基于多特征的杂草逆向定位方法与试验
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摘要
提出了一种基于多特征的杂草逆向定位方法。以田间作物作为研究对象,将多目标杂草定位问题转换为单目标的作物定位问题。采用作物叶片HU不变矩与形状特征的识别准确定位出每一株作物,然后基于颜色特征将作物区域以外的绿色植物均认定为杂草。设计了一款小型杂草定位装置,并应用在宽幅喷药机上。田间试验结果表明,在喷药机工作速度为5 km/h时,该系统对于大豆田间杂草识别的准确率为90%以上,较好地解决了杂草定位与精细喷洒农药问题。
Field weed is the big enemy of agricultural production,and also is one of the key problems that blocked the crop growth in Chinese agriculture. Accurate positioning weeds and realizing the variable precision applying pesticide or herbicide are particularly important. To solve various field weeds positioning difficult problems,a multi-feature based weed reverse positioning method was proposed. By taking the field crops as the research object,the multi-objective weed positioning issue was transformed into single objective crop recognition problem. Firstly,seven moment invariants and eight shape feature parameters were extracted from many of the individual soybean crop leaves,and the mean value of moment invariants and shape features were taken as standard soybean leave feature value. Secondly,after a series of image preprocessing such as image segmentation,regional feature match and connected component analysis,multi-feature recognition method with HU invariant moments and shape features of crops were utilized to accurately locate each crop plant. Finally,based on color feature,the green plants outside of the crops region were treated as weeds. Furthermore,a small weed positioning device was designed based on this method,which was applied to wide pesticide spraying machine. Field experiment results showed that weed recognition accuracy of this system for weed in soybean field was more than 90%when the spraying machine working speed was 5 km / h,hence weed positioning and pesticide accurate spraying problems could be well settled.
Field weed is the big enemy of agricultural production,and also is one of the key problems that blocked the crop growth in Chinese agriculture. Accurate positioning weeds and realizing the variable precision applying pesticide or herbicide are particularly important. To solve various field weeds positioning difficult problems,a multi-feature based weed reverse positioning method was proposed. By taking the field crops as the research object,the multi-objective weed positioning issue was transformed into single objective crop recognition problem. Firstly,seven moment invariants and eight shape feature parameters were extracted from many of the individual soybean crop leaves,and the mean value of moment invariants and shape features were taken as standard soybean leave feature value. Secondly,after a series of image preprocessing such as image segmentation,regional feature match and connected component analysis,multi-feature recognition method with HU invariant moments and shape features of crops were utilized to accurately locate each crop plant. Finally,based on color feature,the green plants outside of the crops region were treated as weeds. Furthermore,a small weed positioning device was designed based on this method,which was applied to wide pesticide spraying machine. Field experiment results showed that weed recognition accuracy of this system for weed in soybean field was more than 90%when the spraying machine working speed was 5 km / h,hence weed positioning and pesticide accurate spraying problems could be well settled.
引文
1 范德耀,姚青,杨保军,等.田间杂草识别与除草技术智能化研究进展[J].中国农业科学,2010,43(9):1823-1833.Fan Deyao,Yao Qing,Yang Baojun,et al.Progress in research on intelligentization of field weed recognition and weed control technology[J].Scientia Agricultura Sinica,2010,43(9):1823-1833.(in Chinese)
2 毛文华,张银桥,王辉,等.杂草信息实时获取技术与设备研究进展[J].农业机械学报,2013,44(1):190-195.Mao Wenhua,Zhang Yinqiao,Wang Hui,et al.Advance techniques and equipments for real-time weed detection[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(1):190-195.(in Chinese)
3 Lo'pez-Grandos F.Weed detection for site-specific weed management:mapping and real time approaches[J].Weed Research,2011,51(1):1-11.
4 Bakker T,Asselt K,Bontsema J.Systematic design of an autonomous platform for robotic weeding[J].Journal of Terramechanics,2010,47(2):63-73.
