Laser Vision-Based Plant Geometries Computation in Greenhouses

Journal of Multimedia

关键词: Canopy Width;    Plant Height;    Leaf Area;    Leaf Length;    Plants Geometry;    3D Point Cloud;    Laser Vision;   

Others  :  1017233
DOI  :  10.4304/jmm.9.4.534-541

【 摘 要 】

Plant growth statuses are important parameters in the greenhouse environment control system. It is time-consumed and less accuracy that measuring the plant geometries manually in greenhouses. To find a portable method to measure the growth parameters of plants portably and automatically, a laser vision-based measurement system was developed in this paper, consisting of a camera and a laser sheet that scanned the plant vertically. All equipments were mounted on a metal shelf in size of 30cm*40cm*100cm. The 3D point cloud was obtained with the laser sheet scanning the plant vertically, while the camera videoing the laser lines which projected on the plant. The calibration was conducted by a two solid boards standing together in an angle of 90. The camera’s internal and external parameters were calibrated by Image toolbox in MatLab®. It is useful to take a reference image without laser light and to use difference images to obtain the laser line. Laser line centers were extracted by improved centroid method. Thus, we obtained the 3D point cloud structure of the sample plant. For leaf length measurement, iteration method for point clouds was used to extract the axis of the leaf point cloud set. Start point was selected at the end of the leaf point cloud set as the first point of the leaf axis. The points in a radian of certain distance around the start point were chosen as the subset. The centroid of the subset of points was calculated and taken as the next axis point. Iteration was continued until all points in the leaf point cloud set were selected. Leaf length was calculated by curve fitting on these axis points. In order to increase the accuracy of curve fitting, bi-directional start point selection was useful. For leaf area estimation, exponential regression model was used to describe the grown leaves for sampled plant (water spinach) in this paper. To evaluate the method in a sample of 18 water spinaches, planted in the greenhouse (length 16 meter and width 8 meter) on the roof of library building in Nanjing Agricultural University, the lengths of 200 leaves and were measured manually and plotted versus their automatically measured counterparts. The accuracy of leaf lengths is 95.39% respectively. For the leaf length measurement, the average error is less than 5mm which is in the boundary of error. A few laser measurement results take on larger errors, because that some leaves were shaded from the others which make an effect on the curve fitting. In additional, the results are affected by the over-wide laser line. Manual measurement gave result information an accuracy of millimeter level, while laser measurement improved the measurement precision. For the leaf area estimation, the accuracy of modeling trained by 200 sampled leaves, is 81.9% with 50 testing sampled leaves. For the plant growth properties, plant height and canopy width are obtained by point clouds reconstruction of plant. The test experiment proved that laser vision-based method could be used on plants geometry measurement and growth monitoring in greenhouses. This method will improve visualization and digitalization of plants in greenhouses, and make a progress on greenhouse environment control system

【 授权许可】

   
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