摘要
为实现田间玉米茎粗的快速测量,提出了一种基于RGB-D(RGB-Depth)相机的玉米茎粗参数提取方法。以小喇叭口期玉米为观测对象,利用RGB-D相机获取田间玉米的彩色图像和深度图像。首先,根据玉米与背景的颜色差异,对图像进行自动阈值分割,提取图像中感兴趣区域内的信息;利用形态学"开"操作剔除图像中的噪声,得到玉米茎杆的主干。其次,对茎杆主干进行骨架化操作,检测骨架的交叉点和末端点,确定茎杆的待测量部位。然后,对该部位的点云数据进行去噪、聚类、椭圆拟合操作,得到椭圆的长轴和短轴,获得玉米的茎粗。对20株玉米进行测试,结果表明:茎粗长轴的平均测量误差为3.31 mm,标准差为3.01 mm,平均测量相对误差为10.27%,茎粗短轴的平均测量误差为3.33 mm,标准差为2.39 mm,平均测量相对误差为12.71%。该研究可为作物表型参数的快速获取提供参考。
Stem diameters of maize are important phenotype parameters and can characterize the crop growth and lodging resistance, drawing more attentions from breeders. Traditional measurement about stem diameters is usually manual measurement, which is timeconsuming, laborious, and subject to human error. In order to rapidly measure stem diameters of maize in field, a method based on RGB-D(red, green, blue-depth) camera was proposed in this paper to extract stem diameters of maize. The color images and depth images of the maize plants at the small bell stage were captured by a RGB-D camera in field. First, maize stem was extracted by processing the color image. It was hard to recognize maize just according to the color differences in red, green and blue component between maize and background due to the illumination variations. To solve the problem, the component that represented the difference between green signals and illumination brightness was calculated and applied to segment maize with Otsu algorithm, and the binary image of maize was generated. And then erosion operation was conducted within region of interest to cut off the connection between little leaves and maize stem, and small regions were eliminated to remove weed and little leaves. The largest region of maize was saved after dilation operation. After that, skeletonization was conducted for main stem. There were crossing points at the points of contact between leaves and stem, and ending points at the points of contact between ground and stem, and the potential measurement region of stem could be identified by searching crossing points and ending points. The color coordinates of the potential measurement region were saved and corresponding point cloud data were generated based on the mapping relationship between color coordinate, depth coordinate and camera coordinate. Second, stem diameters were calculated by processing point cloud data. Noise points affected measurement accuracy of stem diameters, and K-nearest method was applied to remove scattered points
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2017年第S1期170-176,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
北京市科技计划资助项目"作物精确化育种性状采集智能装备的研发与应用"(D151100004215002)
关键词
测量
图像识别
农作物
作物表型
RGB-D相机
茎粗
点云拟合
measurements
image recognition
crops
plant phenotyping
RGB-D camera
stem diameter
point cloud fitting
