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

• 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.

Day 1: Multi-Scale Mechanistic Modeling & Data Integration

• • 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)

• • 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

• • "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.

• 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.

• 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