Integration of AI and Sustainable Computing in Agricultural Electronics for Early Wildfire Smoke Detection and Mitigation
Bibliographic record
Abstract
Wildfires pose a significant threat to agricultural sustainability, with their frequency and destructive power exacerbated by climate change. Early detection of wildfire smoke is crucial to mitigating this threat and enhancing the resilience of agricultural systems. This study contributes to sustainable computing by presenting an AI-driven approach for early wildfire smoke detection, utilising cutting-edge image processing techniques. We leverage the YOLOv9 model, a recent advancement in object detection algorithms, to analyse image data from a benchmark dataset designed for smoke detection in agricultural settings. Our approach is rooted in sustainable computing practices, with the model running on energy-efficient hardware that processes data locally, thereby reducing the carbon emissions associated with data transmission and storage. The effectiveness of the YOLOv9 model in our application is quantified by its mean Average Precision ($\mathbf{m A P}$) of$\mathbf{9 3. 2 \%}$, Precision of$\mathbf{9 1. 3 \%}$, and Recall of$\mathbf{9 1. 2 \%}$, demonstrating robust performance in detecting smoke patterns. This research not only showcases the integration of high-performance AI models with sustainable computing elements but also underscores the critical role of technological innovation in safeguarding agricultural landscapes from environmental disasters. By improving early detection capabilities, we contribute to the development of smart agricultural systems that are both sustainable and resilient, aligning with global efforts towards carbon neutrality and environmental protection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".