基于改进K-means聚类算法的大田麦穗自动计数
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摘要
单位种植面积的小麦麦穗数量是评估小麦产量和小麦种植密度的一个重要参量。为了实现高效、自动地麦穗计数,该文提出了基于改进K-means的小麦麦穗计数方法。该方法建立从图像低层颜色特征到图像中包含麦穗的一个直接分类关系,从而不需要再对图像进行分割或检测。以颜色特征聚类为基础的这种方法能够估计麦穗在空间局部区域中数量,并且在不需要训练的情况下更具有可扩展性。统计试验结果表明,该文算法能够适应不同光照环境,麦穗计数的准确率达到94.69%,超过了传统基于图像颜色特征和纹理特征分割的麦穗计数方法 93.1%的准确率。
The amount of wheat ears per unit area is an important parameter for assessing wheat yield and wheat planting density.Generally,phenotype parameters should be obtained by manual count technique which is time-consuming and needs great effort.Aiming at the traditional image segmentation method based on color feature,texture feature and Haar feature,in this paper,we proposed an improved K-means algorithm for estimating the number of wheat ears.This method established a direct mapping relationship from the low-level features of the image to the number of wheat ears in the image through the color feature so that the target did not need to be segmented or detected.This type of method was more suitable for complex lighting and dense wheat ears,and had higher computational efficiency than counting methods based on image segmentation.This method made full use of the color feature of wheat ear image,took the area feature of local region extracted from local region as the basis of wheat ear judgment,and used the number of sub-regions in clustering region as the estimation value of wheat ear number,thus avoiding the task of target detection and location,and greatly improving the accuracy of Wheat ear counting.According to classification results of K-means method,the wheat ear image was divided into three regions.The green area pixel value representing the wheat eat region was set to 255,the pixel value of other areas was set to 0,so the binary image of wheat ears was obtained.Most of the binarized areas in the image were wheat ears,and a small part of the binarized area that was too small or too large was caused by the leaves.Following the steps were used to count wheat ears:1) extracting the connected regions of the binary image and labeling them;2) calculating the area of each connected region,the area was represented by the number of pixels in the region;3) using the following method to filter out the connected region where the area was too small or too large;4) finally,the number of wheat ears was obtained by counting the retained binary regions.This method established a direct classification relation from the image color feature of the lower layer to the image containing wheat ears so that the image did not need to be segmented or detected.Based on color feature clustering,this method could estimate the number of wheat ears in local area of space,and it was more scalable without training.The average prediction precision of the total wheatear number in a wheatear image for 12 wheatear images was 94.69%.And the Statistical error of the total wheatear number was between 2.17% and 7.41%,the average statistical error was 5.31%.The statistical experiment results showed that the accuracy of this algorithm to wheat ear counting was better than the traditional method based on image color feature and texture feature.
The amount of wheat ears per unit area is an important parameter for assessing wheat yield and wheat planting density.Generally,phenotype parameters should be obtained by manual count technique which is time-consuming and needs great effort.Aiming at the traditional image segmentation method based on color feature,texture feature and Haar feature,in this paper,we proposed an improved K-means algorithm for estimating the number of wheat ears.This method established a direct mapping relationship from the low-level features of the image to the number of wheat ears in the image through the color feature so that the target did not need to be segmented or detected.This type of method was more suitable for complex lighting and dense wheat ears,and had higher computational efficiency than counting methods based on image segmentation.This method made full use of the color feature of wheat ear image,took the area feature of local region extracted from local region as the basis of wheat ear judgment,and used the number of sub-regions in clustering region as the estimation value of wheat ear number,thus avoiding the task of target detection and location,and greatly improving the accuracy of Wheat ear counting.According to classification results of K-means method,the wheat ear image was divided into three regions.The green area pixel value representing the wheat eat region was set to 255,the pixel value of other areas was set to 0,so the binary image of wheat ears was obtained.Most of the binarized areas in the image were wheat ears,and a small part of the binarized area that was too small or too large was caused by the leaves.Following the steps were used to count wheat ears:1) extracting the connected regions of the binary image and labeling them;2) calculating the area of each connected region,the area was represented by the number of pixels in the region;3) using the following method to filter out the connected region where the area was too small or too large;4) finally,the number of wheat ears was obtained by counting the retained binary regions.This method established a direct classification relation from the image color feature of the lower layer to the image containing wheat ears so that the image did not need to be segmented or detected.Based on color feature clustering,this method could estimate the number of wheat ears in local area of space,and it was more scalable without training.The average prediction precision of the total wheatear number in a wheatear image for 12 wheatear images was 94.69%.And the Statistical error of the total wheatear number was between 2.17% and 7.41%,the average statistical error was 5.31%.The statistical experiment results showed that the accuracy of this algorithm to wheat ear counting was better than the traditional method based on image color feature and texture feature.
