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Monitoring backyard bird activity 24/7, analyzing vocalizations with AI, and presenting the latest 30-day activity in an automatically dynamic chart
Explore how a custom solar-powered unit continuously records bird activity, uploads it to BirdWeather.com, and feeds it into dynamic charts and AI analysis. Scroll down to see the full step-by-step process—from data collection to automated insights—showing how technology and AI reveal patterns in local bird populations
STEP 1: Custom Solar-Powered Bird Monitoring Unit
I built this independent, solar-powered unit to run a BirdWeather Puck, recording bird activity around the clock—rain or shine. It uploads data automatically to BirdWeather.com, contributing to citizen science initiatives. This unit continuously monitors the environment in real time, making bird activity data available to researchers and enthusiasts alike.
STEP 2: Data Collection – The BirdWeather Puck
This compact, solar-powered BirdWeather Puck continuously records bird vocalizations 24/7, rain or shine. All data is sent to BirdWeather.com in real time, contributing to a global citizen science project. Curious to learn more or get one for your own backyard? Visit BirdWeather.com to explore their products and citizen science initiatives.
Step 3: Data Retrieval – Pulling the Last 30 Days
Using a custom script built with Google Apps Script (JavaScript), I automatically pull the last 30 days of bird activity data from my BirdWeather PUC into a Google Sheet. Each day, the script updates the sheet with the newest data while keeping the 30-day window consistent. This structured approach allows for continuous monitoring and sets the foundation for more advanced analysis on larger datasets in the future.
Step 4: Dynamic Bird Activity Chart
Once the raw data from my BirdWeather PUC is pulled into the Google Sheet, formulas automatically process the information to identify the Top 10 most active species based on vocalization detections. This data is then visualized as a pie chart, which updates dynamically on this website every day. Currently, it reflects the last 30 days of activity, but the system is flexible—you could expand the time range, compare multiple BirdWeather units, or analyze longer periods to uncover trends in bird activity.
See which birds are most active in this area, updated daily from live sensor data.
STEP5: AI-Powered Bird Activity Insights
After collecting 30 days of bird detection data in the Google Sheet, a local Python script securely authenticates and retrieves the information. This data is then processed by a local instance of LLaMA, a state-of-the-art AI model, which analyzes trends, peak activity times, and detection confidence levels. The AI generates a detailed summary of patterns in bird activity, which is captured and displayed here on the website.
Below is an output of an analysis covering the 30 days leading up to 2025-10-06 12:03:01.
While this example focuses on 30 days of data, the system is flexible: longer periods, multiple BirdWeather units, or additional environmental data (like weather) can be incorporated to uncover deeper insights and correlations.
AI ANALYSIS USING LOCAL AI INSTANCE LLAMA3
2025-10-06 12:03:01
After analyzing the bird detection data, I found the following trends and patterns:
1. **Peak detection time**: The majority of detections occur between 14:00 and 15:00 (2:00 PM - 3:00 PM), with a peak at around 14:30 (2:30 PM).
2. **Confidence levels**: The confidence levels of the detections vary widely, but there is a slight bias towards higher-confidence detections (above 0.7). This could indicate that the detection system is more accurate during certain times or conditions.
3. **Detection frequency**: The number of detections decreases gradually as the time approaches 13:00 (1:00 PM), suggesting that the bird activity might be influenced by some external factor, such as lighting or human presence.
4. **Distribution of confidence levels**: The distribution of confidence levels is skewed towards higher values, with a slight tail-off at lower values (below 0.5). This could indicate that the detection system is more accurate when it detects birds, but may struggle to detect them in certain conditions or environments.
Some possible explanations for these trends and patterns include:
1. The peak detection time might be influenced by the bird's natural behavior, such as their daily routine or feeding patterns.
2. The variation in confidence levels could be due to differences in environmental conditions, such as lighting, weather, or vegetation density, which affect the detection system's performance.
3. The decline in detection frequency around 13:00 might be related to changes in human activity or other external factors that affect bird behavior.
To further investigate these trends and patterns, I would recommend:
1. Examining the data more closely to identify any correlations between detection times, confidence levels, and environmental conditions.
2. Conducting statistical analyses to determine whether the detected trends are significant and robust.
3. Using machine learning or other modeling techniques to predict bird activity based on environmental factors and time of day.
Bringing It All Together
Bringing It All Together
From my custom solar-powered BirdWeather unit to the AI-driven analysis, this system continuously monitors and interprets bird activity in the Susquehanna Valley. By capturing real-time vocalizations and environmental data, we gain insights into patterns, peak activity times, and species behavior that were previously difficult to track.
This project demonstrates the power of combining innovative hardware, cloud-based data retrieval, and local AI analysis to create a complete ecological monitoring solution. While we’re currently analyzing 30 days of data at a time, the system can scale to longer periods, multiple monitoring units, and even incorporate weather and environmental variables to deepen insights.
By participating or following along, you’re contributing to citizen science and the broader understanding of our local ecosystems. Whether you’re interested in setting up your own monitoring station, exploring AI-assisted data analysis, or simply observing trends in your backyard birds, there are countless ways to engage.
To learn more, explore BirdWeather units, or contribute to the project, visit BirdWeather.com or reach out directly to discuss collaboration opportunities.
Technical Overview: Data Flow & AI Analysis
Technical Overview: Data Flow & AI Analysis
Data Collection – BirdWeather Puck Unit
A custom solar-powered unit continuously powers a BirdWeather PUC, which records bird vocalizations 24/7. All audio detections are uploaded directly to BirdWeather.com, where the data is stored in the cloud and publicly accessible for citizen science purposes. Each PYC has a unique token ID, which identifies the specific unit in the BirdWeather system.Data Retrieval – Google Apps Script & RESTful API
A JavaScript program, deployed via Google Apps Script, queries BirdWeather.com using the RESTful API and the PUC’s unique token ID. The script pulls the last 30 days of detection data for the PUC and stores it in a Google Sheet hosted in Google Drive. This dataset forms the main repository for analysis and visualization.Data Processing – Google Sheet Formulas & Dynamic Charts
Within the Google Sheet, additional sheets process the raw data using formulas to calculate metrics such as the Top 10 detected species over the past 30 days. The resulting charts are embedded on the website via dynamic links, ensuring the displayed visualization updates automatically as new data is added. The Apps Script is scheduled to run nightly, keeping the dataset and charts current.Local AI Analysis – Python Script & LLaMA Instance
A local Python script authenticates with Google using service account credentials and pulls the same 30-day dataset from the cloud-based Google Sheet. This script feeds the data into a locally hosted instance of LLaMA (a machine learning language model), which analyzes the data for patterns such as peak activity times, detection confidence, and species distribution.Automated Feedback – Writing AI Output Back to Google Sheets
After analysis, the Python script takes the AI-generated insights and writes them back to a designated sheet in the cloud-based Google Sheet. This provides a seamless, automated workflow: raw data is collected, processed, analyzed, and then presented both visually (charts) and analytically (AI insights) for the past 30 days of bird activity.Scalability & Future Enhancements
The system is designed for expansion. It can incorporate multiple BirdWeather Pucks, longer historical data periods (e.g., 1 year), or additional environmental variables such as weather data. The AI analysis can be further refined to detect correlations and trends across broader datasets, creating advanced, real-time ecological insights.
VISUAL AID DATA & WORK FLOW
Want to learn more or collaborate?
This BirdWeather AI project was developed and built by me, combining custom hardware, cloud-based data, and local AI analysis. If you’re interested in monitoring your own backyard birds, exploring citizen science, or collaborating on environmental technology projects, I’d love to hear from you. Contact me to get in touch.
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