基于地面激光雷达的田间花生冠层高度测量系统研制
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摘要
在花生育种研究中对于冠层高度的获取主要依靠人工测量,不但费时费力,而且存在一定的主观性。为解决这一问题,该文构建了一个田间花生冠层高度特性表型信息获取系统,利用地面激光雷达Li DAR对花生冠层结构进行扫描,获取其三维点云数据;采用多项式曲线拟合算法对点云数据进行分析,描绘冠层的大致轮廓并确定其边界,以得到目标冠层的有效数据集;通过对有效点云数据集生成的冠层高度矩阵分析,得到冠层的高度特性。试验结果表明,利用该系统获取的花生冠层平均高度与手工测量值最小偏差为2%,最大偏差为32%,最大偏差受地形影响和植株早期冠层本身的低高度所致,平均测量偏差约为11%,位于15%的可接受范围之内。该系统可以实现田间花生冠层高度信息的快速自动化获取,减少了人力成本的投入,该研究可为花生育种研究提供参考。
Plant height is a very important phenotypic trait in peanut breeding research. It is a key parameter not only indicating the growth state of peanut, but also calculating peanut biomass and yield. At present, the acquisition of peanut plant height in breeding research mainly relies on manual measurement, which is not only time-consuming and laborious, but also has certain subjectivity. Therefore, it is necessary to design a measurement system that can be used in the field and can quickly and accurately obtain the height of peanut canopy. In this study, a LiD AR-based field peanut canopy height information acquisition system was constructed, which was a mobile data acquisition platform designed for field conditions, and a data processing and analysis algorithm was developed to extract the height of peanut canopy. The sensor was equipped with a LiD AR(LMS291-05 S, SICK) for scanning the peanut canopy, an RGB camera is used to capture the image of the peanut canopy, an encoder was used to record the moving distance of the system platform; all sensors were powered by a 24 V battery and all data were uploaded to a laptop. An experimental field was established with three peanut cultivars at Oklahoma State University's Caddo Research Station in Fort Cobb, Oklahoma state, USA in May and the data collections were conducted monthly from July to September 2015. There were 12 planting plots in the field, which were arranged in a straight line, and the length of each plot was 4.75 m, the interval between adjacent plots was 1.52 m, and the interval between ridges was 0.91 m in a plot. SWR, MCD and GA04 S three different breeds of peanuts were planted in 12 different plots, each of which was repeated four times, and the plots of the same breed were not adjacent to each other. The ground-based LiDAR used for this research was a line-scan laser scanner with a scan-angle of 100?, an angle resolution of 0.25?, and a scanning speed of 53 ms. A wide aperture angle of 100? was used for LiDAR in order to ensure a complete scan of target canopy. As a result, the collected data included those from the adjacent rows. An algorithm was developed to extract the region of the interested data acquired by the system through the polynomial curve fitting method. To provide fixed reference points within each plot over the three collection periods, some metal posts were installed within the center length of each scanned row. These metal stakes were caught by the LiD AR in all the data file, in addition, noise also presented in the raw data. Therefore, a data filtering and correction algorithm was developed to eliminate the interference information. All valid canopy height data, which were processed according to the previously described preprocessing algorithms, were organized into a height matrix, that was, all the canopy height values scanned in each plot were constructed into a canopy height matrix, and then the mean heights were analyzed and calculated. The results showed that the minimum deviation of the average canopy height between obtained by the system and the manual measurement was 2%, the maximum deviation effected by topography was 32%, and the average deviation was about 11%, but the measurement deviation was gradually decreasing with the growth of peanut plants. The accuracy of this result was acceptable compared with the height of the peanut plant, and the collection of canopy height information by the system can greatly reduce working time and the input of artificial labor, and improve the efficiency of crop phenotypic information acquisition and analysis. Future research will focus on the rapid movement and manipulation of measurement system, and apply information fusion to data processing of multiple sensors.