5 张小龙,谢正春,张念生,等.豌豆苗期田间杂草识别与变量喷洒控制系统[J].农业机械学报,2012,43(11):220-225,73.Zhang Xiaolong,Xie Zhengchun,Zhang Niansheng,et al.Weed recognition from pea seedling images and variable spraying control system[J].Transactions of the Chinese Society for Agricultural Machinery,2012,43(11):220-225,73.(in Chinese)
6 尹东富,陈树人,裴文超,等.基于处方图的室内变量喷药除草系统设计[J].农业工程学报,2011,27(4):131-135.Yin Dongfu,Chen Shuren,Pei Wenchao,et al.Design of map-based indoor variable weed spraying system[J].Transactions of the CSAE,2011,27(4):131-135.(in Chinese)
7 尹建军,沈宝国,陈树人.基于机器视觉的田间杂草定位技术[J].农业机械学报,2010,41(6):163-166.Yin Jianjun,Shen Baoguo,Chen Shuren.Positioning technology of field weed based on machine vision[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(6):163-166.(in Chinese)
8 何东健,乔永亮,李攀,等.基于SVM-DS多特征融合的杂草识别[J].农业机械学报,2013,44(2):182-187.He Dongjian,Qiao Yongliang,Li Pan,et al.Weed recognition based on SVM-DS multi-feature fusion[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(2):182-187.(in Chinese)
9 白敬,徐友,魏新华,等.基于光谱特性分析的冬油菜苗期田间杂草识别[J].农业工程学报,2013,29(20):128-134.Bai Jing,Xu You,Wei Xinhua,et al.Weed identification from winter rape at seedling stage based on spectrum characteristics analysis[J].Transactions of the CSAE,2013,29(20):128-134.(in Chinese)
10 毛文华,王辉,赵博,等.基于株心颜色的玉米田间杂草识别方法[J].农业工程学报,2009,25(13):161-164.Mao Wenhua,Wang Hui,Zhao Bo,et al.Weed detection method based the centre color of corn seedling[J].Transactions of the CSAE,2009,25(13):161-164.(in Chinese)
11 Tellaeche A,Burgos Artizzu X P,Pajares G,et al.A new vision based approach to differential spraying in precision agriculture[J].Computers and Electronics in Agriculture,2008,60(2):144-155.
12 Wu X W,Xu W Q,Song Y Y,et al.A detection method of weed in wheat field on machine vision[J].Procedia Engineering,2011,15:1998-2003
13 赵鹏,韦兴竹.基于多特征融合的田间杂草分类识别[J].农业机械学报,2014,45(3):275-281.Zhao Peng,Wei Xingzhu.Weed recognition in agricultural field using multiple feature fusions[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(3):275-281.(in Chinese)
14 陈树人,邹华东,吴瑞梅,等.基于高光谱图像技术的稻田苗期杂草稻识别[J].农业机械学报,2013,44(5):253-257,163.Chen Shuren,Zou Huadong,Wu Ruimei,et al.Identification for weedy rice at seeding stage based on hyper-spectral imaging technique[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(5):253-257,163.(in Chinese)
15 Singh K,Agrawal K N,Bora G C.Advanced techniques for weed and crop identification for site-specific weed management[J].Biosystems Engineering,2011,109(1):52-64.
16 赵川源,何东健,乔永亮.基于多光谱图像和数据挖掘的多特征杂草识别方法[J].农业工程学报,2013,29(2):192-198.Zhao Chuanyuan,He Dongjian,Qiao Yongliang.Identification method of multi-feature weed based on multi-spectral images and data mining[J].Transactions of the CSAE,2013,29(2):192-198.(in Chinese)
17 唐晶磊,何东健,景旭,等.基于SVM的可见/近红外光的玉米和杂草的多类识别[J].红外与毫米波学报,2011,30(2):97-103.Tang Jinglei,He Dongjian,Jing Xu,et al.Maize seedling/weed multiclass detection in visible/near infrared image based on SVM[J].Journal of Infrared and Millimeter Waves,2011,30(2):97-103.(in Chinese)
18 李先锋,朱伟兴,纪滨,等.基于特征优化和LS-SVM的棉田杂草识别[J].农业机械学报,2010,41(11):168-172.Li Xianfeng,Zhu Weixing,Ji Bin,et al.Weed identification based on features optimization and LS-SVM in the cotton field[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(11):168-172.(in Chinese)
19 胡炼,罗锡文,曾山,等.基于机器视觉的株间机械除草装置的作物识别与定位方法[J].农业工程学报,2013,29(10):12-18.Hu Lian,Luo Xiwen,Zeng Shan,et al.Plant recognition and localization for intra-row mechanical weeding device based on machine vision[J].Transactions of the CSAE,2013,29(10):12-18.(in Chinese)
20 Song Zhenghe,Zhao Bo,Zhu Zhongxiang,et al.Research on traffic number recognition based on neural network and invariant moments[C]∥Proceedings of the Sixth International Conference on Machine Learning and Cybernetics,2007:389-393.
