Penerapan Metode Certainty Factor untuk Mendiagnosa penyakit Tanaman Tomat
Bibliographic record
Abstract
Tomatoes are plants that were first discovered in South America, closely related to eggplant, potatoes and peppers. Tomato is a fruit that has an attractive red color and is rich in vitamins such as vitamin C. So it is not wrong if tomatoes are very useful for maintaining the body's immune system. Each 100 grams of tomatoes contains 20 calories of calories, 1 gram of protein, 0.3 grams of fat, 4.2 grams of carbohydrates, 5 milligrams of calcium, carotene (vitamin A) 1500 SI, thiamin (vitamin B) 60 micrograms, ascorbic acid (vitamin C). ) 40 milligrams, phosphorus 27 milligrams, iron 0.5 milligrams, potassium 360 milligrams. Tomatoes are also vegetables or ingredients for cooking that are sought after by people to meet their daily needs. This makes the supply of tomatoes from farmers is always in shortage. The lack of supply of tomatoes in the market is caused by a decrease in tomato production or yields. This decrease in production was caused by several obstacles, one of the obstacles that caused crop failure was due to disease. Disease attacks on tomato plants can occur from planting to harvest. Diseases that often attack penicillin plants are sptoria leaf spot, anthracnose fruit bud, fusarium and verticium wilt, brown spot and late blight. Therefore, to handle this, of course, sufficient knowledge is needed to deal with and deal with pests and diseases in tomato plants appropriately. To overcome this, it is necessary to build a system that can diagnose diseases in tomato plants. So that farmers are able to overcome and deal with pests and diseases on tomato plants appropriately. Researchers have done a lot of research by building an expert system to diagnose a disease. With the results of research, an expert system is designed to assist farmers and agricultural extension workers in detecting diseases in soybean and rice plants. From the results of tests that have been carried out using an expert system, 14 different cases in the field are then cross-checked with the results of expert analysis and have a suitability of 93%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.016 |
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".