Name: Digital Twin Agriculture: Mastering Multi-Source Data Fusion & Machine Learning
Start: 2026-04-13
End: 2026-04-15

About Workshop

In this workshop, you will learn how AI is used to monitor crop health, predict yield, and improve farming decisions using real-world data

Aim

To equip participants with practical knowledge and hands-on skills in using AI, data analytics, and precision monitoring tools to optimize crop management, improve yield predictions, and support sustainable agricultural practices

What Participants Will Learn

To introduce participants to AI-driven tools and technologies for crop monitoring and management.
To enable accurate prediction of crop yields using data analytics and modeling techniques.
To teach methods for assessing and improving crop health through real-time monitoring.
To demonstrate how precision agriculture techniques can optimize resource use and sustainability.
To provide hands-on experience with practical datasets, sensors, and analytics platforms for informed decision-making in agriculture.

Structure

📅 Day 1 — Monitoring Crop Health with AI & Remote Sensing

Overview of crop intelligence and precision agriculture
Introduction to remote sensing and IoT sensor data in agriculture
AI techniques for monitoring crop growth and detecting stress
Key data sources and standards for crop research (MDPI datasets, open-access repositories)

🛠️ Hands-on:

Using satellite imagery and NDVI indices to assess crop health with Python (NumPy, Pandas, Rasterio)

📅 Day 2 — Predicting Crop Yields Using Machine Learning

Data preprocessing and feature engineering for crop datasets
Supervised learning models for crop yield prediction (Random Forest, XGBoost, LSTM)
Integration of environmental and climate data into predictive models
Model evaluation metrics and best practices for agriculture datasets

🛠️ Hands-on:

Building a crop yield prediction model using historical MDPI crop datasets

📅 Day 3 — Improving Crop Productivity through Data-Driven Insights

Decision support systems for crop management
AI-driven irrigation, fertilization, and pest management strategies
Trend analysis and data visualization for agronomic decisions
Future of AI in agriculture and emerging trends

🛠️ Hands-on:

Visualizing crop recommendations and productivity optimization strategies using Python (Matplotlib, Seaborn, Plotly)

Important Dates

Registration Ends

4 : 00 PM IST

Workshop Dates

2026-04-13

05:30PM IST

What You Will Gain

Outcomes

Participants will be able to monitor crop health effectively using AI and sensor-based technologies.
Participants will gain the ability to predict crop yields accurately through data-driven models.
Participants will learn strategies to optimize resource usage and improve overall farm productivity.
Participants will acquire hands-on experience with agricultural datasets and analytics tools.
Participants will be prepared to implement precision agriculture practices for sustainable crop management.

Who Should Attend

Agricultural researchers and scientists interested in AI applications in farming.
Agronomists and crop consultants looking to adopt precision agriculture techniques.
Data scientists and machine learning practitioners focusing on agriculture datasets.
Students and Ph.D. scholars in agriculture, biotechnology, environmental science, or data analytics.
Professionals in agri-tech startups and companies seeking to optimize crop productivity.
Policy makers and sustainability experts exploring data-driven approaches in agriculture.