Agroindustry
Research and Development under Contract
Computer Vision, Interaction and Graphics
Artificial intelligence, machine learning, and decision support systems
Computer vision and image processing
FRAMEWORK
The APC-System project aims to revolutionise the in vitro plant propagation process through the integration of advanced robotics and digital automation technologies.
This collaborative initiative aims to increase the efficiency and sustainability of the in vitro plant propagation process, making it economically viable in the long term. Its primary focus is to optimise the propagation of species such as blueberry and olive trees by using an Artificial Intelligence algorithm to accurately identify cutting nodes, together with a collaborative robotic system to automate repetitive, high-precision tasks, thereby reducing manual workload and operational costs.
PROPOSED SOLUTION
The solution combines a collaborative robotic system (cobot) with advanced plant structure segmentation and recognition algorithms, enabling highly precise and repeatable cutting operations.
- Develop an automated in vitro plant propagation process, demonstrated in a relevant environment, to increase plant production capacity;
- Develop Computer Vision and plant analysis algorithms for plant structures segmentation, attachment points detection, and cutting points identification on stems and leaves of blueberry and olive species;
- Implement a robotic solution to support in vitro propagation, capable of significantly reducing the propagation time for two crop species by the end of the project.
CCG/ZGDV CONTRIBUTION
CCG/ZGDV is responsible for the development of the project's Computer Vision and Artificial Intelligence components, contributing to the technological architecture and technical requirements definition required to automate the in vitro plant micropropagation process.
The main contributions of CCG/ZGDV include:
- Developing Computer Vision algorithms for the detection, segmentation and analysis of plant species;
- Creating Artificial Intelligence models for the automatic identification of cutting points and decision support for the collaborative robotic system;
- Integrating image analysis solutions with the collaborative robotics platform, ensuring real-time communication and data processing.
Through this work, CCG/ZGDV advances the application of Artificial Intelligence and Computer Vision technologies in the agri-biotechnology sector, contributing to greater efficiency, precision and sustainability in plant propagation processes.