21 Premaratne P,Premaratne M.Image matching using moment invariants[J].Neurocomputing,2014,137:65-70.
22 张伟,何金国.Hu不变矩的构造与推广[J].计算机应用,2010,30(9):2449-2452.Zhang Wei,He Jinguo.Construction and generalization of Hu moment invariants[J].Journal of Computer Application,2010,30 (9):2449-2452.(in Chinese)
23 高勇,何刚,张晓辉.基于不变矩和禁忌搜索算法的图像识别方法[J].系统工程与电子技术,2010,32(4):851-853.Gao Yong,He Gang,Zhang Xiaohui.Image recognition algorithm based on moment invariant and tabu search[J].Systems Engineering and Electronics,2010,32(4):851-853.(in Chinese)
24 曾接贤,付俊,符祥.特征点和不变矩结合的遥感图像飞机目标识别[J].中国图象图形学报,2014,19(4):592-602.Zeng Jiexian,Fu Jun,Fu Xiang.Aircraft target recognition in remote sensing images based on distribution of the feature points and invariant moments[J].Journal of Image and Graphics,2014,19(4):592-602.(in Chinese)
2 毛文华,张银桥,王辉,等.杂草信息实时获取技术与设备研究进展[J].农业机械学报,2013,44(1):190-195.Mao Wenhua,Zhang Yinqiao,Wang Hui,et al.Advance techniques and equipments for real-time weed detection[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(1):190-195.(in Chinese)
3 Lo'pez-Grandos F.Weed detection for site-specific weed management:mapping and real time approaches[J].Weed Research,2011,51(1):1-11.
4 Bakker T,Asselt K,Bontsema J.Systematic design of an autonomous platform for robotic weeding[J].Journal of Terramechanics,2010,47(2):63-73.
5 张小龙,谢正春,张念生,等.豌豆苗期田间杂草识别与变量喷洒控制系统[J].农业机械学报,2012,43(11):220-225,73.Zhang Xiaolong,Xie Zhengchun,Zhang Niansheng,et al.Weed recognition from pea seedling images and variable spraying control system[J].Transactions of the Chinese Society for Agricultural Machinery,2012,43(11):220-225,73.(in Chinese)
6 尹东富,陈树人,裴文超,等.基于处方图的室内变量喷药除草系统设计[J].农业工程学报,2011,27(4):131-135.Yin Dongfu,Chen Shuren,Pei Wenchao,et al.Design of map-based indoor variable weed spraying system[J].Transactions of the CSAE,2011,27(4):131-135.(in Chinese)
7 尹建军,沈宝国,陈树人.基于机器视觉的田间杂草定位技术[J].农业机械学报,2010,41(6):163-166.Yin Jianjun,Shen Baoguo,Chen Shuren.Positioning technology of field weed based on machine vision[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(6):163-166.(in Chinese)
8 何东健,乔永亮,李攀,等.基于SVM-DS多特征融合的杂草识别[J].农业机械学报,2013,44(2):182-187.He Dongjian,Qiao Yongliang,Li Pan,et al.Weed recognition based on SVM-DS multi-feature fusion[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(2):182-187.(in Chinese)
9 白敬,徐友,魏新华,等.基于光谱特性分析的冬油菜苗期田间杂草识别[J].农业工程学报,2013,29(20):128-134.Bai Jing,Xu You,Wei Xinhua,et al.Weed identification from winter rape at seedling stage based on spectrum characteristics analysis[J].Transactions of the CSAE,2013,29(20):128-134.(in Chinese)
10 毛文华,王辉,赵博,等.基于株心颜色的玉米田间杂草识别方法[J].农业工程学报,2009,25(13):161-164.Mao Wenhua,Wang Hui,Zhao Bo,et al.Weed detection method based the centre color of corn seedling[J].Transactions of the CSAE,2009,25(13):161-164.(in Chinese)
11 Tellaeche A,Burgos Artizzu X P,Pajares G,et al.A new vision based approach to differential spraying in precision agriculture[J].Computers and Electronics in Agriculture,2008,60(2):144-155.
