Bibliographic record
Abstract
The aim of this research is to improve automatic harvesting of orchard apples through an efficient detection method. By applying TensorRT, the YOLOv8 model will run much more efficiently while optimising computational resources. In particular, we believe that the depth and complexity of various YOLOv8 versions of the model will play a key role in improving the detection performance. Therefore, in this study, we tested several versions of YOLOv8 algorithmic recognition models, such as YOLOv8s, YOLOv8n, YOLOv8l, YOLOv8m, etc., and used a variety of model annotation methods including traditional manual annotation, unsupervised annotation, and semiautomatic annotation tools based on the large-scale SAM model SAMsaa. In addition, we tested the effectiveness of automatic apple detection in orchards with and without hardware acceleration. In order to test the above hypotheses, we conducted several experiments and showed that the overall detection performance of the YOLOv8m model was significantly improved in the experimental setting where the dataset was labelled using the SAMsaa tool and optimised using TensorRT. In addition, the overall detection performance of the YOLOv8m model was even more significantly improved in the experiments where the TensorRT-optimised dataset was labelled using the SAMsaa tool on the Jetson Xavier computing platform. The detection mAP50 improved by 33% and 32.7%, respectively, and the average detection accuracy for apple detection reached 90.41%. These results validate the effectiveness and superiority of our method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".