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 practical, real-world crop protection insights using AI. You’ll work with real datasets to clean and preprocess microbiome + environmental metadata, engineer meaningful microbial features, build disease/pest risk prediction models, and generate explainable outputs that translate model predictions into clear management recommendations.

Everything is delivered in Google Colab, with guided notebooks—so you can learn and implement without worrying about setup. By the end, you’ll have a reusable pipeline and a one-page decision-support report format that can be adapted to different crops, locations, and disease/pest targets.

Aim

To train participants to build predictive and explainable AI models using soil microbiome datasets for disease/pest risk forecasting and decision-support.

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 + field metadata into ML-ready formats

• Engineer meaningful microbial features (diversity metrics, indicator taxa, compositional transforms)

• Train, compare, and evaluate disease/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

• Soil microbiome intelligence: crop protection goals and prediction targets

• Dataset formats: ASV/OTU tables, taxonomy, soil/crop metadata

• Preprocessing essentials: cleaning, normalization, and feature-ready formatting

• Hands-on (Colab): Import microbiome + metadata, preprocess, and create a labeled “risk prediction” table

Module 2: Predictive Modeling with Microbial Features

• Feature engineering: compositional transforms, diversity features, indicator taxa

• Model building workflow: classification, imbalance handling, robust splitting

• Evaluation: cross-validation, ROC/PR metrics, interpreting performance

• Hands-on (Colab): Train and compare two models, generate risk scores for disease/pest forecasting

Module 3: Explainable AI and Decision-Support Outputs

• Interpretability: key microbial drivers, stability checks, responsible biomarker insights

• Translating predictions into action: intervention pathways and practical recommendations

• Reusable workflow: data → prediction → explanation → report

• Hands-on (Colab): Generate an explainable one-page report (risk + drivers + recommendations) inside a notebook

Who Should Enrol?

This course is ideal 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 not mandatory—Colab guidance is provided)