School Garden Disease Modeling Project

ISEF Category: Plant Sciences

Subcategory: Pathology  ·  Difficulty: Intermediate  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

A plant disease can spread across a garden faster than you expect, especially when nearby plants touch or share the same wet conditions. Your phone can help you track that spread like a mini field scientist. You can turn daily photos into data and test how disease pressure moves across a grid.

What Is It?

This project asks a simple question with a big answer. How does plant disease move through a school garden over time and space? A SEIR model helps you think about that spread. The letters stand for susceptible, exposed, infectious, and removed. In plain language, you track which plants can still get sick, which ones have been exposed, which ones show symptoms, and which ones no longer change much.

The spatial part matters. A garden is not a random mix of plants. Neighboring plants can affect each other through splashing water, air movement, shared tools, and crowded spacing. Think of it like a rumor in a classroom. Students sitting next to each other hear it first. Your model asks whether the same pattern shows up in the garden grid.

You use daily smartphone disease-score surveys to parameterize the model. That means you turn your observations into numbers the model can use. You are not just taking pictures. You are building a data-driven map of disease pressure.

Why This Is a Good Topic

This is a strong science fair topic because you can test a real pattern with clear data. You can compare disease spread between rows, beds, or plant types, and you can measure how fast symptoms appear. The project connects to crop loss, school gardens, and plant health monitoring. You can learn spatial modeling, data collection, and basic statistics without needing a professional lab.

Research Questions

• How does plant spacing affect the spread rate of visible disease symptoms across a school-garden grid?
• What is the effect of neighbor proximity on the chance that a healthy plant becomes symptomatic?
• Does a spatial SEIR model predict disease scores better than a non-spatial model?
• To what extent do weather conditions such as rain, humidity, or leaf wetness change the fitted transmission parameter?
• Which garden rows or beds show the highest disease pressure over time?
• How does removing the most symptomatic plants from the model change predicted spread?

Basic Materials

• Smartphone with a camera
• Printed garden map or grid notebook
• Clipboard or hard notebook
• Plant ID labels or grid markers
• Ruler or measuring tape
• Weather app or local weather log
• Spreadsheet software
• Colored pencils or markers
• Digital kitchen scale if you also track plant biomass changes.

Advanced Materials

• Smartphone with consistent camera settings
• Tripod or phone mount
• Calibrated color reference card
• GPS or map-based plotting tool
• Open-source image analysis software
• Statistical software for model fitting
• Access to garden plot records
• Soil moisture meter
• Optional handheld leaf wetness sensor
• Optional spectroscopy app or sensor for symptom scoring.

Software & Tools

• Google Sheets: Organizes daily scores, tracks each plant, and plots spread over time.
• ImageJ: Measures leaf color and lesion area from photos when you want more objective scores.
• R: Fits spatial models, compares parameters, and runs statistical tests.
• Python: Helps you automate grid mapping, data cleaning, and model simulations.
• QGIS: Maps the garden grid and helps you visualize disease clusters in space.

Experiment Steps

1. Define the garden grid and decide which plants you will score each day.
2. Choose one disease score scale and write clear rules for each score level.
3. Plan how you will turn photos or observations into a numeric dataset.
4. Build the SEIR structure you want to test and decide which parameters you will estimate from the data.
5. Set up controls that separate true spread from weather, crowding, and plant-type differences.
6. Choose the statistics you will use to compare spatial predictions with real observations.

Common Pitfalls

• Scoring symptoms differently from day to day, which makes the model fit noisy data instead of disease spread.
• Changing camera angle or lighting, which shifts color and makes smartphone scores hard to compare.
• Mixing up true disease spread with drought stress or nutrient problems, which can inflate the apparent transmission rate.
• Tracking too few plants, which leaves the grid too sparse for a spatial model to detect neighborhood effects.
• Ignoring missing days in the survey record, which creates fake jumps in the time series and weakens the parameter fit.

What Makes This Competitive

A stronger version of this project does more than describe spread. It compares spatial and non-spatial models, tests whether neighbor effects matter, and reports how much prediction improves. You can also try a harder statistic, like cross-validation or uncertainty bounds for each parameter. A project gets stronger when you connect the model to a real management choice, like spacing, pruning, or removal of infected plants.

Project Variations

• Focus on one crop species, then compare disease spread across different cultivars in the same garden grid.
• Replace symptom scoring with leaf-color or lesion-area estimates from ImageJ for a more quantitative response variable.
• Compare sunny and shaded sections of the garden to test whether microclimate changes disease pressure.

Learn More

• USDA ARS Plant Disease resources: Search the USDA Agricultural Research Service site for plant disease basics, management, and crop pathology topics.
• NIH PubMed: Search for review articles on plant disease epidemiology, spatial spread, and SEIR models in plants.
• NOAA Climate Data Online: Find local weather data to pair rain, humidity, and temperature with your garden observations.
• NASA Earthdata: Explore free remote-sensing and environmental data if you want to connect garden disease patterns to broader conditions.
• MIT OpenCourseWare: Search for introductory modeling, probability, and data analysis courses that help with parameter fitting and simulation.
• Plant Disease: Search recent articles in this peer-reviewed journal for examples of disease spread, diagnostics, and field sampling methods.