Automated Seedling Height Assessment for Tree Nurseries Using Point Cloud Processing

This paper presents a prototype of an automated seedling height assessment system for tree nurseries. The proposed system can acquire and store real-time 3D point-cloud data of seedlings; and perform offline identification, measurement, and report generation of seedling heights with an overall system accuracy that meets a 5mm accuracy specification. Periodic growth information of seedlings allows quantifying effects of different factors on the overall seedling development process for research and production optimization purposes. However, current manual sampling approaches used at these facilities produce quite limited data samples, and the process is rather time-consuming and labor intensive for industrial scale operations. In contrast, the proposed system is capable of significantly increasing the measurement sample size, measurement resolution, and frequency of measurement by automating the seedling measurement process using a scanning laser profilometer and an application specific point-cloud processing algorithm. The performance of the proposed profilometry solution for point-cloud generation is compared with several other point-cloud generation methods such as a 3D structured light sensing, light intensity detection and ranging (LiDAR), stereovision, and photogrammetry. This comparison results demonstrate a superior performance of the laser-profilometer over other sensing solutions available for seedling height measurement. The proposed system is experimentally validated for its measurement accuracy and repeatability. The field-test of the measurement system was conducted at Centre for Agriculture and Forestry Development, Wooddale, Newfoundland and Labrador (NL), Canada, and the results demonstrate the practical applicability and technological readiness of the proposed system for field deployment.