A YOLOv8-based AI System for Real-Time Endemic Species Threat Detection and Response
Bibliographic record
Abstract
Endemic species are under threat from various factors, including habitat destruction, illegal hunting, and climate change, which necessitate urgent and effective monitoring solutions. This research introduces an advanced AI system for real-time threat detection and response, utilizing the YOLOv8 algorithm. The system is specifically developed for the protection of endemic species. It combines the strengths of YOLOv8 with enhancements like a multi-scale detection module to tackle the challenges of identifying small or camouflaged species and threats across different ecological environments. In the proposed study, a customized dataset that is developed through the application of the Histogram of Oriented Gradients (HOG) technique along with the Firefly Algorithm is employed. This approach facilitates the efficient fine-tuning of all images within the dataset, enhancing the overall effectiveness of the analysis. The Roboflow platform is used for training, validation, and testing the customized dataset for real-time object detection. YOLOv8 achieves 97.9% mAP, 94.7% precision, and 91.9% recall. Threats are recorded in a Blockchain ledger and sent to Twilio SMS alert system, making it cost-effective and efficient. The proposed framework offers high accuracy, precision, and recall, minimizing false alarms and facilitating quick interventions, making it suitable for smart environmental management systems and biodiversity conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".