About the Project
The Lake Guardian Initiative aims to restore and safeguard Lake Victoria's ecosystem through innovative technology, sustainable practices, stakeholder inclusivity, and community engagement.
Background
The communities around Lake Victoria, Kenya, are facing a critical decline in fish landings due to climate change, overfishing, pollution, and inadequate enforcement of fishing regulations.
This has led to degraded water quality, disrupted fishing activities, and depleted fish stocks, threatening the livelihoods of the local population.
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Objectives
- Real-Time Monitoring with AI
- Encourage Sustainable Fishing Practices
- PromotePollution Control
- Develop Resilient Infrastructure
- Community Engagement
- Data-Driven Decision-Making
- Stakeholder inclusivity
Features
- Real-Time Monitoring
- Predictive Analysis Using LLMs
- Sustainable Fishing Practices
- Pollution Control
- Community Engagement
Technologies to be Employed
APIs
- Community Engagement Technologies: Vambo AI for providing multilingual identification and Translation
- Artificial Intelligence: OnDeck Fisheries AI for species identification and monitoring fishing activities
- Satellite Imagery and Remote Sensing
IOT
- Water Quality Sensors: Measure parameters like pH, temperature, dissolved oxygen, and turbidity to monitor water quality in real time.
- Satellite Imagery and Remote Sensing
- Soil Sensors: Monitor soil moisture, nutrient levels and other factors that support both practice and evidence of sustainable agriculture around the lake.
- Underwater Cameras and sensors: These are deployed to track fish populations, detect illegal fishing activities, monitor underwater ecosystems, collect data in video and image formarts for analysis.
- Weather stations: Collect data on temperature, humidity, wind speed and rainfall to predict weather patterns and prepare for extreme weather events.
- LoRaWAN: This is a low power, long range wireless data communication technology ideal for connecting remote sensors and devices.
- NB-IoT: This is a cellular technology that provides wide coverage nd low power consumption, which is suitable for environment monitoring
- Sigfox: Low powered wide-area network technology that enables long-range communication with minimal energy consumption
Data Aggregation and analysis
- Cloud Computing: To store and process large volumes of data collected from sensors and devices
- Edge computing: Process data locally on devices to reduce latency and bandwidth usage, enabling real-time decision-making
- Data Analytics tools: Analyse collected data to identify trend, predict future conditions, informing conservation strategies
- Community Engagement Technologies: Vambo AI for providing multilingual identification and Translation
- Artificial Intelligence: OnDeck Fisheries AI for species identification and monitoring fishing activities
- Satellite Imagery and Remote Sensing
IOT
- Water Quality Sensors: Measure parameters like pH, temperature, dissolved oxygen, and turbidity to monitor water quality in real time.
- Satellite Imagery and Remote Sensing
- Soil Sensors: Monitor soil moisture, nutrient levels and other factors that support both practice and evidence of sustainable agriculture around the lake.
- Underwater Cameras and sensors: These are deployed to track fish populations, detect illegal fishing activities, monitor underwater ecosystems, collect data in video and image formarts for analysis.
- Weather stations: Collect data on temperature, humidity, wind speed and rainfall to predict weather patterns and prepare for extreme weather events.
- LoRaWAN: This is a low power, long range wireless data communication technology ideal for connecting remote sensors and devices.
- NB-IoT: This is a cellular technology that provides wide coverage nd low power consumption, which is suitable for environment monitoring
- Sigfox: Low powered wide-area network technology that enables long-range communication with minimal energy consumption
Data Aggregation and analysis
- Cloud Computing: To store and process large volumes of data collected from sensors and devices
- Edge computing: Process data locally on devices to reduce latency and bandwidth usage, enabling real-time decision-making
- Data Analytics tools: Analyse collected data to identify trend, predict future conditions, informing conservation strategies
- Water Quality Sensors: Measure parameters like pH, temperature, dissolved oxygen, and turbidity to monitor water quality in real time.
- Satellite Imagery and Remote Sensing
- Soil Sensors: Monitor soil moisture, nutrient levels and other factors that support both practice and evidence of sustainable agriculture around the lake.
- Underwater Cameras and sensors: These are deployed to track fish populations, detect illegal fishing activities, monitor underwater ecosystems, collect data in video and image formarts for analysis.
- Weather stations: Collect data on temperature, humidity, wind speed and rainfall to predict weather patterns and prepare for extreme weather events.
- LoRaWAN: This is a low power, long range wireless data communication technology ideal for connecting remote sensors and devices.
- NB-IoT: This is a cellular technology that provides wide coverage nd low power consumption, which is suitable for environment monitoring
- Sigfox: Low powered wide-area network technology that enables long-range communication with minimal energy consumption
Data Aggregation and analysis
- Cloud Computing: To store and process large volumes of data collected from sensors and devices
- Edge computing: Process data locally on devices to reduce latency and bandwidth usage, enabling real-time decision-making
- Data Analytics tools: Analyse collected data to identify trend, predict future conditions, informing conservation strategies
Expected Outcome
- Improved fish populations and water quality in Lake Victoria.
- Sustainable fishing practices adopted by local communities.
- Enhanced resilience of fishing communities through robust infrastructure.
- Increased community engagement and awareness of conservation efforts.
- Community Engagement
- Data-driven decision-making leads to more effective resource management.
- Improved Living standards of local communities