Seedlings Supplement Device and Seedling Recognition Based on Convolution Neural Network
Bibliographic record
Abstract
A fully automatic plug seedling device is designed, its structure and working principle are introduced, and a plug seedling hole identification method based on CNN is proposed to address the issue of adjacent holes in order to increase the automation and intelligence of the vegetable transplanting machine. The issue of low recognition accuracy of plug seedlings is brought on by intertwined stems and leaves. This study first grows tomato seedlings in an artificial greenhouse and then utilizes an SLR camera to take pictures of those plants. The photos are then subjected to the appropriate preprocessing, such as separating the complete hole plate image into several hole images in accordance with the hole plate standards to facilitate recognition. The CNN model is then finished being trained after receiving the processed image. Relu, which has a better ability for classification, is chosen as the activation function of the convolutional layer after the network is enlarged on the basis of LeNet-5CNN. In addition, the over-fitting issue of the model is resolved using data augmentation technology, resulting in a recognition accuracy of the test set of the model that is as high as 0.985. The automatic vegetable transplanting machine can greatly increase the automation and intelligence level of the plug seedling recognition model based on CNN, which has high recognition accuracy and generalization ability. This model also solves the main technical problems of the plug seedling device and improves the machine's ability to transplant.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".