Time time Series series Datadata[J]. Soils and Crops, 2017,6(2):104-112.]
[23]毕恺艺,牛铮,黄妮,等.基于Sentinel-2A时序数据和面向对象决策树方法的植被识别[J].地理与地理信息科学,2017,33(5):16-33.[Bi Kaiyi Y, Niu Zheng, Huang Ni, et al. Identifying vegetation with decision tree model based on Object-Oriented method using Multimulti-temporal Sentinel-2A images[J]. Geography and Geo-Information Science, 2017,33(5):16-33.]
[24]郭昱杉,刘庆生,刘高焕,等.基于MODIS时序NDVI主要农作物种植信息提取研究[J].自然资源学报,2017,32(10):1808-1818.[Guo Y S, Liu Q S, Liu G H, et al. Extraction of main crops in Yellow River Delta based on MODIS NDVI time series[J]. Journal of Natural Resources, 2017,32(10):1808-1818.]
[25]鹿琳琳,郭华东.基于SPOT/VEGETATION时间序列的冬小麦物候提取方法[J].农业工程学报,2009,25(6):174-179.[Lu L L, Guo H D. Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2009,25(6):174-179.]
[26]欧阳玲,毛德华,王宗明,等.基于GF-1与Landsat8 OLI影像的作物种植结构与产量分析[J].农业工程学报,2017,33(11):147-156.[Ou Y L, Mao D H, Wang Z M, et al.Analysis crops planting structure and yield based on GF-1 and Landsat8 OLI images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(11):147-156.]
[27]刘佳,王利民,杨福刚,等.基于HJ时间序列数据的农作物种植面积估算[J].农业工程学报,2015,31(3):99-206.[Liu J, Wang L M, Yang F G, et al. Remote sensing estimation of crop planting area based on HJ time-series images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(3):199-206.]
[28]黄青,唐华俊,周清波,等.东北地区主要作物种植结构遥感提取及长势监测[J].农业工程学报,2010,26(9):218-223.[Huang Q, Tang H J, Zhou Q B, et al. Remote-sensing based monitoring of planting structure and growth condition of major crops in Northeast China[J]. Transactions of the Chinese Society of Agricultural Engineering,2010,26(9):218-223.]
[2]黄健熙,侯矞焯,苏伟,等.基于GF-1 WFV数据的玉米与大豆种植面积提取方法[J].农业工程学报,2017,33(7):164-170.[Huang J X, Hou Y Z, Su W, et al. Mapping corn and soybean cropped area with GF-1 WFV data[J].Transactions of the Chinese Society of Agricultural Engineering, 2017,33(7):164-170.]
[3]李冰,梁燕华,李丹丹,等.多时相GF-1卫星PMS影像提取农作物种植结构[J].中国农业资源与区划,2017,38(9):56-62.[Li B, Liang Y H, Li D D, et al. Crop planting structure extraction based on multi-temporal GF-1 PMS remote sensing images[J]. Chinese Journal Agricultural Resources and Regional Planning, 2017,38(9):56-62.]
[4]杨闫君,占玉林,田庆久,等.基于GF-1/WFV NDVI时间序列数据的作物分类[J].农业工程学报,2015,31(24):155-161.[Yang Y J, Zhan Y L, Tian Q J, et al. Crop classification based on GF-1/WFV NDVI time series[J].Transactions of the Chinese Society of Agricultural Engeering, 2015,31(24):155-161.]
[5] Wardlow B D, Egbert S L, Kastens J H. Analysis of timeseries modis 250 m vegetation index data for crop classification in the US. central great plains[J]. Remote Sensing of Environment, 2007,108(3):290-310.
[6] Atzberger C, Rembold F. Mapping the spatial distribution of winter crops at sub-pixel level using avhrr ndvi time series and neural nets[J]. Remote Sensing, 2013,5(3):1335-1354.
[7]许青云,杨贵军,龙慧灵,等.基于MODIS NDVI多年时序数据的农作物种植识别[J].农业工程学报,2014,30(11):134-144.[Xu Q Y, Yang G J, Long H L, et al. Crop information identification based on MODIS NDVI time-series data[J]. Transactions of the Chinese Society of Agricultural Engeering, 2014,30(11):134-144.]
[8]张荣群,王盛安,高万林,等.农作物种植格局对遥感分类精度的影响[J].农业机械学报,2016,47(10):318-324.[Zhang R Q, Wang S A, Gao W L, et al.Effects of crop planting structure on remote sensing classification accuracy[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016,47(10):318-324.]
[9]熊勤学,黄敬峰.利用NDVI指数时序特征监测秋收作物种植面积[J].农业工程学报,2009,25(1):144-148.[Xiong Q X, Huang J F. Estimation of autumn harvest crop planting area based on NDVI sequential characteristics[J].Transactions of the Chinese Society of Agricultural Engineering, 2009,25(1):144-148.]
[10]张焕雪,曹新,李强子,等.基于多时相环境星NDVI时间序列的农作物分类研究[J].遥感技术与应用,2015,30(2):304-311.[Zhang H X, Cao X, Li Q Z, et al. Research on crop identification using multi-temporal NDVI HJ images[J]. Remote Sensing Technology and Application, 2015,30(2):304-311.]
[11]刘吉凯,钟仕全,徐雅,等.基于多时相GF-1 WFV数据的南方丘陵地区甘蔗种植面积提取[J].广东农业科学2014,41(18):149-154.[Liu J K, Zhong S Q, Xu Y, et al.Sugarcane extraction in the southern hills using multitemporal GF-1 WFV data[J]. Guangdong Agricultural Sciences, 2014,41(18):149-154.]