Plant height is a very important phenotypic trait in peanut breeding research. It is a key parameter not only indicating the growth state of peanut, but also calculating peanut biomass and yield. At present, the acquisition of peanut plant height in breeding research mainly relies on manual measurement, which is not only time-consuming and laborious, but also has certain subjectivity. Therefore, it is necessary to design a measurement system that can be used in the field and can quickly and accurately obtain the height of peanut canopy. In this study, a LiD AR-based field peanut canopy height information acquisition system was constructed, which was a mobile data acquisition platform designed for field conditions, and a data processing and analysis algorithm was developed to extract the height of peanut canopy. The sensor was equipped with a LiD AR(LMS291-05 S, SICK) for scanning the peanut canopy, an RGB camera is used to capture the image of the peanut canopy, an encoder was used to record the moving distance of the system platform; all sensors were powered by a 24 V battery and all data were uploaded to a laptop. An experimental field was established with three peanut cultivars at Oklahoma State University's Caddo Research Station in Fort Cobb, Oklahoma state, USA in May and the data collections were conducted monthly from July to September 2015. There were 12 planting plots in the field, which were arranged in a straight line, and the length of each plot was 4.75 m, the interval between adjacent plots was 1.52 m, and the interval between ridges was 0.91 m in a plot. SWR, MCD and GA04 S three different breeds of peanuts were planted in 12 different plots, each of which was repeated four times, and the plots of the same breed were not adjacent to each other. The ground-based LiDAR used for this research was a line-scan laser scanner with a scan-angle of 100?, an angle resolution of 0.25?, and a scanning speed of 53 ms. A wide aperture angle of 100? was used for LiDAR in order to ensure a complete scan of target canopy. As a result, the collected data included those from the adjacent rows. An algorithm was developed to extract the region of the interested data acquired by the system through the polynomial curve fitting method. To provide fixed reference points within each plot over the three collection periods, some metal posts were installed within the center length of each scanned row. These metal stakes were caught by the LiD AR in all the data file, in addition, noise also presented in the raw data. Therefore, a data filtering and correction algorithm was developed to eliminate the interference information. All valid canopy height data, which were processed according to the previously described preprocessing algorithms, were organized into a height matrix, that was, all the canopy height values scanned in each plot were constructed into a canopy height matrix, and then the mean heights were analyzed and calculated. The results showed that the minimum deviation of the average canopy height between obtained by the system and the manual measurement was 2%, the maximum deviation effected by topography was 32%, and the average deviation was about 11%, but the measurement deviation was gradually decreasing with the growth of peanut plants. The accuracy of this result was acceptable compared with the height of the peanut plant, and the collection of canopy height information by the system can greatly reduce working time and the input of artificial labor, and improve the efficiency of crop phenotypic information acquisition and analysis. Future research will focus on the rapid movement and manipulation of measurement system, and apply information fusion to data processing of multiple sensors.
引文
[1]王传宇,郭新宇,杜建军,等.基于时间序列图像的玉米植株干旱胁迫表型检测方法[J].农业工程学报,2016,32(21):189-195.Wang Chuanyu,Guo Xinyu,Du Jianjun,et al.Maize plant drought stress phenotype testing method based on time-series images[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(21):189-195.(in Chinese with English abstract)
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[8]Zaman Q U,Schumann A W,Hostler H K.Estimation of citrus fruit yield using ultrasonically-sensed tree size[J].Applied Engineering in Agriculture,2006,22(1):39-44.
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[10]Luvall J C,Lieberman D,Lieberman M,et al.Estimation of tropical forest canopy temperatures,thermal response numbers,and evapotranspiration using an aircraft-based thermal sensor[J].Photogrammetric Engineering&Remote Sensing,1990,56(10):1393-1401.
[11]Jones H G,Serraj R,Loveys B R,et al.Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field[J].Functional Plant Biology,2009,36(10/11):978-989.
[12]Wang Mingfei,Dong Daming,Zheng Wengang,et al.Using infrared sensor for large area canopy total temperature measurements of rice plants[J].Applied Engineering in Agriculture,2013,29(1):115-122.
[13]Mahendra Bhandari.Use of Infrared Thermal Imaging for Estimating Canopy Temperature in Wheat and Maize[D].Canyon:West Texas A&M University,2016.
[14]刘建刚,赵春江,杨贵军,等.无人机遥感解析田间作物表型信息研究进展[J].农业工程学报,2016,32(24):98-106.Liu Jiangang,Zhao Chunjiang,Yang Guijun,et al.Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(24):98-106.(in Chinese with English abstract)
[15]Dimitry Van der Zande,Wouter Hoet,Inge Jonckheere,et al.Influence of measurement set-up of ground-based LiDAR for derivation of tree structure[J].Agricultural and Forest Meteorology,2006,141(2/3/4):147-160.
[16]Cote Jean-Francois,Fournier Richard A,Frazer Gordon W,et al.A fine-scale architectural model of trees to enhance LiDAR-derived measurements of forest canopy structure[J].Agricultural and Forest Meteorology,2012,166-167(1):72-85.