引文
[1]Olsen J,Kristensen L,Weiner J.Influence of sowing density and spatial pattern of spring wheat(Triticum aestivum)on the suppression of different weed species[J].Weed Biol Manag,2006,34(6):165?173.
[2]Olsen J,Weiner J.The influence of Triticum aestivum density,sowing pattern and nitrogen fertilization on leaf area index and its spatial variation[J].Basic Appl Ecol,2007,43(8):252?265.
[3]刘涛,孙成明,王力坚,等.基于图像处理技术的大田麦穗计数[J].农业机械学报,2014,45(2):282?290.Liu Tao,Sun Chengming,Wang Lijian,et al.In-field wheatear counting based on image processing technology[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(2):282?290.(in Chinese with English abstract)
[4]范梦阳,马钦,刘峻明,等.基于机器视觉的大田环境小麦麦穗计数方法[J].农业机械学报,2015,46(12):234-239.Fan Mengyang,Ma Qin,Liu Junming,et al.Counting method of wheat ear in field based on machine vision technology[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(12):234?239.(in Chinese with English abstract)
[5]赵锋,王克俭,苑迎春.基于颜色特征和Ada Boost算法的麦穗识别的研究[J].作物杂志,2014,34(1):140-145.Zhao Feng,Wang Kejian,Yuan Yingchun.Study on wheat spike identification based on color features and adaboost algorithm[J].Crops,2014,34(1):140?145.(in Chinese with English abstract)
[6]Zhu Y,Cao Z,Lu H,et al.In-field automatic observation of wheat heading stage using computer vision[J].Biosyst Eng,2016,53(143):28?41.
[7]Cointault F,Guerin D,Guillemin J,et al.In-field Triticum aestivum ear counting using colour-texture image analysis[J].N Z J Crop Hortic Sci.,2008,21(36):117?130.
[8]Jose A.Fernandez-Gallego,Shawn C Kefauver,et al.Wheat ear counting in-field conditions:High throughput and low-cost approach using RGB images[J].Plant Methods,2018,14(22):21?33.
[9]Dornbusch T,Hawkesford M,Jansen M,et al.Digital field phenotyping by LemnaTec[Z].Aachen:LemnaTec,2015.
[10]Erikson M.Species classification of individually segmented tree crowns in high-resolution aerial images using radiometric and morphologic image measures[J].Remote Sens Environ,2004,32(91):469?477.
[11]Leckie D G,Gougeon F A,Tinis S,et al.Automated tree recognition in old growth conifer stands with high resolution digital imagery[J].Remote Sens Environ,2005,33(94):311?326.
[12]Leckie D G,Gougeon F A,Walsworth N,et al.Stand delineation and composition estimation using semi automated individual tree crown analysis[J].Remote Sens Environ,2003,31(85):355?369.
[13]Pouliot D A,King D J,Bell F W,et al.Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration[J].Remote Sens Environ.,2002,30(82):322?334.
