Research Intern – Plant Phenotyping & Growth-Defense Trade-offs

IRHS, Angers | January – June 2024

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Overview

As part of my M2 in Plant Biology, I conducted a 6-month research internship at the IRHS laboratory in Angers, within the RESPOM team.

Research Topic

Semi-automated phenotyping of apple trees to study growth-defense trade-offs

This project aimed to develop and apply robotic imaging methods to analyze the trade-off between vegetative growth and disease resistance in apple trees.

Research Poster

Methods & Skills

Technical Skills

Robotic Imaging & Phenotyping

  • Operation of automated phenotyping robot software for high-throughput image acquisition and irrigation management
  • Semi-automated phenotyping pipelines
  • Time-series analysis of plant growth

Plant Pathology & Biosafety

  • Inoculation with Erwinia amylovora (fire blight) in containment level 2 (S2) greenhouse facilities
  • Disease severity scoring and resistance phenotyping
  • Strict adherence to biosafety protocols for regulated plant pathogens
  • Application of phytohormones and chemical treatments in controlled environments

Data Analysis

  • Image processing and feature extraction (ImageJ)
  • Statistical modeling of growth-defense relationships
  • R and Java programming for data visualization and analysis

Professional & Soft Skills

  • Adaptability: Adjusted to evolving project requirements and diverse research tasks
  • Problem-Solving: Identified experimental design issues and proposed alternative approaches
  • Communication: Effectively presented technical concerns and research findings to supervisors and team members, developed scientific writing skills through comprehensive reporting
  • Attention to Detail: Maintained rigorous data collection protocols across thousands of measurements
  • Time Management: Coordinated daily monitoring of large plant populations with other project responsibilities
  • Initiative: Explored innovative solutions such as deep learning applications for automated phenotyping

Supervision

Outcomes

Full Internship Report
Final Oral Presentation

Reflections on the Internship

Initial Expectations vs. Reality

I initially chose this internship because of my strong interest in robotics and computer vision analysis in plant phenotyping. I was also particularly excited about learning transcriptomic methods (qPFD - quantitative Low Density Array based on quantitative RT-PCR in microplate), which would have allowed me to develop advanced laboratory skills and omics data analysis expertise, and using Licor equipment to measure CO₂ flux and photosynthesis.

Technical Challenges and Learning Opportunities

The internship presented several technical challenges that provided valuable learning experiences:

  • Robot Implementation: The phenotyping robot was in its early deployment phase, which presented opportunities to work on debugging and optimization. I gained experience troubleshooting software issues related to image acquisition and plant ID assignment systems.
  • Experimental Design Considerations: Drawing on my experience cultivating ornamental plants since childhood, I recognized early in the project the importance of environmental consistency in phenotyping studies. I proposed cultivating plants directly in the robot environment from germination onwards to avoid stress-related confounding factors. While this suggestion was not implemented, the experience taught me valuable lessons about the complexity of experimental design decisions and the importance of effectively communicating technical concerns in collaborative research settings. I observed how transitioning plants between growth environments can affect physiological responses, particularly regarding light spectrum and intensity during early growth stages, reinforcing the value of maintaining consistent growing conditions for reliable phenotyping data.
  • Data Acquisition Methods: I gained hands-on experience with various measurement approaches, including physical measurements and ImageJ-based digital analysis, processing approximately 2,700 images. This experience reinforced my understanding of the trade-offs between manual precision and automation efficiency in phenotyping workflows.

Project Evolution and Skill Development

The internship provided exposure to diverse aspects of plant research:

  • I gained practical experience with field-based hormone screening protocols, performing daily measurements on approximately 250 plants
  • I developed skills in systematic data collection and disease symptom assessment
  • I explored potential applications of deep learning (YOLO-based models) for automated plant growth measurement

Key Takeaways and Career Insights

This internship was instrumental in helping me clarify my professional interests and career direction. Through this experience, I engaged in significant self-reflection about my goals and aspirations. I discovered that my strengths and interests align more closely with AI-driven methodologies and computational approaches to biological research. This introspective period also led me to explore broader career paths, including considerations about medicine and how computational tools could be applied across different scientific domains.

This realization directly influenced my decision to pursue an MSc in Bioinformatics the following year at the University of Rennes 1, where I could develop the computational and data analysis skills I was eager to apply in biological and medical research.

I also learned valuable lessons about professional communication and project management. I recognized areas where I could improve, particularly in adapting my communication style to different research environments and maintaining stricter adherence to deadlines and formatting guidelines. These insights have been valuable for my professional development.

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