Team Name:
NatureGuard
Team Members:
NatureGuard
NatureGuard is an innovative solution designed to streamline the management of Sunshine Coast Council's public spaces asset managment, by leveraging existing IoT infrastructure and predictive AI models. The goal is to optimize maintenance schedules, enhance asset utilization, improve visitor experiences, and protect Australia’s flora and fauna.
Key Features:
Dashboard for Reserve Management:
A user-friendly dashboard allows council members to monitor critical data for regions such as Sugar Bag and Mary Cairncross. This includes details on maintenance schedules, repair timelines and allocation, device health, and replacement needs. Additionally, sections are dedicated to tracking and discovering local flora and fauna, as well as recommending new additions to add to the reserves.
Predictive AI for Optimization:
The AI model forecasts visitor traffic, identifies high-traffic areas, and suggests optimized maintenance during quieter periods to minimize disruptions. A dedicated page provides insights into IoT device performance, including battery status and maintenance needs.
Automated Reports and Data Analytics:
Auto-generated reports offer detailed analyses with AI recommendations. Employees can access raw data to conduct their own analyses. The system also prioritizes repair tasks and generates schedules for staff, ensuring efficient workflows.
Visitor Management and Gamification:
TrailGuard’s AI distributes visitors across trails to reduce wear and tear. A gamified experience rewards visitors with points for activities like bike trail participation and flora/fauna discoveries, enhancing engagement and competition.
Flora and Fauna Identification:
Using an AI-based image recognizer, visitors can identify plant species in real-time via their smartphones. This feature is integrated with a scavenger hunt game, promoting interaction and discovery of new species, while supporting biodiversity conservation through data-driven planting recommendations. This is a feature which can be implemented in other areas outside of just the Sugar Bag and Mary Cairncross areas.
Comprehensive Plant Data Resource:
For council staff, the plant data page offers comprehensive information on all species within the reserves. The system tracks the geolocation of flora, allowing for the monitoring of environmental changes over time. Additionally, this data can be used to identify and plant other Australian fauna known to thrive in the area, thereby promoting biodiversity.
Impact:
TrailGuard utilizes IoT data and predictive analytics to create a more efficient, sustainable, and engaging trail experience for the Sunshine Coast community. It enhances reserve management, promotes biodiversity, and offers an interactive platform for visitors.
In this project, we leverage sensor data from two popular sites, Sugar Bag Mountain Bike Track and Mary Cairncross Scenic Reserve, to develop an AI-driven predictive maintenance system. The goal is to optimize park management by predicting when maintenance will be required, based on real-time environmental and visitor data. Sensors at each location capture key metrics such as visitor counts, air temperature, humidity, and precipitation, providing a holistic view of how these variables impact the need for maintenance.
By analyzing trends in visitor traffic and environmental conditions, we developed machine learning models to predict when areas of the parks will require maintenance. We have designed a prototype with figma that provides park managers with a user-friendly interface to view historical trends and monitor maintenance predictions.
The system is automated to make predictions, ensuring timely maintenance and efficient use of resources. This data-driven approach enables more sustainable management of park assets, reduces downtime, and enhances the visitor experience by keeping facilities in optimal condition.
Description of Use We made use of all their data sets, including the alur data set we found. The only data set we did not use was the last one the vibration data set.
Description of Use Used to track objects entering
Description of Use Used to determine number of people entering and where they went
Description of Use Used to determine number of people in each entering and where they headed
Description of Use Gets weather data to determine which plants to add.
Description of Use Used to determine which plants to add.
Description of Use Count objects entering trails.
Description of Use Gets environmental readings, used to determine new plants to add.
Description of Use Used to determine which plants use what soil readings so we can identify new plants to add.
Description of Use Gets region weather data
Description of Use Find number of people in parking lot, plus new devices
Description of Use Counts people
Description of Use Counts people.
Description of Use Used in the AI model to recognise plants using photos.
Go to Challenge | 16 teams have entered this challenge.
Go to Challenge | 16 teams have entered this challenge.
