Disease Feature Recognition of Hydroponic Lettuce Images Based on Support Vector Machine
Bibliographic record
Abstract
To achieve early recognition of lettuce diseases, this paper combines the technology of image processing and the classifier of support vector machine (SVM) to identify and classify two common diseases of hydroponic lettuce: leafroll and brown blotch disease (BBD). Specifically, the authors designed programs for the acquisition and preprocessing, segmentation, and feature extraction of hydroponic lettuce images, and developed an identification program for hydroponic lettuce diseases based on the SVM. On this basis, the color, shape, and texture features were extracted from these images, and adopted as the training set of the SVM. Then, the identification model for hydroponic lettuce diseases was trained with the radial kernel function as the core, and applied to identify the different types of diseases. In total, 1,800 images were selected as samples, and subjected to denoising, enhancement, segmentation, and feature extraction. The leaf features of hydroponic lettuce were extracted, and used to establish the SVM-based disease identification model. The experimental results on the test set show that the identification model could recognize 93% of hydroponic lettuce diseases, achieving an excellent identification effect.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".