Real-Time Estimation of Citrus Canopy Volume Based on Laser Scanner and Irregular Triangular Prism Module Method
LI Peng1, ZHANG Ming1,2, DAI XiangSheng3, WANG Teng1, ZHENG YongQiang1, YI ShiLai1, Lü Qiang,11 Citrus Research Institute, Southwest University, Chongqing 400712 2 College of Engineering and Technology, Southwest University, Chongqing 400716 3 Jinggang Honey Pomelo Research Institute, Jinggangshan Agricultural Science and Technology Park, Ji’an 343016, Jiangxi
Abstract 【Objective】Accurate measurement of volume and structure of fruit tree canopy can provide important reference for variable application of pesticide and fertilizer, as well as yield estimation. In order to accurately measure the canopy volume, a scanning platform based on laser sensor (LMS111-10100, SICK) was built. Aiming at the problem of irregular canopy shape, the poor accuracy of the existing real-time measurement methods of canopy volume and difficult to measure and estimate the canopy volume, a new estimation method based on irregular triangular prism modules was proposed in this work. 【Method】Five spherical landscape trees with regular canopy and ten citrus trees with irregular canopy were scanned by the laser sensor at the speeds of 0.5, 1.0 and 1.5 m·s -1, respectively. The canopy volume was measured by two methods: cuboid module method (CMM) and irregular triangular prism module method (ITPMM), and the error analysis was conducted based on manual measurement. 【Result】 The results showed that the error ranges of CMM for measuring landscape trees at the different speeds of 0.5, 1.0 and 1.5 m·s -1were 4.17%-6.59%, 4.56%-7.42% and 4.17%-9.86%, respectively, while the error ranges of the ITPMM for measuring landscape trees were 2.37%-4.63%, 3.18%-5.00% and 4.10%-5.73%, respectively. The distance range of the relative error of the two methods for measuring citrus trees was -0.28%-4.22%%, and the average difference was 1.78%. The error ranges of CMM for measuring citrus trees at the different speeds of 0.5, 1.0 and 1.5 m·s -1 were 11.63%-31.02%, 11.88%-33.23% and 13.28%-33.30%, respectively. The error ranges by ITPMM for measuring citrus trees were 3.25%-6.69%, 4.50%-8.31% and 5.66%-11.55%, respectively. The distance range of the relative error of the two methods for measuring citrus trees was 6.43%-26.20%, and the average difference was 13.04%. 【Conclusion】 The research showed that the estimation error of the ITPMM was significantly smaller than the CMM. For the same target, when the speed was 0.5 m·s -1, both of the estimation accuracy for the two methods were the highest. As the sensor speed increased, laser scanning points on the canopy decreased. So, the relative error of volume estimation increased with increase of advance speed of the laser sensor. When scanning the regular target, the accuracy difference between the two methods was small; when scanning the irregular target, the error of the CMM was larger. The processing time of a frame laser data by the CMM was 2.86 ms, and the processing time by the ITPMM was 4.73 ms, which were less than the scanning period of 20 ms of the laser sensor. The data processing time could match the acquirement of real-time collection and processing of laser data. Keywords:canopy volume;laser scanning;irregular triangular prism module method;real-time detection;trunk recognition
PDF (2921KB)元数据多维度评价相关文章导出EndNote|Ris|Bibtex收藏本文 本文引用格式 李鹏, 张明, 戴祥生, 王腾, 郑永强, 易时来, 吕强. 基于不规则三棱柱分割法实时测算果树冠层体积[J]. 中国农业科学, 2019, 52(24): 4493-4504 doi:10.3864/j.issn.0578-1752.2019.24.005 LI Peng, ZHANG Ming, DAI XiangSheng, WANG Teng, ZHENG YongQiang, YI ShiLai, Lü Qiang. Real-Time Estimation of Citrus Canopy Volume Based on Laser Scanner and Irregular Triangular Prism Module Method[J]. Scientia Acricultura Sinica, 2019, 52(24): 4493-4504 doi:10.3864/j.issn.0578-1752.2019.24.005
(a)景观树;(b)柑橘树;(c)景观树激光点云图;(d)柑橘树激光点云图;(e)景观树深度图;(f)柑橘树深度图 Fig. 7Color images, laser point cloud images and depth images of landscape tree and citrus tree
(a) Landscape tree; (b) Citrus tree; (c) Laser point cloud image of landscape tree; (d) Laser point cloud image of citrus tree; (e) Depth image of landscape tree; (f) Depth image of citrus tree
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