[12]马丽,徐新刚,贾建华,等.利用多时相TM影像进行作物分类方法[J].农业工程学报,2008,24(2):191-195.[Ma L,Xu X G, Jia J H, et al. Crop classification method using multi-temporal TM images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2008,24(2):191-195.]
[13]岳桢干.欧洲Sentinel-2A卫星即将大显身手—“哥白尼”对地观测计划简介(上)[J].红外,2015,36(8):32-36.[Yue Z G. The Sentinel-2A satellite will display skills to the full-"Copernicus"on earth observation program[J]. Infrared, 2015,36(8):32-36.]
[14] Fernández M A, Fernández M O, Quintano C. Sentinel-2A red-edge spectral indices suitability for discriminating burn severity[J]. International Journal of Applied Earth Observation and Geoinformation, 2016,50:170-175.
[15] Katja D, Anna G, Peter G, et al. Water constituents and water depth retrieval from Sentinel-2A:A first evaluation in an oligotrophic lake[J]. Remote Sensing, 2016,8(11):941-965.
[16] Lefebvre A, Sannier C, Corpetti T. Monitoring urban areas with Sentinel-2A data:Application to the update of the copernicus high resolution layer imperviousness degree[J]. Remote Sensing, 2016,8(7):606-621.
[17] Korhonen L, Hadi, Packalen P, et al. Comparison of Sentinel-2 and landsat 8 in the estimation of boreal forest canopy cover and leaf area index[J]. Remote Sensing of Environment, 2017,195:259-274.
[18]郑阳,吴炳方,张淼. Sentinel-2数据的冬小麦地上干生物量估算及评价[J].遥感学报,2017,21(2):318-328.[Zheng Y, Wu B F, Zhang M. Estimating the aboveground biomass of winter wheat using the Sentinel-2 data[J]. Journal of Remote Sensing, 2017,21(2):318-328.]
[19]彭光雄,宫阿都,崔伟宏,等.多时相影像的典型区农作物识别分类方法对比研究[J].地球信息科学学报,2009,11(2):225-230.[Peng G X, Gong A D, Cui W H, et al.Study on methods comparison of typical remote sensing classification based on multi-temporal images[J]. Journal of geo-information science, 2009,11(2):225-230.]
[20]李晓东,姜琦刚.基于多时相遥感数据的农田分类提取[J].农业工程学报,2015,31(7):145-150.[Li X D, Jiang Q G. Extraction of farmland classification based on multitemporal remote sensing data[J]. Transactions of the Chinese Society of Agricultural Engine-ering, 2015,31(7):145-150.]
[21]贾明明,王宗明,张柏,等.综合环境卫星与MODIS数据的面向对象土地覆盖分类方法[J].武汉大学学报·信息科学版,2014,39(3):305-310.[Jia M M, Wang Z M, Zhang B, et al. Land cover classification of compositing HJ-1and MODIS data base on object-based method[J]. Geomatics and Information Science of Wuhan University,2014,39(3):305-310.]
[22]刘明月,王宗明,满卫东,等.基于MODIS时序数据的Landsat8影像选取及面向对象分类方法的农作物分类[J].土壤与作物,2017,6(2):104-112.[Liu Mingyue Y,Wang Zongming M, Man Weidong D, et al. Crop Classification classification Based based on Multimulti-temporal Landsat8 OLI Imagery imagery and MODIS NDVI Time time Series series Datadata[J]. Soils and Crops, 2017,6(2):104-112.]
[23]毕恺艺,牛铮,黄妮,等.基于Sentinel-2A时序数据和面向对象决策树方法的植被识别[J].地理与地理信息科学,2017,33(5):16-33.[Bi Kaiyi Y, Niu Zheng, Huang Ni, et al. Identifying vegetation with decision tree model based on Object-Oriented method using Multimulti-temporal Sentinel-2A images[J]. Geography and Geo-Information Science, 2017,33(5):16-33.]
[24]郭昱杉,刘庆生,刘高焕,等.基于MODIS时序NDVI主要农作物种植信息提取研究[J].自然资源学报,2017,32(10):1808-1818.[Guo Y S, Liu Q S, Liu G H, et al. Extraction of main crops in Yellow River Delta based on MODIS NDVI time series[J]. Journal of Natural Resources, 2017,32(10):1808-1818.]
[25]鹿琳琳,郭华东.基于SPOT/VEGETATION时间序列的冬小麦物候提取方法[J].农业工程学报,2009,25(6):174-179.[Lu L L, Guo H D. Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2009,25(6):174-179.]
[26]欧阳玲,毛德华,王宗明,等.基于GF-1与Landsat8 OLI影像的作物种植结构与产量分析[J].农业工程学报,2017,33(11):147-156.[Ou Y L, Mao D H, Wang Z M, et al.Analysis crops planting structure and yield based on GF-1 and Landsat8 OLI images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(11):147-156.]
[27]刘佳,王利民,杨福刚,等.基于HJ时间序列数据的农作物种植面积估算[J].农业工程学报,2015,31(3):99-206.[Liu J, Wang L M, Yang F G, et al. Remote sensing estimation of crop planting area based on HJ time-series images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(3):199-206.]
[28]黄青,唐华俊,周清波,等.东北地区主要作物种植结构遥感提取及长势监测[J].农业工程学报,2010,26(9):218-223.[Huang Q, Tang H J, Zhou Q B, et al. Remote-sensing based monitoring of planting structure and growth condition of major crops in Northeast China[J]. Transactions of the Chinese Society of Agricultural Engineering,2010,26(9):218-223.]
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