AI-Driven Geospatial Analysis for Smart Irrigation, Water Resource Management & Agricultural Sustainability
About This Course
This three-day workshop introduces participants to GIS, remote sensing, artificial intelligence and machine learning for agricultural monitoring and water resource management. It covers satellite imagery analysis, vegetation indices, soil moisture assessment, irrigation demand prediction and sustainable water-use strategies. Participants will gain practical experience using accessible tools such as QGIS, Google Earth Engine, Python and Google Colab to analyse geospatial datasets and develop data-driven solutions for smart irrigation and sustainable agriculture.
Aim
To equip participants with practical knowledge and research skills in AI-driven geospatial analysis for precision agriculture, irrigation optimisation, water resource assessment and environmental sustainability.
Workshop Objectives
- Understand the fundamentals of GIS, remote sensing and precision agriculture.
- Analyse satellite imagery and geospatial datasets for agricultural monitoring.
- Apply vegetation indices and spatial analysis techniques to assess crop conditions.
- Use machine learning to predict soil moisture and irrigation requirements.
- Evaluate rainfall, water availability and agricultural water-use patterns.
- Integrate geospatial data and AI models to support irrigation planning.
- Develop research-oriented workflows for sustainable water management.
Workshop Structure
Day 1: Geospatial Analysis and Remote Sensing for Agricultural Water Management
Topics covered
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Fundamentals of GIS, remote sensing and precision agriculture.
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Satellite imagery and spatial datasets for agricultural monitoring.
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Mapping agricultural fields, watersheds and irrigation zones.
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Vegetation indices (NDVI, EVI) for crop health assessment.
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Soil moisture, rainfall and land-use data analysis.
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Geospatial data preprocessing, coordinate systems and map layers.
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Spatial visualisation and interpretation of agricultural research data.
Hands-on 1: Agricultural Land Mapping and Vegetation Analysis Using Satellite Data.
Tools: QGIS, Google Earth Engine, Google Earth Pro.
Day 2: AI and Machine Learning for Smart Irrigation
Topics covered
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Fundamentals of AI and machine learning in precision agriculture.
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Collection and preprocessing of soil moisture, rainfall and weather datasets.
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Feature selection for irrigation demand prediction.
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Regression models for soil moisture and crop water requirement estimation.
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Classification techniques for crop water stress assessment.
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Model training, testing and evaluation using performance metrics.
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Data-driven irrigation scheduling and prediction visualisation.
Hands-on 2: Develop a Machine Learning Model for Soil Moisture Prediction and Irrigation Demand Estimation.
Tools: Python, Google Colab, Pandas, NumPy, Scikit-learn, Matplotlib.
Day 3: Water Resource Optimisation and Sustainable Agriculture
Topics covered
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GIS-based water resource assessment and watershed mapping.
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Rainfall, runoff and agricultural water availability analysis.
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Irrigation scheduling using weather and soil moisture data.
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AI-supported water-use optimisation and conservation strategies.
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Rainwater harvesting and groundwater sustainability assessment.
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Integration of satellite imagery, GIS layers and machine learning outputs.
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Research result visualisation, sustainability indicators and decision-support systems.
Hands-on 3: Develop a Geospatial Irrigation Decision-Support Workflow Using Water and Weather Data.
Tools: QGIS, Google Earth Engine, Python, Google Colab, Pandas, Matplotlib.
Who Should Enrol?
- Undergraduate and postgraduate students in agriculture, environmental science, geography, hydrology, civil engineering and related disciplines.
- PhD scholars and early-career researchers working on precision agriculture, water resources, climate studies or sustainability.
- Faculty members and research professionals interested in GIS, remote sensing and AI-based agricultural applications.
- Professionals involved in irrigation planning, watershed management and environmental monitoring.
Prerequisites: Basic computer literacy is recommended. Prior programming or GIS experience is helpful but not mandatory. Participants should have access to a computer and a stable internet connection for the practical sessions.
Important Dates
Registration Ends
October 27, 2026
IST 4:30PM
Workshop Dates
October 27, 2026 – October 29, 2026
IST 5:00 PM
Workshop Outcomes
- Process and visualise satellite imagery and agricultural spatial data.
- Perform GIS-based mapping and geospatial analysis using QGIS.
- Analyse vegetation indices and environmental variables using remote sensing.
- Build basic machine learning models for soil moisture and irrigation demand prediction.
- Assess water resource data and identify opportunities for efficient water use.
- Develop a basic geospatial irrigation decision-support workflow.
- Interpret and communicate research findings through maps, plots and visualisations.
Fee Structure
Student
₹2499 | $65
Ph.D. Scholar / Researcher
₹3499 | $75
Academician / Faculty
₹4499 | $85
Industry Professional
₹5499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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