[17]McMahon Sean M,Bebber Daniel P,Butt Nathalie,et al.Ground based Li DAR demonstrates the legacy of management history to canopy structure and composition across a fragmented temperate woodland[J].Forest Ecology and Management,2015,335:255-260.
[18]Saeys W,Lenaerts B,Craessaerts G,et al.Estimation of the crop density of small grains using Li DAR sensors[J].Biosystems Engineering,2009,102(1):22-30.
[19]Li Xudong,Zhao Huijie,Liu Yang,et al.Laser scanning based three dimensional measurement of vegetation canopy structure[J].Optics and Lasers in Engineering,2014,54(1):152-158.
[20]Liu Shouyang,Baret Fred,Abichou Mariem,et al.Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model[J].Agricultural and Forest Meteorology,2017,247:12-20.
[21]Underwood James P,Hung Calvin,Whelan Brett,et al.Mapping almond orchard canopy volume,flowers,fruit and yield using lidar and vision sensors[J].Computers and Electronics in Agriculture,2016,130:83-96.
[22]Narvaez F J Y,Pedregal J S D,Prieto P A,et al.LiDAR and thermal images fusion for ground-based 3D characterization of fruit trees[J].Biosystems Engineering,2016,151:479-494.
[23]Weiss Ulrich,Biber Peter.Plant detection and mapping for agricultural robots using a 3D LIDAR sensor[J].Robotics and Autonomous Systems,2011,59(5):265-273.
[24]Shafiekhani A,Kadam S,Fritschi F B,et al.Vinobot and vinoculer:two robotic platforms for high-throughput field phenotyping[J].Sensors,2017,17(1):214.
[25]Deery D,Jimenez-Berni J,Jones H,et al.Proximal remote sensing buggies and potential applications for fieldbased phenotyping[J].Agronomy,2014,4(3):349-379.
[26]Virlet N,Sabermanesh K,Sadeghi-Tehran P,et al.Field scanalyzer:An automated robotic field phenotyping platform for detailed crop monitoring[J].Functional Plant Biology,2016,44(1):143-153.
[27]Liebisch F,Kirchgessner N,Schneider D,et al.Remote,aerial phenotyping of maize traits with a mobile multisensor approach[J].Plant Methods,2015,11(1):9.
[28]Friedli M,Kirchgessner N,Grieder C,et al.Terrestrial 3Dlaser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions[J].Plant Methods,2016,12(1):9.
[29]Holman F H,Riche A B,Michalski A,et al.High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing[J].Remote Sensing,2016,8(12):1031.
[30]Nadia Shakoor,Scott Lee,Todd C Mockler.High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field[J].Current Opinion in Plant Biology,2017,38(8):184-192.
[2]牛庆林,冯海宽,杨贵军,等.基于无人机数码影像的玉米育种材料株高和LAI监测[J].农业工程学报,2018,34(5):73-82.Niu Qinglin,Feng Haikuan,Yang Guijun,et al.Monitoring plant height and leaf area index of maize breeding material based on UAV digital images[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(5):73-82.(in Chinese with English abstract)
[3]Lati Ran Nisim,Filin Sagi,Eizenberg Hanan.Estimating plant growth parameters using an energy minimization-based stereovision model[J].Computers and Electronics in Agriculture,2013,98(7):260-271.
[4]Sylvain Jay,Gilles Rabatel,Xavier Hadoux,et al.In-field crop row phenotyping from 3D modeling performed using structure from motion[J].Computers and Electronics in Agriculture,2015,110:70-77.
[5]胡鹏程,郭焱,李保国,等.基于多视角立体视觉的植株三维重建与精度评估[J].农业工程学报,2015,31(11):209-214.Hu Pengcheng,Guo Yan,Li Baoguo,et al.Three-dimensional reconstruction and its precision evaluation of plant architecture based on multiple view stereo method[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(11):209-214.(in Chinese with English abstract)
[6]Alexandre Escola,Santiago Planas,Joan Ramon Rosell,et al.Performance of an ultrasonic ranging sensor in apple tree canopies[J].Sensors,2011,11(3):2459-2477.
[7]Hossein Maghsoudi,Saeid Minaei,Barat Ghobadian,et al.Ultrasonic sensing of pistachio canopy for low-volume precision spraying[J].Computers and Electronics in Agriculture,2015,112:149-160.
[8]Zaman Q U,Schumann A W,Hostler H K.Estimation of citrus fruit yield using ultrasonically-sensed tree size[J].Applied Engineering in Agriculture,2006,22(1):39-44.
