WHEAT EAR DETECTION IN PLOTS BY SEGMENTING MOBILE LASER SCANNER DATA
| Autor: | S.J. Oude Elberink, Michael Ying Yang, K. Velumani, F. Baret |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
lcsh:Applied optics. Photonics 0211 other engineering and technologies Point cloud 02 engineering and technology computer.software_genre 01 natural sciences lcsh:Technology Voxel Statistics Segmentation Computer vision Mean-shift 021101 geological & geomatics engineering Mathematics Counting process business.industry lcsh:T lcsh:TA1501-1820 Ranging Lidar lcsh:TA1-2040 Stage (hydrology) Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) computer 010606 plant biology & botany |
| Zdroj: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol IV-2-W4, Pp 149-156 (2017) |
| ISSN: | 2194-9050 2194-9042 |
| Popis: | The use of Light Detection and Ranging (LiDAR) to study agricultural crop traits is becoming popular. Wheat plant traits such as crop height, biomass fractions and plant population are of interest to agronomists and biologists for the assessment of a genotype's performance in the environment. Among these performance indicators, plant population in the field is still widely estimated through manual counting which is a tedious and labour intensive task. The goal of this study is to explore the suitability of LiDAR observations to automate the counting process by the individual detection of wheat ears in the agricultural field. However, this is a challenging task owing to the random cropping pattern and noisy returns present in the point cloud. The goal is achieved by first segmenting the 3D point cloud followed by the classification of segments into ears and non-ears. In this study, two segmentation techniques: a) voxel-based segmentation and b) mean shift segmentation were adapted to suit the segmentation of plant point clouds. An ear classification strategy was developed to distinguish the ear segments from leaves and stems. Finally, the ears extracted by the automatic methods were compared with reference ear segments prepared by manual segmentation. Both the methods had an average detection rate of 85 %, aggregated over different flowering stages. The voxel-based approach performed well for late flowering stages (wheat crops aged 210 days or more) with a mean percentage accuracy of 94 % and takes less than 20 seconds to process 50,000 points with an average point density of 16 points/cm2. Meanwhile, the mean shift approach showed comparatively better counting accuracy of 95% for early flowering stage (crops aged below 225 days) and takes approximately 4 minutes to process 50,000 points. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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