[14]Wulder M,Niemann K O,Goodenough D G.Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery[J].Remote Sens Environ,2000,28(73):103?114.
[15]Font D,PallejàT,Tresanchez M,et al.Counting red grapes in vineyards by detecting specular spherical reflection peaks in RGB images obtained at night with artificial illumination[J].Comput Electron Agric,2014,26(108):105?117.
[16]Stajnko D,Lakota M,Ho?evar M.Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging[J].Comput Electron Agric,2004,16(42):31?42.
[17]Tian L F,Slaughter D C.Environmentally adaptive segmentation algorithm for outdoor image segmentation[J].Comput Electron Agric,1998,21:153?168.
[18]Arroyo J,Guijarro M,Pajares G.An instance-based learning approach for thresholding in crop images under different outdoor conditions[J].Comput Electron Agric,2016,28(127):669?679.
[19]Guijarro M,Pajares G,Riomoros I,et al.Automatic segmentation of relevant textures in agricultural images[J].Comput Electron Agric,2011,75(1):75?83.
[20]Li H,Lee W S,Wang K.Identifying blueberry fruit of different growth stages using natural outdoor color images[J].Comput Electron Agric,2014,106:91?101.
[21]Payne A B,Walsh K B,Subedi P P,et al.Estimation of mango crop yield using image analysis:Segmentation method[J].Comput Electron Agric,2013,25(91):57?64.
[22]Payne A,Walsh K,Subedi P,et al.Estimating mango crop yield using image analysis using fruit at“stone hardening”stage and night time imaging[J].Comput Electron Agric,2014,100:160?167.
[23]Ruiz-Ruiz G,Gómez-Gil J,Navas-Gracia L M.Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm(EASA)[J].Comput Electron Agric,2009,68(1):88?96.
[24]Liu Shouyang,Fred Baret,Denis Allard,et al.A method to estimate plant density and plant spacing heterogeneity:Application to wheat crops[J].Plant Methods,2017,8(3):1-10.
[25]赵锋.基于颜色特征和改进AdaBoost算法的麦穗识别的研究[D].保定:河北农业大学,2014.Zhao Feng.Research on Wheat Spike Identification Based on Color Features and Improved AdaBoost Algorithm[D].Baoding:Agricultural University of Hebei,2014.(in Chinese with English abstract)
[26]陈含,吕行军,田凤珍,等.基于Sobel算子边缘检测的麦穗图像分割[J].农机化研究,2013,35(3):33?36.Chen Han,LüXingjun,Tian Fengzhen,et al.Wheat panicle image segmentation based on sobel operator-edge detection[J].Journal of Agricultural Mechanization Research,2013,35(3):33-36.(in Chinese with English abstract)
[27]Tang W,Zhang Y,Zhang D,et al.Corn tassel detection based on image processing[C]//International Workshop on Image Processing and Optical Engineering.International Society for Optics and Photonics,2012,8335:83350J-1-83350J-7.
[28]Cointault F,Guerin D,Guillemin J P,et al.In-field Triticum aestivum ear counting using colour-texture image analysis[J].New Zealand Journal of Crop and Horticultural Science,2008,36(2):117-130.
[29]Frédéric C,Frédéric J,Gilles R,et al.Texture,color and frequential proxy-detection image processing for crop Characterization in a Context of Precision Agriculture[M].Godwin Aflakpui:Agricultural Science,2012.
[30]Zhu Y,Cao Z,Lu H,et al.In-field automatic observation of wheat heading stage using computer vision[J].Biosystems Engineering,2016,143:28-41.
[31]Duan L,Huang C,Chen G,et al.Determination of rice panicle numbers during heading by multi-angle imaging[J].The Crop Journal,2015,3(3):211-219.
[32]Lecun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521(7553):436-444.
