Accuracy assessment of a mobile terrestrial laser scanner for tree crops F. H. S. Karp † , A. F. Colaço, R. G. Trevisan and J. P. Molin 1 Biosystems Engineering Department, University of São Paulo. Av. Pádua Dias 11, 13418-900, Piracicaba, São Paulo, Brazil LiDAR technology is one option to collect spatial data about canopy geometry in many crops. However, the method of data acquisition includes many errors related to the LiDAR sensor, the GNSS receiver and the data acquisition set up. Therefore, the objective of this study was to evaluate the errors involved in the data acquisition from a mobile terrestrial laser scanner (MTLS). Regular shaped objects were scanned with a developed MTLS in two different tests: i) with the system mounted on a vehicle and ii) with the system mounted on a platform running over a rail. The errors of area estimation varied between 0.001 and 0.071 m 2 for the circle, square and triangle objects. The errors on volume estimations were between 0.0003 and 0.0017 m 3 , for cylinders and truncated cone. Keywords: LiDAR, error analysis, regular shaped objects Introduction LiDAR technology (Light Detection and Ranging) is one option to collect data about the canopy geometry of plants in tree crops. LiDAR sensors are based on the emission and reception of a laser beam in a specific direction. The sensor calculates the distance to the nearest obstacle by the time difference between the emission and reception of this laser beam. Using appropriate software, the laser beam impacts can be seen in the format of a point cloud, from which geometric parameters can be retrieved (Escolà et al., 2016; del-Moral-Martínez et al., 2016; Auat Cheein et al., 2015). The geometric information obtained by a mobile terrestrial laser scanner (MTLS) is important for the management of tree crops. Canopy volume, height, or leaf area (LA) can be used to estimate canopy growth and predict the input necessity of the plants. Some researchers demonstrated the use of sensor- based measurements of canopy volume, height and LA in variable rate applications. Escolà et al. (2013) developed a LiDAR-based prototype sprayer varying doses according to the tree volume. Chen et al. (2012) also developed a LiDAR- based prototype sprayer. The authors used the tree height, width and foliage density to guide variable rate applications. Moreover, several studies developed LiDAR systems in order to estimate geometric parameters in different tree crops, such as apple (Walklate et al., 2002; Walklate et al., 2003), citrus (Tumbo et al., 2002; Lee & Ehsani, 2009), vineyard (Arnó et al., 2013; Rinaldi et al., 2013; Llorens et al., 2011), olives (Moorthy et al., 2011; Miranda-Fluentes et al., 2015; Escolà et al., 2015) and others. However, the proposed methods for data acquisition may include many errors related to the GNSS, the LiDAR sensor and the data acquisition system. The data acquisition set up may cause the rotation of the sensor along its three axes (pitch, yaw and roll) generating errors in the final position of the point cloud. Some author pointed the importance of using IMU (inertial measurement unit) during data acquisi- tion in order to allow the correction of the LiDAR sensor position (del-Moral-Martínez et al., 2016). According to Pallejà et al. (2010), the collection of LiDAR sensor data is affected by the vehicle speed, difference in the height of the sensor, variation in the distance relative to the tree row and in the orientation angle of the LiDAR sensor. Also, the mixed pixels phenomenon is an important source of uncertainty (Sanz et al., 2011). Georreferencing the position of the sensor eliminates some of these errors (del-Moral-Martínez et al., 2016) except the errors caused by the orientation angle of LiDAR sensor and mixed pixels phenomenon, among others. Given the potential of LiDAR sensors in agriculture, it is crucial to understand the errors involved in the data acquisition. Therefore, the aim of this study was to evaluate the errors in the representation of targets by point clouds obtained by a MTLS. Materials and Methods To evaluate the accuracy of the data generated by MTLS, a LMS200 terrestrial 2D laser scanner (Sick, Waldkirch, † E-mail: felippe.karp@usp.br Advances in Animal Biosciences: Precision Agriculture (ECPA) 2017, (2017), 8:2, pp 178–182 © The Animal Consortium 2017 doi:10.1017/S2040470017000073 advances in animal biosciences 178