Development of synthetic visual datasets to enhance weed and crop perception in agricultural robots
This project is developed within the framework of Earth Rover, a company focused on designing advanced agricultural robots. Specifically, it aims at the development of a robot capable of eliminating weeds by concentrating energy pulses to boil the stem cells located in the meristem of the plants. Th...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/442576 |
| Acceso en línea: | https://hdl.handle.net/2117/442576 |
| Access Level: | acceso abierto |
| Palabra clave: | Robotics Artificial intelligence Computer vision Robòtica Intel·ligència artificial Visió per ordinador Àrees temàtiques de la UPC::Enginyeria agroalimentària |
| Sumario: | This project is developed within the framework of Earth Rover, a company focused on designing advanced agricultural robots. Specifically, it aims at the development of a robot capable of eliminating weeds by concentrating energy pulses to boil the stem cells located in the meristem of the plants. The main objective is to intervene at the earliest growth stage, when weeds are typically small dicotyledons, ensuring a more efficient and precise elimination. Manual data annotation for training computer vision models is complex, costly, and often imprecise, especially when weeds form dense clusters, making precise human annotation nearly impossible. In this context, the use of synthetic datasets emerges as a powerful tool that not only assists in annotating complex scenes but also introduces domain randomization that is difficult to achieve with real-world images (e.g., variations in lighting conditions, terrain types, etc.). This work develops a system that strives to realistically replicate agricultural environments using Blender and its Geometry Nodes system for the procedural generation of crops and weeds. It enables the automatic creation of synthetic images along with segmentation masks, depth maps, and keypoint annotations (focused on the meristem). Synthetic datasets are increasingly established as a key resource to exponentially accelerate the development of computer vision systems and machine learning models in agricultural robotics. |
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