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Christian Rueda-Ayala

Mechatronics Engineer - AI & Data Science Enthusiast

Now, working on Momentum - DigitalWeeds! AI-powered tools for precise invasive weed management, reducing herbicides and boosting farm profitability. Leveraging drone and mobile imagery to revolutionize plant health. (More info click here)
"I'm a passionate problem-solver, leveraging innovative technology to overcome challenges. My driving force is continuous learning and building robust solutions."
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Experience

UCO Logo

PhD(c) in Agricultural Engineering

University of Córdoba

2024 – 2028

UPM Logo

MSc in Robotics & Automation

Polytechnic University of Madrid

2017 – 2018

UTE Logo

Bachelor in Mechatronics Engineering

Equinoccial Technological University

2011 – 2016

INIA Logo

Momentum's Program Researcher

INIA-CSIC

2024 – Present

PUCE Logo

Guest Lecturer - Data Science Master's

PUCE

2023 – Present

UIDE Logo

Lecturer - Mechatronics Engineering

International University of Ecuador

2023 – 2025

UTE Logo

Lecturer - Mechatronics Engineering

UTE University

2018 – 2023

NIBIO Logo

Research Collaborator

Norwegian Institute of Bioeconomy (NIBIO)

2019 – 2020

CAR Logo

Assistant Researcher

Automation and Robotics Center (CAR-CSIC)

2017 – 2018

Skills

AI & Data Science

7+ Years XP

Programming

5+ Years XP

GIS (Geographic Information Systems)

1+ Year XP

Engineering Skills

12+ Years XP

Soft Skills

5+ Years XP

Certifications

Projects

Featured Project: Momentum's - DigitalWeeds

🛠 In Progress
Screenshot 1

DigitalWeeds kick-off Meeting - We're on!

Screenshot 2

Momentum's DigitalWeeds project conceptualization

Screenshot 3

Amaranthus Palmeri seedling

Screenshot 3

Drone for DigitalWeeds - DJI Matrix 300 RTK

DigitalWeeds project aims to design, develop, and validate digital agriculture AI tools focused on detecting and mapping Amaranthus Palmeri. The ultimate goal is to support its control, improve management strategies, and reduce herbicide use in alignment with precision agriculture (PA) principles.

Overall progress: 60%

Planning
Data Collection
Model Training
Testing
Interface & Deployment
Maintainance
GitHub Position Offer

SWOT – DigitalWeeds Project

Strengths

  • Integration of cutting-edge technologies (remote sensing, deep learning, mobile apps).
  • Multidisciplinary team with expertise in precision agriculture, AI, and geomatics.
  • Clear ecological and sustainability objectives aligned with global goals.
  • Potential for real impact on herbicide reduction and biodiversity preservation.

Weaknesses

  • Dependence on high-quality labeled datasets for AI model training.
  • Limited scalability in early stages due to technological complexity.
  • Possible field data acquisition constraints.
  • Need for constant maintenance and updates.

Opportunities

  • Growing demand for sustainable agricultural solutions.
  • Scalability to other species or regions.
  • Potential for academic publications and open tools.
  • Interest from public or private sectors.

Threats

  • Rapid evolution of AI and sensor tech.
  • Regulatory or privacy constraints on data collection.
  • Resistance to digital adoption in traditional farming.
  • Funding or continuity risks beyond initial phase.

Other Projects

DIGinvasive

Clasificador de Plantas

DIGinvasive is an innovative digital agriculture web-based system designed to map and assess the emergence and spread of invasive weed species threatening crop production.

Technologies: TensorFlow, Keras, OpenCV, Python, ArcGIS, PostGIS

TensorFlow Python OpenCV PostgreSQL
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Strengths: Novel integration of geospatial and digital tools tailored to invasive weed monitoring. Open-access platform promoting collaborative data entry and regional-scale mapping. Scalable design applicable to other weed species and territories.
Weaknesses: Dependence on user input for high-resolution local data. Requires continuous data validation to maintain accuracy. Platform adoption may vary across regions and user profiles.
Opportunities: Growing institutional interest in biodiversity and invasive species control. Potential for national and EU-level environmental integration.
Threats: Data privacy or policy restrictions on georeferenced weed reporting. Technological barriers among end-users (training, device access). Risk of platform underuse without adequate outreach.

Progress: 95%

Webpage

AI for Data Science projects

AI for Data Science projects

AI-Data Science Projects developed for the PUCE's Data Science Master Programme

Technologies: Python, Pandas, Scikit-Learn

Python
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Strengths: Built using open-source technologies such as Python, Pandas, Scikit-learn, and TensorFlow. Aligned with real-world Data Science challenges and academic standards. Provides hands-on experience in machine learning and deep learning. Offers reproducible, transparent, and modular code.
Weaknesses: Requires users to have intermediate to advanced technical knowledge. Limited documentation for some projects may hinder onboarding. Variability in code quality across contributions. Not optimized for production environments.
Opportunities: Potential to expand into a collaborative learning hub or teaching platform. Can serve as a reference for thesis work or professional portfolios. Opportunity to integrate with cloud computing services and real datasets. Can evolve into a showcase for AI applications across domains.
Threats: Risk of project stagnation without active contributors or maintainers.

Progress: 100%

GitHub

AI for Engineering Projects

AI for Engineering projects

Applied AI for Mechatronics Engineering - Compilation of projects developed for UIDE's Mechatronics Engineering Bachelor Programme

Technologies: Python, Pandas, Scikit-Learn, Tensorflow, Matlab

Python TensorFlow
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Strengths: Novel integration of geospatial and digital tools tailored to invasive weed monitoring. Open-access platform promoting collaborative data entry and regional-scale mapping. Scalable design applicable to other weed species and territories.
Weaknesses: Dependence on user input for high-resolution local data. Requires continuous data validation to maintain accuracy. Platform adoption may vary across regions and user profiles.
Opportunities: Growing institutional interest in biodiversity and invasive species control. Potential for national and EU-level environmental integration.
Threats: Data privacy or policy restrictions on georeferenced weed reporting. Technological barriers among end-users (training, device access). Risk of platform underuse without adequate outreach.

Progress: 100%

GitHub

Dissemination

News

Momentum's DigitalWeeds kick-off Meeting

March 2025

On March 13th, 2025, we officially launched the DigitalWeeds project with a kick-off meeting attended by the Principal Investigator (PI), the Co-Investigator (Co-IP), and myself as the contracted researcher. This meeting marked the beginning of a collaborative effort aimed at leveraging digital tools and artificial intelligence for precision weed management. During the session, we reviewed the core objectives of the project, discussed the proposed timeline, and outlined the key deliverables. The PI and Co-IP provided valuable insights into the strategic vision of DigitalWeeds, setting clear expectations for each phase of development. We also aligned on communication protocols and set milestones for the first quarter.

I Encuentro Momentum: De la teoría a la práctica

February 2025

Attending the "I Encuentro Momentum: De la teoría a la práctica" organized by the Spanish National Research Council (CSIC) was quite insightful. The main focus was on how to effectively apply digital theories in real-world scenarios. The event took place on Wednesday, February 26, 2025, starting at 11:30 AM in Madrid. I found the discussions around AI, social networks, and building a professional portfolio in today's digital landscape particularly engaging. It was clear that this encuentro was part of the CSIC's broader "Momentum CSIC Programme," an initiative aimed at fostering digital talent. The welcome address by Eloísa del Pino, the president of CSIC, really set the tone for the day, emphasizing the importance of these digital skills..

Contact

Interested in collaborating or hiring me? Feel free to email me at

christian.rueda@csic.es

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