| Project Id
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BITS025F001524
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| Project Detail
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| Project Title
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Use of Generative AI for Climate-Adaptive and Sustainable Agricultural Water Management
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| Senior Supervision Team (BITS)
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| Supervisor name and Title
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Dr. Rallapalli Srinivas
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School or Department (or company, if applicable)
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BITS PILANI, PILANI CAMPUS
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|
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Email ID
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r.srinivas@pilani.bits-pilani.ac.in
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| URL for more info
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https://bits-pilani.irins.org/profile/228172
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| a) Are you currently supervising a BITS or RMIT HDR student?
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YES
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| Please comment how many you are supervising
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4
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| b) Have you supervised an offshore candidate before?
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NO
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| If no, what support structures do you have in place?
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|
| If yes, please elaborate
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| Senior Supervision Team (RMIT)
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| Supervisor name and Title
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Deepak Gautam
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School or Department (or company, if applicable)
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STEM
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Email ID
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deepak.gautam@rmit.edu.au
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| URL for more info
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https://www.rmit.edu.au/profiles/g/deepak-gautam
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| a) Are you currently supervising a BITS or RMIT HDR student?
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NO
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| Please comment how many you are supervising
|
2
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| b) Have you supervised an offshore candidate before?
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NO
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| If no, what support structures do you have in place?
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|
| If yes, please elaborate
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N
|
| Other Supervisors (BITS)
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| Supervisor name and Title
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Dr. Dhruv Kumar
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School or Department (or company, if applicable)
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BITS PILANI, PILANI CAMPUS
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| Phone Number (Optional)
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+917742710554
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Email ID
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dhruv.kumar@pilani.bits-pilani.ac.in
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| URL for more info
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https://kudhru.github.io/
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| Other Supervisors (BITS)
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| Supervisor name and Title
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Dr Debaditya Acharya
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School or Department (or company, if applicable)
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STEM
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| Phone Number (Optional)
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39925 0512
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Email ID
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debaditya.acharya@rmit.edu.au
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| URL for more info
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tinyurl.com/ms34rp6c
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| Field of Research (For Codes)
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| 070199 | Agriculture | 30.00 |
| 080199 | Artificial intelligence | 45.00 |
| 090903 | Geospatial information and modeling | 25.00 |
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| Project Description
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|
Agricultural water management faces challenges from climate variability, rising demand, and declining water quality. Conventional AI models, though effective, rely heavily on historical data and struggle to generalize under unseen climate scenarios. This study proposes a Generative AI-driven framework that integrates synthetic climate and water scenarios with remote sensing-based soil moisture and crop health to enhance real-time decision-making and irrigation optimization. Unlike traditional AI, Generative AI models complex, nonlinear interactions, dynamically filling data gaps and optimizing water allocation. The framework will integrate AI-driven hydrological and agricultural software (SWAT+, AquaCrop, DSSAT) with Generative AI models (e.g., GANs, VAEs) and remote sensing data (Sentinel-2, MODIS, Landsat) to simulate climate-driven water demand and supply. A multi-source database of climate, soil, hydrology, crops, and socio-economic data will support model training, providing growers with adaptive irrigation strategies for both expected and extreme climate conditions.
Objectives
1. Develop an intensive multi-source database for training Generative AI models, integrating climate, soil, hydrological, remote sensing, agronomic, and socio-economic data.
2. Develop a Generative AI framework for climate-adaptive water management, integrating remote sensing data for improved irrigation planning.
3. Utilize AI-driven hydrological models (e.g., SWAT+, AquaCrop) and satellite-based soil moisture and vegetation indices for real-time irrigation optimization.
4. Generate synthetic climate and crop water demand scenarios using Generative AI for future climate conditions.
5. Develop an AI-powered chatbot and mobile application for real-time farmer advisory, integrating remote sensing-based field-specific insights.
6. Assess the socio-economic and environmental benefits of AI-driven water management, focusing on water-use efficiency, crop productivity, and groundwater conservation.
Generative AI will enable adaptive, scenario-based water management, surpassing conventional AI limitations. Remote sensing ensures high-resolution insights, while a robust database enhances AI predictions. This project empowers farmers with AI-driven, satellite-enhanced tools for sustainable water use, food security, and climate resilience.
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| Project Deliverable/Outcomes
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This study will develop a Generative AI-driven mobile application and chatbot to assist farmers in climate-adaptive water management by integrating remote sensing, AI-driven hydrological modeling, and Generative AI. First, a comprehensive multi-source database will be created, incorporating satellite remote sensing data (Sentinel-2, MODIS, Landsat) for soil moisture, evapotranspiration, and vegetation indices, along with climate, hydrological, agronomic, and socio-economic data. Hydrological and crop models such as SWAT+, AquaCrop, and DSSAT will be employed for water balance estimation and irrigation planning. Generative AI models (GANs, Variational Autoencoders) will generate synthetic climate scenarios and soil moisture predictions to address data gaps and optimize decision-making. The AI-powered mobile application and chatbot will provide real-time, field-specific advisory to farmers, integrating remote sensing insights and AI-driven recommendations for sustainable irrigation and climate adaptation. The chatbot will be multilingual and accessible, offering personalized recommendations based on farmer queries. This methodology ensures a data-driven, adaptive approach to agricultural water management, enhancing resilience to climate change. The outcomes/deliverables of the project are:
-AI-Powered Mobile Application: A farmer-friendly app integrating Generative AI, remote sensing, and hydrological models for climate-adaptive water management.
-Multilingual Generative AI Chatbot: 24/7 virtual assistant for farmers, offering localized irrigation and climate adaptation advice.
-AI-Driven Water Advisory Platform: Automated irrigation scheduling based on remote sensing, climate forecasts, and soil moisture models.
-Synthetic Data Models for Climate Resilience: Generative AI-based climate and water availability scenarios to enhance decision-making under uncertainty.
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| Research Impact Themes
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| AI/ML and Data Analytics / Data Science with a focus on applications/translation in | Food & Agri |
| ENHANCED LIVABILITY AND URBAN FUTURES | SMART CITIES, WATER STEWARDSHIP AND EFFECTIVE WATER USE |
| SUSTAINABLE DEVELOPMENT AND ENVIRONMENT
| SOCIAL AND ECONOMIC CHALLENGES IN ENERGY, WATER, FOOD, FINANCIAL MARKETS AND INFRASTRUCTURE AND PREVENTING ENVIRONMENTAL DEGRADATION |
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| Which RMIT Sustainable Development Goal (SDG) does your project align to
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SUSTAINABLE CITIES AND COMMUNITIES
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| Which RMIT Enabling Impact Platform (EIP) does your project align to
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SUSTAINABLE TECHNOLOGIES AND SYSTEMS PLATFORM
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| Which RMIT Program code will this project sit under?
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DR223 (GEO)
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| Student Capabilities and Qualifications
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Background with AI/ML capabilities, background with GIS and remote sensing, Background with agriculture sciences
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Software/Coding Skills: ML, ArcGIS
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MSc or Mtech
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| Preferred discipline of Student
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| Agriculture |
| Artificial Intelligence |
| Civil Engineering, Structural Engineering |
| Environmental Engineering |
| Environmental Science and Engineering |
| Geography, Geoinformatics, Geoscience |
|