Bibliographic record
Abstract
In recent years, with the continuous development of agricultural technology, potato bud recognition technology has attracted more and more attention. Potato bud recognition is the key to the automatic cutting of potato seed tubers, which has a significant impact on the quality and yield of potatoes. Therefore, bud recognition technology is of great significance for the management and decision-making of agricultural production. At present, research on potato bud recognition mainly focuses on morphological methods, computer vision, and deep learning. Among them, morphological methods are mainly based on the theory of mathematical morphology, through the extraction and processing of morphological features of the bud eye to achieve recognition; Computer vision methods mainly use techniques such as image processing and feature extraction to achieve eye bud recognition; The deep learning method is mainly based on neural network models and achieves automatic recognition of eye buds through training with a large amount of data. In recent years, with the development of deep learning technology, potato bud recognition methods based on convolutional neural networks have become a research hotspot. By constructing a convolutional neural network model and using data enhancement, transfer learning and other methods, researchers have achieved a relatively significant recognition effect, but for potato bud eyes with attachments or mechanical damage on the surface, the recognition effect is general. Therefore, this article aims to review the current research progress of potato bud recognition, and seek a more efficient, accurate, and robust bud recognition technology that can provide better decision support for agricultural production, thereby improving the yield and quality of potatoes, reducing manual operation time and cost, and improving production efficiency and economic benefits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".