Modeling Plant Stress Responses to Drought Using Phenotypic and Environmental Data

Research Field

Plant Science | Ecology | Environmental Biology | Data Analysis

Project Overview

Understanding how plants respond to environmental stress is essential for improving agricultural resilience and ecosystem sustainability. In this project, a student will investigate how drought conditions influence plant growth and physiological traits using a combination of phenotypic measurements and publicly available environmental datasets.

The project is designed to expose students to authentic plant science research methods used in academic laboratories while allowing for independent hypothesis development and data-driven analysis.

Student Responsibilities

The student will:

  • Review scientific literature on plant responses to drought and water stress

  • Analyze plant growth and phenotypic datasets under varying environmental conditions

  • Develop and test hypotheses relating water availability to growth, biomass, or survival traits

  • Apply basic statistical and modeling techniques to interpret results

  • Produce a formal research paper suitable for science competitions and academic presentation

No prior research experience is required, but students should be comfortable with biology concepts and willing to learn data analysis methods.

Skills Gained

  • Foundations of plant physiology and stress biology

  • Data analysis and visualization techniques

  • Experimental design and hypothesis testing

  • Scientific writing and presentation skills

  • Experience working with a university-level research mentor

Time Commitment

Approximately 5–8 hours per week, depending on project scope and student goals.

Ideal Student Profile

  • Strong interest in plant biology, ecology, or environmental science

  • Organized, detail-oriented, and motivated to work independently

  • Prior coursework in biology recommended; statistics experience helpful but not required

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Detecting Bias and Fairness Trade-offs in Machine Learning Models Using Real-World Datasets