[33]周云成,许童羽,邓寒冰,等.基于面向通道分组卷积网络的番茄主要器官实时识别[J].农业工程学报,2018,34(10):153-162.Zhou Yuncheng,Xu Tongyu,Deng Hanbing,et al.Real-time recognition of main organs in tomato based on channel wise group convolutional network[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(10):153-162.(in Chinese with English abstract)
[34]Pound M P,Atkinson J A,Townsend A J,et al.Deep machine learning provides state-of-the-art performance in image-based plant phenotyping[J].Gigascience,2017,6(10):1-10.
[35]Xiong X,Duan L,Liu L,et al.Panicle-SEG:A robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization[J].Plant Methods,2017,13(1):104-113.
[2]Olsen J,Weiner J.The influence of Triticum aestivum density,sowing pattern and nitrogen fertilization on leaf area index and its spatial variation[J].Basic Appl Ecol,2007,43(8):252?265.
[3]刘涛,孙成明,王力坚,等.基于图像处理技术的大田麦穗计数[J].农业机械学报,2014,45(2):282?290.Liu Tao,Sun Chengming,Wang Lijian,et al.In-field wheatear counting based on image processing technology[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(2):282?290.(in Chinese with English abstract)
[4]范梦阳,马钦,刘峻明,等.基于机器视觉的大田环境小麦麦穗计数方法[J].农业机械学报,2015,46(12):234-239.Fan Mengyang,Ma Qin,Liu Junming,et al.Counting method of wheat ear in field based on machine vision technology[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(12):234?239.(in Chinese with English abstract)
[5]赵锋,王克俭,苑迎春.基于颜色特征和Ada Boost算法的麦穗识别的研究[J].作物杂志,2014,34(1):140-145.Zhao Feng,Wang Kejian,Yuan Yingchun.Study on wheat spike identification based on color features and adaboost algorithm[J].Crops,2014,34(1):140?145.(in Chinese with English abstract)
[6]Zhu Y,Cao Z,Lu H,et al.In-field automatic observation of wheat heading stage using computer vision[J].Biosyst Eng,2016,53(143):28?41.
[7]Cointault F,Guerin D,Guillemin J,et al.In-field Triticum aestivum ear counting using colour-texture image analysis[J].N Z J Crop Hortic Sci.,2008,21(36):117?130.
[8]Jose A.Fernandez-Gallego,Shawn C Kefauver,et al.Wheat ear counting in-field conditions:High throughput and low-cost approach using RGB images[J].Plant Methods,2018,14(22):21?33.
[9]Dornbusch T,Hawkesford M,Jansen M,et al.Digital field phenotyping by LemnaTec[Z].Aachen:LemnaTec,2015.
[10]Erikson M.Species classification of individually segmented tree crowns in high-resolution aerial images using radiometric and morphologic image measures[J].Remote Sens Environ,2004,32(91):469?477.
[11]Leckie D G,Gougeon F A,Tinis S,et al.Automated tree recognition in old growth conifer stands with high resolution digital imagery[J].Remote Sens Environ,2005,33(94):311?326.
[12]Leckie D G,Gougeon F A,Walsworth N,et al.Stand delineation and composition estimation using semi automated individual tree crown analysis[J].Remote Sens Environ,2003,31(85):355?369.
[13]Pouliot D A,King D J,Bell F W,et al.Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration[J].Remote Sens Environ.,2002,30(82):322?334.
[14]Wulder M,Niemann K O,Goodenough D G.Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery[J].Remote Sens Environ,2000,28(73):103?114.
[15]Font D,PallejàT,Tresanchez M,et al.Counting red grapes in vineyards by detecting specular spherical reflection peaks in RGB images obtained at night with artificial illumination[J].Comput Electron Agric,2014,26(108):105?117.
[16]Stajnko D,Lakota M,Ho?evar M.Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging[J].Comput Electron Agric,2004,16(42):31?42.
[17]Tian L F,Slaughter D C.Environmentally adaptive segmentation algorithm for outdoor image segmentation[J].Comput Electron Agric,1998,21:153?168.
