New Year Offer End Date: 30th April 2024
Program
Applied Machine Learning for Agriculture and Environmental Data
Skills you will gain:
About Program:
This workshop series focuses on harnessing AI to drive sustainable solutions for climate change, energy optimization, and environmental monitoring. Participants will gain hands-on experience with AI tools and techniques to tackle real-world challenges in agriculture, smart cities, renewable energy, and life cycle assessment, using platforms like Google Colab and Python libraries.
Aim: The aim of this workshop is to equip participants with practical AI skills and techniques to address critical sustainability challenges. By integrating AI into environmental and climate-related research, the workshop aims to empower researchers, academicians, and industry professionals to develop data-driven solutions for climate risk mitigation, energy optimization, smart agriculture, and sustainable urban development.
Program Objectives:
- Apply AI to sustainability challenges like climate change, energy, and agriculture.
- Gain hands-on experience with AI tools like Google Colab and Python.
- Build explainable AI models for transparency and trust.
- Focus on smart cities, renewable energy, and life cycle assessment.
- Foster collaboration between researchers, industry, and academics.
- Develop real-world solutions for environmental sustainability.
What you will learn?
📅 Day 1: Geospatial Engineering & Data Robustness
- Goal: Mastering the “Research-to-Data” pipeline using satellite and sensor inputs
- Handling Scientific Noise: Implementing automated pipelines for outlier detection and KNN-based imputation for missing environmental sensor logs
- Feature Engineering for Agri-Data: Programmatic calculation of Vegetation Indices (NDVI, EVI) and atmospheric corrections using Geopandas and Rasterio
- Dimensionality Reduction: Using PCA and RFE to identify the “minimal viable features” for publication-quality models
- Hands-on:
- Building a pre-processing class in Python to clean a raw CSV/GeoJSON dataset
📅 Day 2: Advanced Ensemble Modeling & Optimization
- Goal: Implementing high-performance algorithms that outperform traditional regression
- Beyond Linear Models: Implementing Gradient Boosted Decision Trees (XGBoost & LightGBM) for complex crop yield and soil carbon sequestration forecasting
- Spatial Validation Strategies: Addressing “Spatial Autocorrelation” using Group K-Fold Cross-Validation to ensure model generalizability across different geographical study areas
- Hyperparameter Tuning: Utilizing Optuna for Bayesian optimization to maximize $R^2$ and minimize RMSE
- Hands-on:
- Training a multi-stage regressor to predict agricultural output based on multi-variate climate data
📅 Day 3: Explainable AI (XAI) & Research Deployment
- Goal: Interpreting the “Black Box” to meet peer-review standards for causality
- Global & Local Interpretability: Utilizing SHAP to quantify the impact of specific environmental variables on model outcomes
- Feature Dependency: Generating Partial Dependence Plots (PDP) to visualize non-linear relationships (e.g., tipping point where rainfall becomes detrimental to crop health)
- Interactive Prototyping: Deploying a live Gradio interface within Google Colab to allow peers to interact with your trained model in real-time
- Hands-on:
- Generating a “Feature Importance Report” suitable for inclusion in a scientific manuscript
Mentor Profile
Fee Plan
INR 1999 /- OR USD 50
Get an e-Certificate of Participation!
Intended For :
- Researchers and academicians in sustainability, climate, and environmental sciences.
- Industry professionals working in AI, energy, agriculture, and environmental sectors.
- Data scientists and AI enthusiasts interested in applying AI to sustainability challenges.
- Ph.D. scholars and postgraduate students focused on climate change, energy optimization, or smart cities.
- Anyone looking to integrate AI into environmental data analysis and decision-making.
Career Supporting Skills
Program Outcomes
- Hands-on experience with AI tools for environmental data analysis.
- Ability to build explainable AI models for sustainability.
- Enhanced research skills in climate, energy, and agriculture.
- Practical knowledge to apply AI solutions to real-world challenges.
- Networking opportunities with experts and peers in AI and sustainability.