12 Wu X W,Xu W Q,Song Y Y,et al.A detection method of weed in wheat field on machine vision[J].Procedia Engineering,2011,15:1998-2003
13 赵鹏,韦兴竹.基于多特征融合的田间杂草分类识别[J].农业机械学报,2014,45(3):275-281.Zhao Peng,Wei Xingzhu.Weed recognition in agricultural field using multiple feature fusions[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(3):275-281.(in Chinese)
14 陈树人,邹华东,吴瑞梅,等.基于高光谱图像技术的稻田苗期杂草稻识别[J].农业机械学报,2013,44(5):253-257,163.Chen Shuren,Zou Huadong,Wu Ruimei,et al.Identification for weedy rice at seeding stage based on hyper-spectral imaging technique[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(5):253-257,163.(in Chinese)
15 Singh K,Agrawal K N,Bora G C.Advanced techniques for weed and crop identification for site-specific weed management[J].Biosystems Engineering,2011,109(1):52-64.
16 赵川源,何东健,乔永亮.基于多光谱图像和数据挖掘的多特征杂草识别方法[J].农业工程学报,2013,29(2):192-198.Zhao Chuanyuan,He Dongjian,Qiao Yongliang.Identification method of multi-feature weed based on multi-spectral images and data mining[J].Transactions of the CSAE,2013,29(2):192-198.(in Chinese)
17 唐晶磊,何东健,景旭,等.基于SVM的可见/近红外光的玉米和杂草的多类识别[J].红外与毫米波学报,2011,30(2):97-103.Tang Jinglei,He Dongjian,Jing Xu,et al.Maize seedling/weed multiclass detection in visible/near infrared image based on SVM[J].Journal of Infrared and Millimeter Waves,2011,30(2):97-103.(in Chinese)
18 李先锋,朱伟兴,纪滨,等.基于特征优化和LS-SVM的棉田杂草识别[J].农业机械学报,2010,41(11):168-172.Li Xianfeng,Zhu Weixing,Ji Bin,et al.Weed identification based on features optimization and LS-SVM in the cotton field[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(11):168-172.(in Chinese)
19 胡炼,罗锡文,曾山,等.基于机器视觉的株间机械除草装置的作物识别与定位方法[J].农业工程学报,2013,29(10):12-18.Hu Lian,Luo Xiwen,Zeng Shan,et al.Plant recognition and localization for intra-row mechanical weeding device based on machine vision[J].Transactions of the CSAE,2013,29(10):12-18.(in Chinese)
20 Song Zhenghe,Zhao Bo,Zhu Zhongxiang,et al.Research on traffic number recognition based on neural network and invariant moments[C]∥Proceedings of the Sixth International Conference on Machine Learning and Cybernetics,2007:389-393.
21 Premaratne P,Premaratne M.Image matching using moment invariants[J].Neurocomputing,2014,137:65-70.
22 张伟,何金国.Hu不变矩的构造与推广[J].计算机应用,2010,30(9):2449-2452.Zhang Wei,He Jinguo.Construction and generalization of Hu moment invariants[J].Journal of Computer Application,2010,30 (9):2449-2452.(in Chinese)
23 高勇,何刚,张晓辉.基于不变矩和禁忌搜索算法的图像识别方法[J].系统工程与电子技术,2010,32(4):851-853.Gao Yong,He Gang,Zhang Xiaohui.Image recognition algorithm based on moment invariant and tabu search[J].Systems Engineering and Electronics,2010,32(4):851-853.(in Chinese)
24 曾接贤,付俊,符祥.特征点和不变矩结合的遥感图像飞机目标识别[J].中国图象图形学报,2014,19(4):592-602.Zeng Jiexian,Fu Jun,Fu Xiang.Aircraft target recognition in remote sensing images based on distribution of the feature points and invariant moments[J].Journal of Image and Graphics,2014,19(4):592-602.(in Chinese)