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#IoT #Arduino #C++ #3D-Printing



STRIX: Ultra-Early Detection and Dynamic Response System for Forest Wildfires


### Overview

The Ultra-Early Detection and Dynamic Response System for Forest Wildfires, initiated by Harvard's Master in Design Engineering (MDE) Class of 2025, presents a groundbreaking approach to combat the increasing threat posed by wildfires to Sequoia trees. This project emerges in response to the urgent need for enhanced wildfire management strategies, as current methodologies have proven inadequate in the face of rapid environmental changes. Traditional methods are bogged down by outdated mapping and inefficient response strategies, resulting in significant delays and suboptimal fire containment. Our system leverages cutting-edge technology, real-time data acquisition, and a dynamic response interface to address these challenges head-on.


### The Challenge

Recent data indicates a disturbing rise in Sequoia tree mortality due to wildfires, underscoring the inadequacy of existing wildfire detection and response measures. The team pinpointed the critical gaps in current approaches, particularly the reliance on antiquated mapping techniques and the absence of effective response mechanisms. These shortcomings underscore the need for a transformative solution capable of addressing the complexities of modern wildfire management.


### Proposed Solution

Our proposed system is built around three innovative components designed to drastically improve wildfire management:


1. **Advanced Sensor Technology**: At the heart of our system is a 4-in-1 environmental sensor equipped with integrated AI for gas detection and analysis. This sensor boasts long-range communication capabilities and the ability to detect and alert authorities to wildfires in real-time, thereby drastically reducing response times.


2. **Scar Detection Using AI**: Employing fine-tuned image segmentation models, our system can detect scars on Sequoia tree trunks—a novel method that enhances the monitoring and health assessment of these trees.


3. **Efficient Tree Wrapping**: We have engineered a new wrapping scheme for Sequoia trees that is not only more efficient but also weather-resistant. This method simplifies the application process, reducing the time and manpower required for wrapping, thereby enabling rapid deployment in emergency situations.


### Implementation Strategy

The project follows a systematic design and implementation process that encompasses data collection, processing, and dynamic response management. By integrating a sophisticated sensor system with GIS and mapping tools, along with a user-friendly data processing interface, the project ensures that response measures are both swift and scientifically sound.


### Impact and Future Directions

The Ultra-Early Detection and Dynamic Response System is designed to be both cost-effective and scalable, making it suitable for widespread adoption. Its implementation is expected to:


- **Reduce Response Times**: With real-time data processing, the system reduces the typical response time to wildfires to under 10 minutes.

- **Increase Safety Measures**: By providing accurate and timely data, the system enhances the safety of firefighting teams and reduces the risk to affected ecosystems.

- **Decrease Burnt Acres**: The efficiency of the response system allows for quicker containment, significantly reducing the area affected by fires.

- **Enhance Wrapping Efficiency**: The new tree wrapping technology decreases the required time and resources, facilitating quicker protective measures.


The Ultra-Early Detection and Dynamic Response System for Forest Wildfires represents a pivotal advancement in the field of environmental technology. By incorporating AI-driven data analysis and innovative response mechanisms, this system sets a new standard for wildfire management, offering a robust solution to protect our valuable Sequoia forests from the growing threat of wildfires.


1. Case Design

  • Compact, enclosed case for all controller components

  • 50% reduction in size and weight(final volume under 996 Cu. In. & final weight less than 21 lbs)

  • Ability to support a 10lb static load

2. Minimum Power Requirements

  •  Provide at least 12 volts at 2 amps per motor

3. Command Execution

  • Software developed in C++

  • Process encoder signals from each motor

  • Allow command-line input

4. Functionality

  • Maintain the full range of motion outlined in Scorbot VII user manual

  • Operate the arm with a maximum payload of 4.4 lbs

  • Manipulate the robot by rotating each motor individually

The aforementioned project was done in accordance with the final project requirement for MECH 447: Mechanical Design II at the University of Nebraska-Lincoln. This group project was accomplished in a span of 15 weeks during the Fall 2019 term. Other members of the team included Drew Jerred and Connor Kaeding. Within the allocated time for this project, the design team held weekly meetings throughout the semester with the sponsor of this project, Dr. Carl Nelson, to provide updates on the progress and adjust the output result based on the specific needs of the sponsor. At the end of this period, the aforementioned deliverables were successfully met. A compact case with a volume of 154 cubic inches and an overall weight of 0.89 pounds capable of supporting 10 pounds of the external load was 3D printed. The prototype’s cost was $287.84, with a custom-designed PCB taking $67.00 of the expenses. Moreover, sufficient power was successfully provided to operate the motors. Command execution and functionality deliverables were also achieved.


The full report detailing the approach to accomplish the above tasks with justifications to the decisions made for each phase can be found below. 

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