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Nanotechnology · Moderate · Live online

AI-Powered Nanoagriculture: From Mechanistic Transport Modeling to Digital Crop Twins

Integrate artificial intelligence, mechanistic modeling, and agricultural data to predict bionanomaterial uptake, crop performance, phytotoxicity, and safe application doses.

★ 4.2 (9 ratings)10–12 Aug 20265:00 PM IST3 Days (60-90 Min)Certificate
The live sessions are over · get the full recording

About This Course

Join our 3-Day International Workshop on Intelligent Nanoagriculture & Digital Crop Twins! Master Python mechanistic modeling for nanoparticle transport, Explainable AI (SHAP) for crop prediction, and Monte Carlo dose optimization for sustainable precision agriculture. Register today!

Aim

To equip participants with practical knowledge and computational skills for using artificial intelligence and multi-scale modeling to predict bionanomaterial uptake, crop response, environmental risk, and safe application doses.

Workshop Objectives

  • Understand interactions among bionanomaterials, crops, soil, rhizosphere, and environmental conditions.
  • Identify important nanomaterial, crop, soil, and exposure-related modeling variables.
  • Develop a soil–root–shoot uptake and transport model.
  • Prepare experimental agricultural datasets for AI-based analysis.
  • Build machine-learning models for predicting crop growth, stress response, nutrient uptake, and yield.
  • Evaluate model performance using standard regression and classification metrics.
  • Interpret AI predictions using feature importance and SHAP analysis.
  • Perform sensitivity and uncertainty analysis.
  • Predict phytotoxicity and environmental risk.
  • Optimize bionanomaterial dose by balancing crop benefit, safety, and application efficiency.
  • Understand laboratory-to-greenhouse and field-scale model validation.
Next: Day-by-day →

Workshop Structure

Day 1: Multi-Scale Mechanistic Modeling & Data Integration

Objective: Bridge molecular behaviors with plant-scale physiological responses.

    • Foundations of “Intelligent Nanoagriculture”.
    • Mechanistic modeling of Soil–Rhizosphere–Microbiome (SRM) dynamics.
    • Nanoparticle (NP) corona formation and its impact on foliar vs. root uptake.
  • Hands-on Lab:
    • Activity: Developing a compartment-based transport model.
    • Task: Use Python to simulate NP translocation from soil to shoot based on surface charge and aggregation state.
    • Tools: Python (NumPy, SciPy), Google Colab.

Day 2: Predictive Analytics & Explainable AI (XAI)

Objective: Transform “black-box” ML models into transparent decision-support tools.

    • Feature engineering for agricultural datasets: Integrating weather, soil sensors, and NP properties.
    • Comparative analysis: Random Forest vs. XGBoost for biomass and chlorophyll prediction.
    • The “Interpretability Gap”: Why XAI is critical for field translation and regulatory approval.
  • Hands-on Lab:
    • Activity: Training a crop-response predictor.
    • Task: Implement SHAP (Shapley Additive Explanations) to identify which variables (e.g., soil pH vs. NP dose) most influence phytotoxicity.
    • Tools: Scikit-learn, XGBoost, SHAP.

Day 3: Dose Optimization, Risk Mitigation & Digital Twins

Objective: Identify the “Sweet Spot” for maximum yield with minimum environmental risk.

    • “Safe-by-design”: Designing bionanomaterials for targeted delivery and low persistence.
    • Multi-objective optimization: Balancing cost, dose, and crop benefit.
    • Introduction to Digital Crop Twins: Real-time modeling of nanofertilizer efficacy.
  • Hands-on Lab:
    • Activity: AI-driven dose optimization.
    • Task: Run a Monte Carlo simulation to assess uncertainty in field performance and find the optimal application rate for a new nano-biostimulant.
    • Tools: SALib (Sensitivity Analysis), SciPy.Optimize.

Who Should Enrol?

  • Researchers, scientists, and R&D professionals
  • PhD scholars, postdoctoral researchers, and faculty members
  • Postgraduate and final-year undergraduate students
  • Agricultural, plant, soil, and environmental scientists
  • Nanotechnology, nanobiotechnology, and biotechnology professionals
  • Crop-modeling, precision-agriculture, and agricultural data specialists
  • Agrochemical and nanofertilizer industry professionals
Next: Dates →

Important Dates

Registration Ends

August 10, 2026
IST 4:00PM

Workshop Dates

August 10, 2026 – August 12, 2026
IST 5:00 PM

Workshop Outcomes

  • Model bionanomaterial uptake, transport, and accumulation in crop systems.
  • Build AI models using nanomaterial, plant, soil, and environmental data.
  • Predict crop biomass, physiological response, stress tolerance, and phytotoxicity.
  • Identify the variables that most strongly influence crop response.
  • Compare treatment formulations and application doses.
  • Apply explainable AI to interpret biological predictions.
  • Conduct basic sensitivity and uncertainty analysis.
  • Identify safe and effective bionanomaterial treatment ranges.
  • Design a reproducible predictive workflow for academic or industrial research.
  • Plan validation studies for greenhouse and field applications.
Next: Fees →

Recording fee

Participants in India

₹2,499

Participants outside India

$50

  • All session recordings
  • Study material
  • Certificate after completion
  • 1-year access

Get the recording

The live sessions of this workshop are over, so the live fees no longer apply. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.

Next: FAQ →

Frequently asked questions

Will I get the recording?

Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).

What if I miss a live session?

Watch the recording in your dashboard and send your questions to the mentor through the support desk.

Do I get a certificate?

Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.

Which fee category applies to me?

Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.

How can I pay?

By card, UPI or PayPal on the payment page; international bank transfer is also available there.

What is the refund policy?

See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy

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