AI for Predictive Crop Protection with Microbial Data Science
Turn Soil Microbiome Data into Early-Warning Crop Protection Intelligence.
About This Course
This hands-on workshop teaches how to convert soil microbiome data (ASV/OTU tables, taxonomy, and field metadata) into actionable crop protection insights using AI. Participants will preprocess real datasets, engineer microbial features, train and evaluate risk prediction models, and generate explainable outputs that translate model predictions into practical management recommendations—all in Google Colab.
Aim
To train participants to build predictive and explainable AI models using soil microbiome datasets for disease/pest risk forecasting and decision-support.
Workshop Objectives
- Build literacy in soil microbiome datasets and prediction targets.
- Preprocess microbiome + metadata for ML-ready modeling.
- Engineer meaningful microbial features (diversity, indicator taxa, transforms).
- Train, compare, and evaluate disease/pest risk prediction models.
- Apply explainable AI to identify key microbial drivers of risk.
- Create a reusable pipeline and a one-page decision-support report.
Workshop Structure
📅 Day 1 — Soil Microbiome Data Literacy for AI
- Soil microbiome intelligence: crop protection objectives and prediction targets
- Microbiome datasets: ASV/OTU tables, taxonomy, soil/crop metadata
- Preprocessing essentials: cleaning, normalization, feature-ready formatting
- Hands-on (Colab): Import a microbiome dataset + metadata, preprocess, and build a labeled “risk prediction” table
📅 Day 2 — Predictive Modeling with Microbial Features
- Feature engineering: compositional transforms, diversity features, indicator taxa
- Model development: classification workflow, imbalance handling, robust splitting
- Evaluation: cross-validation, ROC/PR metrics, performance interpretation
- Hands-on (Colab): Train and compare two models for disease/pest risk prediction and generate risk scores
📅 Day 3 — Explainable AI and Decision-Support Outputs
- Interpretability: microbial drivers of risk, stability checks, responsible biomarker insights
- Translating predictions to actions: intervention pathways and management recommendations
- Reusable pipeline: data → prediction → explanation → report
- Hands-on (Colab): Generate an explainable one-page report (risk + key drivers + recommendations) from the model in a notebook
Who Should Enrol?
- 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.
- Basic familiarity with biology/microbiomes and comfort with spreadsheets; prior coding in Python is helpful but not mandatory (Colab guidance provided).
Important Dates
Registration Ends
01/19/2026
IST 04:30 PM
Workshop Dates
01/19/2026 – 01/21/2026
IST 05:30 PM
Workshop Outcomes
- Prepare microbiome datasets for ML (cleaning, normalization, labeling).
- Engineer features and handle imbalance for robust classification.
- Train and evaluate models using ROC/PR metrics and cross-validation.
- Generate risk scores and interpret microbial drivers using explainable AI.
- Produce a notebook-based one-page report with risk, key drivers, and recommendations.
Fee Structure
Student
₹2499 | $70
Ph.D. Scholar / Researcher
₹3499 | $80
Academician / Faculty
₹4499 | $90
Industry Professional
₹6499 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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