摘要
为了实现大田大豆单株植株几何参数(高度、体积)准确获取,该文构建了基于机载激光雷达(LiDAR)的农作物表型探测系统,并开展了大田标定和探测试验。针对大田大豆垄上种植模式下地面平整度差异大、植株枝叶交接难以区分的问题,提出了一种LiDAR表型探测系统下的基于局部邻域特征分割与均值漂移算法的提取方法。在获取的点云中,首先使用基于局部邻域特征的语义分割方法提取一垄植株行,然后采用均值漂移算法提取单株植株,最后进行植株表面重建和植株几何参数统计。LiDAR表型探测系统在沿探测系统前进运动方向、垂直运动方向、垂直地面方向最大误差分别为0.58%(5.8 cm)、−1.75%(−7.0 cm)、−1.74%(−3.4 cm)。该文采用的基于局部邻域特征的分割方法,植株与地面分类的效果良好,人工统计植株数量相比,检测植株数量的平均相对误差为11.83%。相对于常用的RANSAC(random sample consensus)方法,使用该文提出的高度计算方法,大豆植株高度平均相对误差从9.05%下降到5.14%,利用alpha-shape算法重建后的冠层体积平均值为48.5 dm3。该文工作可为作物植株分割和体积统计提供借鉴。
Accurate acquisition and analysis of crop geometric information is an important basis for the implementation of precision agriculture.Canopy height and volume are important decision parameters for variable sprayer application rate.In the field environment,the large change of ambient light has an important influence on the measurement of canopy geometry information by sensors.At the same time,there are few researches on the problems of remove the effect of ground roughness and difficulty in distinguishing individual plants due to branches and leaves crossing under the ridge planting mode of field soybean.Therefore,it is necessary to design an information acquisition system that is less affected by light conditions and an algorithm to improve the ability to extract geometric information from individual crops.In this study,a crop phenotype detection system based on airborne lidar was constructed and its accuracy was verified.A method of extracting individual plant based on local geometric feature segmentation and mean shift algorithm was proposed.In the process of soybean plant and ground classification,firstly,the local geometric features constructed in the optimal neighborhood are classified into 2D and 3D local shape features according to their dimensions.Secondly,in order to select 5 feature combinations that are strongly related to classification,all features were evaluated using Gini index algorithm,Chi-square algorithm,ReliefF algorithm,and random forest method.Finally,according to different feature combinations,a random forest classifier is selected to predict the test set data.In the process of extracting a single soybean plant,the point cloud data of different plants were used to obtain the point cloud data of a single plant using the mean shift algorithm to complete the extraction of a single soybean plant.In the process of obtaining geometric information of single plants,the height of plants was defined as the height difference from the intersection point of soybean stem and ground to the highest point o
作者
管贤平
刘宽
邱白晶
董晓娅
薛新宇
Guan Xianping;Liu Kuan;Qiu Baijing;Dong Xiaoya;Xue Xinyu(Key Laboratory of Crop Protection Engineering,Ministry of Agriculture and Rural Affairs,Jiangsu University,Zhenjiang 212013,China;Nanjing Research Institute for Agricultural Mechanization,Ministry of Agriculture and Rural Affairs,Nanjing 210014,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2019年第23期96-103,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划项目课题(2017YFD0701403)
关键词
激光
提取
数据处理
单株点云
几何参数
laser
extraction
data processing
point cloud of single crop
geometric parameters
