Abstract: This study aims to enhance the accuracy, real-time performance, and practicality of real-time target detection in spray robots in forest and fruit orchards. A novel approach was introduced using a lightweight PointNet deep learning network. Efficient detection of target was realized during spray under complex natural environments. Several key steps were consisted as follows. Firstly, the target was extracted from the original point cloud. Preprocessing techniques included the region extraction downsampling, ground segmentation, and an improved density-based spatial clustering of application with noise. The target points were isolated to facilitate the subsequent recognition. After extraction, the target point cloud was transformed into the format suitable for the input into the point cloud recognition network. The moving least squares upsampling was carried out to collect the appropriately prepared data. The recognition network of point cloud was constructed to specifically designed for the forest and fruit tree environments. The residual module was introduced to enhance the cyclic pruning algorithm into the PointNet network. The representation power of the network was improved to capture more complex patterns and features of target objects. The cyclic pruning algorithm was used to refine the network architecture, and then remove unnecessary connections, in order to reduce the computational overhead with high performance. The experimental results demonstrate that an impressive accuracy of 89.7% was achieved in the widely-used ModelNet40 dataset. The accuracy of target recognition reached 92.49% in actual nursery environments. Moreover, the misidentification and rejection rates were reduced to 13.4% and 6.47%, respectively. The accuracy of the improved PointNet was outperformed by 4.38 percentage points, compared with the original. While the misidentification and rejection rates were reduced significantly. Comparative analysis revealed that the recognition accuracy of the improved model was only 1.14 percentage points lower than that of PointNet++. Additionally, the misidentification rate and false rejection rate were slightly higher by 0.9 and 1.12 percentage points, respectively. Nevertheless, the number of parameters and computational operations were reduced remarkably in the lightweight PointNet model, only 11.5% of PointNet and 27.02% of PointNet++. The operations were reduced by 13.3 and 76.79 percentage points, respectively. The efficiency and response performance were promoted to reduce the computational complexity suitable for real-time applications. In conclusion, the improved lightweight PointNet deep learning network can offer the high real-time performance, accuracy, and robustness, which was well-suited for the target recognition in spraying operations within forest and fruit orchard environments. These findings can provided the strong reference to advance the field of target detection and recognition in spray operations.