【Objective】 To address the problem of online detection of street-tree crowns, this study proposes a pointwise detection method for the point cloud of street-tree crowns based on mobile laser scanning (MLS), and generates a high-performance tree-crown detector capable of online, fast and accurate detection of the point cloud of street-tree crowns to provide basic data for street-tree-targeted spraying. 【Method】 The MLS system equipped with a 2D LiDAR (light detection and ranging) was used to collect the street contour measurement data in real time, and five attributes such as three-dimensional coordinates, primary echo intensity and number of echoes were extracted; a δ spherical neighborhood search method was built to realize online and fast query of point cloud neighborhood; seven kinds of local features of point cloud such as width, depth, height, dimensionality, density, number of echoes and echo intensity were extracted from the spherical domain of the point to be identified; the supervised learning algorithm was used to fuse the local features of point cloud and train a crown detector to predict the category of the point to be identified. 【Result】 The point cloud data of a 137 m long street were collected, and four comparative experiments of neighborhood search methods, supervised learning algorithms, local features of point clouds, and pointwise crown detectors were conducted. The experimental results show that the query time of the proposed δ spherical neighborhood search method is 10.90% of that of k-D tree; among the four supervised learning algorithms of neural network (NN), support vector machine (SVM), Boosting and random forest (RF), the classification performance of the crown detector trained by RF is the best; compared with single kind of features, combined features have better generalization performance; the pointwise crown detector designed by this paper is obviously superior to the existing method in terms of detection accuracy and efficiency. When the radius δ changes within the range of 0.1-1.0 m, the F₁ score on the test set is ≥ 97.74%. 【Conclusion】 The proposed method can quickly and accurately detect street-tree crown point clouds from MLS point-cloud data collected in real time and provide a spray prescription map for targeted spraying applications on street trees.