[18]Arroyo J,Guijarro M,Pajares G.An instance-based learning approach for thresholding in crop images under different outdoor conditions[J].Comput Electron Agric,2016,28(127):669?679.
[19]Guijarro M,Pajares G,Riomoros I,et al.Automatic segmentation of relevant textures in agricultural images[J].Comput Electron Agric,2011,75(1):75?83.
[20]Li H,Lee W S,Wang K.Identifying blueberry fruit of different growth stages using natural outdoor color images[J].Comput Electron Agric,2014,106:91?101.
[21]Payne A B,Walsh K B,Subedi P P,et al.Estimation of mango crop yield using image analysis:Segmentation method[J].Comput Electron Agric,2013,25(91):57?64.
[22]Payne A,Walsh K,Subedi P,et al.Estimating mango crop yield using image analysis using fruit at“stone hardening”stage and night time imaging[J].Comput Electron Agric,2014,100:160?167.
[23]Ruiz-Ruiz G,Gómez-Gil J,Navas-Gracia L M.Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm(EASA)[J].Comput Electron Agric,2009,68(1):88?96.
[24]Liu Shouyang,Fred Baret,Denis Allard,et al.A method to estimate plant density and plant spacing heterogeneity:Application to wheat crops[J].Plant Methods,2017,8(3):1-10.
[25]赵锋.基于颜色特征和改进AdaBoost算法的麦穗识别的研究[D].保定:河北农业大学,2014.Zhao Feng.Research on Wheat Spike Identification Based on Color Features and Improved AdaBoost Algorithm[D].Baoding:Agricultural University of Hebei,2014.(in Chinese with English abstract)
[26]陈含,吕行军,田凤珍,等.基于Sobel算子边缘检测的麦穗图像分割[J].农机化研究,2013,35(3):33?36.Chen Han,LüXingjun,Tian Fengzhen,et al.Wheat panicle image segmentation based on sobel operator-edge detection[J].Journal of Agricultural Mechanization Research,2013,35(3):33-36.(in Chinese with English abstract)
[27]Tang W,Zhang Y,Zhang D,et al.Corn tassel detection based on image processing[C]//International Workshop on Image Processing and Optical Engineering.International Society for Optics and Photonics,2012,8335:83350J-1-83350J-7.
[28]Cointault F,Guerin D,Guillemin J P,et al.In-field Triticum aestivum ear counting using colour-texture image analysis[J].New Zealand Journal of Crop and Horticultural Science,2008,36(2):117-130.
[29]Frédéric C,Frédéric J,Gilles R,et al.Texture,color and frequential proxy-detection image processing for crop Characterization in a Context of Precision Agriculture[M].Godwin Aflakpui:Agricultural Science,2012.
[30]Zhu Y,Cao Z,Lu H,et al.In-field automatic observation of wheat heading stage using computer vision[J].Biosystems Engineering,2016,143:28-41.
[31]Duan L,Huang C,Chen G,et al.Determination of rice panicle numbers during heading by multi-angle imaging[J].The Crop Journal,2015,3(3):211-219.
[32]Lecun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521(7553):436-444.
[33]周云成,许童羽,邓寒冰,等.基于面向通道分组卷积网络的番茄主要器官实时识别[J].农业工程学报,2018,34(10):153-162.Zhou Yuncheng,Xu Tongyu,Deng Hanbing,et al.Real-time recognition of main organs in tomato based on channel wise group convolutional network[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(10):153-162.(in Chinese with English abstract)
[34]Pound M P,Atkinson J A,Townsend A J,et al.Deep machine learning provides state-of-the-art performance in image-based plant phenotyping[J].Gigascience,2017,6(10):1-10.
[35]Xiong X,Duan L,Liu L,et al.Panicle-SEG:A robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization[J].Plant Methods,2017,13(1):104-113.