AI for Predictive Crop Protection with Microbial Data Science

About This Course

This three-week, hands-on course teaches you how to turn soil microbiome data (ASV/OTU tables, taxonomy files, and field metadata) into actionable crop protection insights using AI. You’ll work with real datasets to clean and preprocess microbiome and environmental metadata, engineer useful microbial features, build disease and pest risk prediction models, and produce explainable outputs that translate predictions into clear, practical recommendations for crop management.

All exercises are delivered in Google Colab with step-by-step notebooks, so you can focus on learning without worrying about setup. By the end of the course, you’ll have a reusable AI pipeline and a one-page decision-support report format that can be adapted for different crops, regions, and disease/pest targets.

Aim

The goal of this course is to train participants to develop predictive and explainable AI models using soil microbiome datasets for disease and pest risk forecasting, empowering them to provide actionable decision-support insights in agriculture.

Course Objectives

By the end of this course, participants will be able to:

• Understand the structure of soil microbiome datasets and common prediction targets
• Preprocess microbiome and field metadata into machine-learning-ready formats
• Engineer microbial features, including diversity metrics, indicator taxa, and compositional transformations
• Train, compare, and evaluate disease and pest risk prediction models
• Apply explainable AI to identify microbial drivers influencing risk
• Build a reusable pipeline and generate a one-page decision-support report

Course Structure

Module 1: Soil Microbiome Data Literacy for AI

• Introduction to soil microbiome intelligence: crop protection goals and prediction targets
• Understanding dataset formats: ASV/OTU tables, taxonomy files, soil and crop metadata
• Preprocessing essentials: cleaning, normalization, and feature-ready formatting
• Hands-on (Colab): Import microbiome and metadata, preprocess, and create a labeled “risk prediction” table

Module 2: Predictive Modeling with Microbial Features

• Feature engineering: compositional transforms, diversity features, and indicator taxa
• Model building workflow: classification, handling class imbalance, robust train/test splitting
• Evaluation: cross-validation, ROC/PR metrics, and interpreting model performance
• Hands-on (Colab): Train and compare models, generate risk scores for disease and pest forecasting

Module 3: Explainable AI and Decision-Support Outputs

• Interpretability: identifying key microbial drivers, stability checks, responsible biomarker insights
• Translating predictions into actionable recommendations for interventions
• Reusable workflow: data → prediction → explanation → report
• Hands-on (Colab): Generate an explainable one-page report (risk, drivers, and recommendations) inside a notebook

Who Should Enrol?

This course is perfect for:

• Students, researchers, and faculty in Agriculture, Microbiology, Biotechnology, Environmental Science, or related fields
• Data science/AI learners and professionals interested in agritech and biological datasets
• Anyone comfortable with spreadsheets and basic biology/microbiomes (Python is helpful, but Colab guidance is provided)