CONVOLUTIONAL SVM NETWORKS FOR DETECTION OF <i>GANODERMA BONINENSE</i> AT EARLY STAGE IN OIL PALM USING UAV AND MULTISPECTRAL PLEIADES IMAGES
Bibliographic record
Abstract
Abstract. Oil palm performs a considerable role in Malaysia’s economic system as Malaysia is the second-biggest palm oil manufacturer in the world. In oil palm plantations. Basal stem rot (BSR) is a disease caused by Ganoderma boninense that is responsible for a considerable annual losses, particularly in South East Asia. The disease remains an unresolved problem in most production areas due to lack of disease management strategy to detect the infected palms at their early stage. In recent years, advancement in remote sensing platforms and image processing methods have produced remarkable results for the detection of diseases at early stage. In this study, support vector machine (SVM) classifier was performed on UAV and Pleiades imagery to determine the ideal classification model for the early diagnosis of BSR disease in oil palms. The investigation's results showed that UAV provided the most accurate prediction, with a total accuracy of 68.28%, while 64.52% of the early Ganoderma infections could be identified with accuracy levels of 64.07% and 64.49%, respectively. The early Ganoderma infection could be recognized with an overall accuracy of 64.07% and 64.49%, respectively, while the Pleiades had an overall accuracy of 68.28% and 64.52%. Although the categorization accuracy appeared to be only modest at first glance, the quantity of detail offered by the imageries suggested that the accuracies were acceptable.
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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 | 0.001 | 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) | 0.001 | 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".