Research on Tea Disease Detection System Based on Improved YOLOv8
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aiming at the problems of low efficiency and poor accuracy of traditional tea pest and disease detection, this study constructs an improved detection system based on YOLOv8.By integrating FasterBlock, EMA multi-scale attention mechanism and BiFPN module, we combine migration learning and multi-scale training to improve the detection accuracy and robustness. The performance of the improved model is excellent, with mAP50 reaching 75.0% and mAP50-95 51.6%, with a small number of parameters and low computational complexity. Finally, the detection results are fed into DeepSeek to generate prevention and control recommendations. The improved model has excellent performance, and the detection results are connected to the DeepSeek large model knowledge base to automatically analyse and generate control recommendations, which provides powerful support for the management of tea plantations and promotes the intelligent development of the tea industry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it