Identification of land and water Centella asiatica leaf herbal plants using digital imagery with the Sobel Edge Detection algorithm
Bibliographic record
Abstract
Centella asiatica leaves or gotu kola leaves are wild plants that grow in Asian countries such as China, Indonesia, Japan and India. Since thousands of years ago, this gotu kola leaf has been known to treat various diseases. This plant is even used as a traditional herbal medicine in China and India. Centella asiatica is an annual herbaceous plant that grows and flowers throughout the year. Plants will thrive if the soil and environment are suitable to be used as a ground cover. Types of gotu kola that are often found are red gotu kola and green gotu kola. Centella asiatica is also known as antanan taman or antanan batu because it is found in rocky, dry and open areas. Centella asiatica grows with stolons and has no stems, but has rhizomes (short rhizomes). Meanwhile, green gotu kola is often found in rice fields and on the sidelines of the grass. Based on this problem, a study is needed to develop a system to determine the shape of leaf fiber density with a comparison of ground gotu kola and water gotu kola using image processing techniques to find the diameter. This measurement process uses the Matlab application and tests with the Sobel edge detection method and image processing to see edges that are more clearly visible. The results showed that the developed system was capable of obtaining images and identifying the fiber density of Centella asiatica leaves. The system was designed with Jupyter Notebook Python-based programming language analysis with image data taken via internet sources as research material.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
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".