Identification of Longan Species Based on Leaf Shape Texture and Color Using KNN Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to identify the type of longan based on the shape, texture and color of the leaves using KNN classification. With a method that can identify the type of longan automatically, farmers and researchers can obtain information more quickly and accurately about the type of longan that is being cultivated or studied. This can help in choosing the right variety, more efficient maintenance, and improve the quality and productivity of longan plants. This research is an experimental research consisting of eight steps, namely preparation, theoretical studies, data collection, data analysis and processing, testing and implementation and the last is the final stage. Based on research conducted at UD Mitra Tani on Jalan Madura No. 81 Kebun Lada, Kec. Binjai Utara, Binjai City, North Sumatra, the results of data analysis from longan leaves show that the most common type of longan found in the nursery is Red longan. This study was conducted to identify the dominant longan species in the population and gain a deeper understanding of the diversity of longan varieties in the region.
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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 | 0.000 |
| 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 it