Data from: Avian Sentinels Neural Nest: AI-Powered Bird Monitoring System for Real-Time Detection and Species Identification
Bibliographic record
Abstract
Traditional bird deterrent methods, such as scarecrows, loud noise emitters, and netting, can become less effective over time due to bird habituation. This study presents an AI-driven avian monitoring system, integrating advanced deep learning models and real-time environmental sensing as a baseline for potential adaptive deterrent mechanisms to manage bird populations in aquaculture environments. The proposed system leverages high-resolution imaging, motion tracking, and environmental sensors to identify species and analyze behavioral patterns. The AI-powered classification is driven by the developed Avian Eye Net, a specialized neural network optimized for avian species detection and classification, ensuring high precision in real-time monitoring. The AI framework utilizes a multi-stage image processing pipeline, starting with region of interest (ROI) extraction using adaptive image segmentation. Image metadata is then processed through AI-based feature extraction and context-aware metadata parsing, which fuses structured data with neural network outputs. The final dataset is compiled into structured formats, exporting key parameters—including image filename, date, time, location, detected species, and population count—to a ready to analyze data file for further analysis. This structured approach enhances system efficiency, providing real-time, high-fidelity bird population monitoring. Experimental results demonstrate a classification accuracy of 97.2%, with a precision rate of 95.8% and a recall rate of 96.4% for avian species identification for three important species associated with aquaculture farms. In the future, the system’s integration with Internet of Things (IoT) devices may enable the deployment of non-invasive deterrent measures—such as LED lighting, ultrasonic sound waves, and airflow manipulation—to mitigate avian interference with aquaculture operations. This IoT with AI-powered approach enhances sustainable aquaculture management by ensuring minimal disruption to avian species while optimizing fish farm productivity.The dataset contains a subset of images used to build the species identification and quantification models, highlighting the main predatory birds in the area: great blue heron, egret, and Canada goose. Also included in classification are humans. Meta-data annotation from camera trap images is also included in the code and extraction from the images.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".