[9]Kimes D S,Idso S B,Pinter P J J,et al.View angle effects in the radiometric measurement of plant canopy temperatures[J].Remote Sensing of Environment,1980,10(4):273-284.
[10]Luvall J C,Lieberman D,Lieberman M,et al.Estimation of tropical forest canopy temperatures,thermal response numbers,and evapotranspiration using an aircraft-based thermal sensor[J].Photogrammetric Engineering&Remote Sensing,1990,56(10):1393-1401.
[11]Jones H G,Serraj R,Loveys B R,et al.Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field[J].Functional Plant Biology,2009,36(10/11):978-989.
[12]Wang Mingfei,Dong Daming,Zheng Wengang,et al.Using infrared sensor for large area canopy total temperature measurements of rice plants[J].Applied Engineering in Agriculture,2013,29(1):115-122.
[13]Mahendra Bhandari.Use of Infrared Thermal Imaging for Estimating Canopy Temperature in Wheat and Maize[D].Canyon:West Texas A&M University,2016.
[14]刘建刚,赵春江,杨贵军,等.无人机遥感解析田间作物表型信息研究进展[J].农业工程学报,2016,32(24):98-106.Liu Jiangang,Zhao Chunjiang,Yang Guijun,et al.Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(24):98-106.(in Chinese with English abstract)
[15]Dimitry Van der Zande,Wouter Hoet,Inge Jonckheere,et al.Influence of measurement set-up of ground-based LiDAR for derivation of tree structure[J].Agricultural and Forest Meteorology,2006,141(2/3/4):147-160.
[16]Cote Jean-Francois,Fournier Richard A,Frazer Gordon W,et al.A fine-scale architectural model of trees to enhance LiDAR-derived measurements of forest canopy structure[J].Agricultural and Forest Meteorology,2012,166-167(1):72-85.
[17]McMahon Sean M,Bebber Daniel P,Butt Nathalie,et al.Ground based Li DAR demonstrates the legacy of management history to canopy structure and composition across a fragmented temperate woodland[J].Forest Ecology and Management,2015,335:255-260.
[18]Saeys W,Lenaerts B,Craessaerts G,et al.Estimation of the crop density of small grains using Li DAR sensors[J].Biosystems Engineering,2009,102(1):22-30.
[19]Li Xudong,Zhao Huijie,Liu Yang,et al.Laser scanning based three dimensional measurement of vegetation canopy structure[J].Optics and Lasers in Engineering,2014,54(1):152-158.
[20]Liu Shouyang,Baret Fred,Abichou Mariem,et al.Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model[J].Agricultural and Forest Meteorology,2017,247:12-20.
[21]Underwood James P,Hung Calvin,Whelan Brett,et al.Mapping almond orchard canopy volume,flowers,fruit and yield using lidar and vision sensors[J].Computers and Electronics in Agriculture,2016,130:83-96.
[22]Narvaez F J Y,Pedregal J S D,Prieto P A,et al.LiDAR and thermal images fusion for ground-based 3D characterization of fruit trees[J].Biosystems Engineering,2016,151:479-494.
[23]Weiss Ulrich,Biber Peter.Plant detection and mapping for agricultural robots using a 3D LIDAR sensor[J].Robotics and Autonomous Systems,2011,59(5):265-273.
[24]Shafiekhani A,Kadam S,Fritschi F B,et al.Vinobot and vinoculer:two robotic platforms for high-throughput field phenotyping[J].Sensors,2017,17(1):214.
[25]Deery D,Jimenez-Berni J,Jones H,et al.Proximal remote sensing buggies and potential applications for fieldbased phenotyping[J].Agronomy,2014,4(3):349-379.
[26]Virlet N,Sabermanesh K,Sadeghi-Tehran P,et al.Field scanalyzer:An automated robotic field phenotyping platform for detailed crop monitoring[J].Functional Plant Biology,2016,44(1):143-153.
[27]Liebisch F,Kirchgessner N,Schneider D,et al.Remote,aerial phenotyping of maize traits with a mobile multisensor approach[J].Plant Methods,2015,11(1):9.
[28]Friedli M,Kirchgessner N,Grieder C,et al.Terrestrial 3Dlaser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions[J].Plant Methods,2016,12(1):9.
[29]Holman F H,Riche A B,Michalski A,et al.High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing[J].Remote Sensing,2016,8(12):1031.
[30]Nadia Shakoor,Scott Lee,Todd C Mockler.High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field[J].Current Opinion in Plant Biology,2017,38(8):184-192.