ANOVA-Artificial Bee Colony Algorithm-Driven Feature Selection for Classifying Downy Mildew Severity in Melon Leaves
Bibliographic record
Abstract
Diseases of melon plants can cause losses to farmers, such as reduced productivity or even death of melon plants.Downy mildew (DM) is a well-known fast-spreading disease affecting the leaves of melon plants.It is important to determine the level of severity of DM leaf disease so that farmers can take preventive measures according to the severity of DM disease that infects the leaves.Determining the severity of DM disease can be done with experts, but experts have limitations, namely, the availability of experts, and not all areas have leaf disease experts.The stages of this research were data acquisition, preprocessing, feature extraction, feature selection, and classification.Data were taken directly from farmers' gardens and then pre-processed.Color, texture, edge, and entropy features were extracted to obtain the combined feature values.Combined features are prone to redundant and irrelevant features; therefore, feature selection must be performed to obtain the best features.This research proposes an integration concept between the analysis of variance (ANOVA) and artificial bee colony (ABC) optimization, which is named AVABC, and is used as a feature selection algorithm.The test results for the search process time for the eight best features using the AVABC algorithm took 05 minutes 23 s, whereas the test results for the search process time for eight features using ABC with the accuracy model fitness function took 20 h 08 min 55 s.The AVABC feature selection algorithm has the advantage of faster search time for the eight best features.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".