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Efficient and environmentally responsible tree planting is crucial to sustainable land management. Tree planting processes involve significant machinery and labor, impacting efficiency and ecosystem health. In response, Södra Skogsägarna introduced the BraSatt initiative to develop an autonomous planting vehicle called E-Beaver. This vehicle aims to simultaneously address efficiency and ecological concerns by autonomously planting saplings in clear-felled areas. BIT ADDICT, partnering with Södra Skogsägarna, is re- sponsible for developing the control system for E-Beaver’s autonomous navigation and perception. 

In this thesis work, we examine the possibility of using the computer vision models YOLO and U-Net for detecting and segmenting newly planted saplings in a clear felled area. We also compare the models’ performances with and without augmenting the dataset to see if that would yield better-performing models. RGB and RGB-D images were gath- ered with the ZED 2i stereo camera. Two different models are presented, one for detecting saplings in RGB images taken with a top-down perspective and the other for segmenting saplings trunks from RGB-D images taken with a side perspective. The purpose of this the- sis work is to be able to use the models for evaluating the plating of newly planted saplings so that autonomous tree planting can be done. 

The outcomes of this research showcase that YOLOv8s has great potential in detecting tree saplings from a top-down perspective and the YOLOv8s-seg models in segmenting sapling trunks. The YOLOv8s-seg models performed significantly better on segmenting the trunks compared to U-Net models. 

The research contributes insights into using computer vision for efficient and ecologi- cally sound tree planting practices, poised to reshape the future of sustainable land man- agement.