Static and Dynamic Performance Evaluation of a Solid-State LiDAR for 3D Object Detection in Greenhouse Spray Applications

Abstract

Highlights Comprehensive evaluation of the measurement accuracy of an inexpensive solid-state LiDAR for object detection. Development of an algorithm to acquire point clouds of objects with various shapes under both static and dynamic conditions. Utilization of pseudo-color images to assess the surfaces of regular-shaped cartons and irregular artificial plants. Proposal for integrating the solid-state LiDAR into variable-rate spray applications for greenhouses. Abstract. An effective variable-rate spraying system for greenhouses requires accurate canopy structure parameters of plants to ensure proper pesticide dosage adjustment. While conventional laser systems integrated into spray systems can provide precise point cloud data of plants, they still present a high expense. This study examines the performance of a recently introduced, cost-effective, and high-resolution solid-state LiDAR (Intel RealSense L515) in relation to its potential for greenhouse spray applications. Additionally, a specialized point cloud acquisition algorithm was developed for this solid-state LiDAR to obtain the geometrical parameters of objects. To assess the LiDAR sensor's suitability for greenhouse spray applications, the performance of the LiDAR sensor and the algorithm was evaluated using five different sized regular-shaped cartons and three artificial plants with complex geometry. Various factors were analyzed, such as the horizontal distances between objects and the LiDAR sensor (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m), the tilt angle of the LiDAR sensor relative to the ground (45°, 60°, and 75°), the height of the LiDAR sensor from the ground (ranging from 0.3 to 0.8 m with 0.5 m distance intervals), and the forward speed of the LiDAR sensor (0.1, 0.3, 0.6, and 0.9 m s-1). The findings revealed that the optimal detection distance for this LiDAR sensor is 1.0 m. Increasing or decreasing the detection distance of the object relative to the LiDAR sensor diminished the measurement accuracy. The accuracy of the derived geometrical variables was affected by the height and tilt angle of the LiDAR sensor. Nevertheless, the geometrical parameters obtained from the solid-state LiDAR showed a favorable correspondence with the results of manual measurements. The highest root mean square error (RMSE) and coefficient of variation (CV) for the overall test were 14.3 mm and 14.3% in the X (length) direction, 14.3 mm and 14.3% in the Y (width) direction, and 10.8 mm and 10.8% in the Z (height) direction, respectively. The contour Edge Similarity Score for objects measured using the solid-state LiDAR and images obtained with an RGB camera exceeded 0.90. These findings suggest that the proposed solid-state LiDAR and the specifically designed algorithm could be effectively adapted to acquire the geometrical parameters of objects and to develop precise variable-rate spraying systems for greenhouse applications. Keywords: Canopy structure measurements, Point cloud, Precision agriculture, Precision spray technology, Variable-rate spraying systems.