https://github.com/arkanivasarkar/self-aware-agentic-ai-for-monitoring-water-quality-through-satellite-observation
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/arkanivasarkar/self-aware-agentic-ai-for-monitoring-water-quality-through-satellite-observation
- Owner: arkanivasarkar
- Created: 2025-01-27T20:14:38.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-02-10T16:20:44.000Z (9 months ago)
- Last Synced: 2025-04-03T15:51:01.467Z (7 months ago)
- Language: Python
- Size: 2.35 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# NEMORRA: A Self Aware Agentic AI for Monitoring of Water Quality Through Satellite Observation to Help Precision Farming
This repository contains a prototype application for Water Quality Monitoring Through Satellite Images using a Self Aware (Self Learning) Agentic AI. This prototype was developed during [The BeHappyFish Challenge](https://blogs.upm.es/the-be-happy-fish-challenge/).
## Problem Statement
#### Utilize satellite data to design systems that monitor key water quality parameters in real time, ensuring optimal conditions for aquaculture.
This challenge addresses the need for advanced technologies to monitor factors such as water temperature, dissolved oxygen levels, turbidity, and pollutants. Data from satellites like the Sentinel series from the Copernicus program can be processed and analyzed to provide valuable insights for aquaculture farmers.
## Pain Points
- Unpredictable Water Conditions: Sudden changes harm fish, requiring constant monitoring.
- High Fish Mortality Rates: Pollution and oxygen loss cause disease outbreaks.
- Lack of Real-Time Monitoring: Manual testing is slow and reactive.
- Complex & Costly Solutions: Advanced tools are expensive and hard to use for farmers.
- Regulatory & Sustainability Challenges: Meeting environmental standards is tough without proper data.
## Our Proposed Solution
We developed an agentic AI based system to continously monitor water quality through satellite images and perform certain actions based on its understanding of the data.
The agentic AI can-
- Track and monitor algal bloom, chlorophyll and dissolved oxygen levels using satellite images.
- Inform the farmer in case the water quality goes bad.
- Talk to the farmer and solve queries.
- Suggest farmer measures to improve water quality querying a pre-developed knowledge base (vector database) of water quality management techniques.
- Fetch weather information and locality information for detecting natural causes of degrading water quality in a water body.
- Remember conversations and the fetched knowledge and add new informations in the existing knowledge base to increase generalizability with unseen data (self aware/self learning AI).