Department of Electrical Engineering, Mokpo National University, Muan 58554, Jeonnam, Republic of Korea.
Department of Electrical and Control Engineering, Mokpo National University, Muan 58554, Jeonnam, Republic of Korea.
Sensors (Basel). 2023 Apr 17;23(8):4040. doi: 10.3390/s23084040.
Automation in agriculture can save labor and raise productivity. Our research aims to have robots prune sweet pepper plants automatically in smart farms. In previous research, we studied detecting plant parts by a semantic segmentation neural network. Additionally, in this research, we detect the pruning points of leaves in 3D space by using 3D point clouds. Robot arms can move to these positions and cut the leaves. We proposed a method to create 3D point clouds of sweet peppers by applying semantic segmentation neural networks, the ICP algorithm, and ORB-SLAM3, a visual SLAM application with a LiDAR camera. This 3D point cloud consists of plant parts that have been recognized by the neural network. We also present a method to detect the leaf pruning points in 2D images and 3D space by using 3D point clouds. Furthermore, the PCL library was used to visualize the 3D point clouds and the pruning points. Many experiments are conducted to show the method's stability and correctness.
农业自动化可以节省劳动力并提高生产力。我们的研究旨在让机器人在智能农场中自动修剪甜椒植株。在之前的研究中，我们研究了通过语义分割神经网络检测植物部分。此外，在这项研究中，我们使用 3D 点云检测叶片在 3D 空间中的修剪点。机器人手臂可以移动到这些位置并修剪叶片。我们提出了一种通过应用语义分割神经网络、ICP 算法和带有 LiDAR 相机的视觉 SLAM 应用 ORB-SLAM3 来创建甜椒 3D 点云的方法。这个 3D 点云由神经网络识别的植物部分组成。我们还提出了一种通过使用 3D 点云在 2D 图像和 3D 空间中检测叶片修剪点的方法。此外，还使用了 PCL 库来可视化 3D 点云和修剪点。进行了许多实验以展示该方法的稳定性和正确性。
