Vision-based planting position selection system for an unmanned reforestation machine

Abstract

Research on automated seedling planting systems in forestry is a crucial aspect of forestry automation. This paper introduces the development of a vision-based automated seedling planting position selection system, integrated with hardware and software components on an unmanned forest machine platform. Developed around object detection as the core, this research presents a comprehensive system consisting of two main functionalities: (i) A vision system that performs obstacle detection and localization, providing estimated obstacle types, sizes, and positions to the plant planner function. (ii) A plant planner function utilizes this information to plan the plantable areas and selects suitable planting locations. The integrated system has been tested in the field and we found it to effectively determine suitable planting locations on the ground of a clear-cut. The implementation of this system lays the foundation for subsequent automated planting operations. Furthermore, the automation of forest seedling planting reduces the need for manual labor and enhances planting precision, contributing to improved forest health and ecological balance. Looking ahead, this research offers insights into the future development of unmanned forestry operations, making strides in automating forest management, achieving cost-effectiveness, and facilitating ecological restoration.