This 3-day hands-on workshop introduces participants to cutting-edge applications of Artificial Intelligence in agriculture. From analyzing satellite imagery and IoT data to building disease detection models and intelligent crop advisory tools, learners will explore how AI is transforming modern farming. Ideal for students, researchers, agri-tech professionals, and enthusiasts, this program blends theory with project-based learning using platforms like Google Colab and Streamlit.
Aim
To empower participants with the knowledge and practical skills to leverage Artificial Intelligence for precision farming, plant health monitoring, and data-driven crop recommendations, fostering sustainable and efficient agricultural practices.
What Participants Will Learn
Introduce participants to the core principles of precision farming and smart crop management
Train participants in the application of remote sensing and NDVI data in agriculture
Enable learners to build image classification models for crop disease detection
Facilitate hands-on development of crop advisory systems using AI logic
Discuss real-world implementations, challenges, and future potential of AI in agriculture
Structure
Day 1: Data-Driven Agriculture – Foundations of Precision Farming
Introduction to Precision Farming & Smart Crop Monitoring
Understanding Data Sources: Satellite Imagery, IoT Sensors, Weather & Soil Data
NDVI & Remote Sensing Dataset Exploration
Hands-On: Visualizing Crop Health Data with Google Colab
Case Studies: Use of Remote Sensing in Real Farms
Day 2: Deep Learning in the Field – Detecting Plant Diseases with AI
Image-Based Disease Detection in Crops
PlantVillage Dataset & Common Crop Diseases
CNN Model for Leaf Disease Classification
Model Training, Evaluation & Prediction
Tailoring Models to Specific Crops
Day 3: Smart Decisions – AI-Driven Crop Recommendation Systems
Overview of AI-Driven Decision Support in Agriculture
Using Soil, Weather & Historical Data for Crop Recommendation
Build Your Own: Crop Suggestion Tool using OpenAI or Custom Rules
Model Deployment via Streamlit or Google Colab
Use Cases: Crop Planning & Smart Irrigation Systems
Important Dates
Registration Ends
4:00 PM
Workshop Dates
2025-04-28
5:00 PM
5:00 PM
What You Will Gain
Outcomes
Understand how AI is integrated into modern agriculture systems
Gain experience in processing satellite and sensor data for crop health monitoring
Build and evaluate deep learning models for plant disease detection
Design an AI-powered crop recommendation tool using real-world data
Learn to deploy AI tools using accessible platforms like Google Colab and Streamlit
Analyze real case studies of AI use in global agriculture
Who Should Attend
Undergraduate/Graduate students in Agriculture, AI, Computer Science, or related fields
Professionals from AgriTech, Environment, or Data Science domains
Researchers or startup founders interested in Smart Farming
Basic knowledge of Python and machine learning concepts is recommended but not mandatory
