AI, IoT and Remote Sensing for Precision Agriculture

The aim of this workshop is to help participants understand how AI, IoT, and remote sensing technologies can be integrated for precision agriculture, crop monitoring, irrigation intelligence, yield estimation, and smart farming decision-making.

• Introduce the fundamentals of precision agriculture and smart farming systems.
• Explain the role of AI, IoT, and remote sensing in modern agriculture.
• Help participants understand field-level data such as soil moisture, temperature, humidity, crop health, and vegetation status.
• Introduce IoT sensors and real-time farm data collection workflows.
• Demonstrate smart farm system mapping for soil, crop, and irrigation monitoring.
• Explain IoT architecture for smart agriculture applications.
• Introduce MQTT communication and HiveMQ-based data publishing/subscription.
• Guide participants in creating virtual IoT sensor setups using Wokwi.
• Demonstrate Python-based sensor data reading, cleaning, and basic analytics.

📅 Day 1: Foundations of AI, IoT and Remote Sensing in Precision Agriculture

• Introduction to precision agriculture and smart farming systems
• Understanding the role of AI, IoT, and remote sensing in modern agriculture
• Basics of soil monitoring, crop monitoring, irrigation intelligence, and yield estimation
• Understanding field-level data: soil moisture, temperature, humidity, crop health, and vegetation status
• Introduction to IoT sensors and real-time farm data collection
• Basics of satellite and drone-based crop monitoring
• Developing a smart agriculture framework for data-driven farm decision-making

🛠️ Hands-on:

• Hands-on 1: Smart Farm System Mapping for Soil, Crop, and Irrigation Monitoring
• Hands-on 2: Virtual IoT Sensor Setup for Soil Moisture and Environmental Data

🧰 Tools Covered: Wokwi, MQTT / HiveMQ, Google Sheets, Python

📅 Day 2: IoT-Based Farm Monitoring and Irrigation Intelligence

• Understanding IoT architecture for smart agriculture
• Working with virtual sensors for soil moisture, temperature, humidity, and crop environment monitoring
• Introduction to MQTT communication for real-time sensor data transfer
• Using HiveMQ for IoT data publishing and subscription
• Connecting sensor data workflows for smart irrigation decision-making
• Using Python for sensor data reading, cleaning, and basic analytics
• Designing irrigation alerts based on soil and crop condition indicators

🛠️ Hands-on:

• Hands-on 1: IoT-Based Soil and Crop Monitoring using Wokwi and MQTT / HiveMQ
• Hands-on 2: Python-Based Irrigation Intelligence and Alert Logic Development

🧰 Tools Covered: Wokwi, MQTT / HiveMQ, Python, Google Colab

📅 Day 3: Remote Sensing, Yield Estimation and Farm Decision Dashboards

• Introduction to satellite remote sensing for agricultural monitoring
• Using Sentinel Hub for crop health and vegetation analysis
• Understanding vegetation indices for precision agriculture
• Basics of NDVI and crop stress interpretation
• Using QGIS for spatial visualization of farm and crop health data
• Introduction to yield estimation using remote sensing and field indicators
• Creating decision-ready dashboards for crop monitoring, irrigation planning, and smart farming insights

🛠️ Hands-on:

• Hands-on 1: Crop Health and Vegetation Index Analysis using Sentinel Hub / QGIS
• Hands-on 2: Mini Project: Smart Farming Dashboard for Soil, Crop, Irrigation, and Yield Insights

🧰 Tools Covered: Sentinel Hub, QGIS, Python, Google Colab, MQTT / HiveMQ

• Explain the role of AI, IoT, and remote sensing in precision agriculture.
• Identify key field-level data indicators used in smart farming systems.
• Understand soil monitoring, crop monitoring, irrigation intelligence, and yield estimation workflows.
• Design a smart agriculture framework for data-driven farm decision-making.
• Set up a virtual IoT sensor workflow for soil moisture and environmental monitoring.
• Understand MQTT-based communication for real-time IoT data transfer.
• Use HiveMQ for publishing and subscribing to IoT sensor data.
• Process and analyze basic farm sensor data using Python and Google Colab.

• Students and postgraduate learners in agriculture, environmental science, data science, computer science, biotechnology, engineering, and related fields.
• PhD scholars and researchers working in precision agriculture, smart farming, IoT, AI, remote sensing, GIS, and crop monitoring.
• Academicians and faculty members interested in teaching or researching AI-enabled agriculture and digital farming.
• Agriculture professionals, agritech learners, and farm technology enthusiasts.
• Data science and AI learners interested in real-world agricultural applications.
• Remote sensing, GIS, and drone imaging learners interested in crop monitoring and farm analytics.