EXPERIMENTATION OF AN APPLICATION OF EARLY DIAGNOSIS AND INVENTORY OF SOYBEAN DISEASES (GLYCINE MAX (L.) MERR.) IN BURKINA FASO
Bibliographic record
Abstract
Glycine max (L.) Merr also known as soya or soybean plays an important role in legume production in Burkina Faso. Every year, the country produces an average of 30,000 tonnes of soybean. It is grown for its oilseeds, which are rich in protein, fat, minerals and vitamins, making it an important food and feed crop. In addition, soya production is profitable for growers because it provides a real source of income through marketing operations. The lack of fertile land, adequate rainfall and phytosanitary protection in soya cultivation are not conducive for efficient production. Ignorance and lack of knowledge of the diseases encountered in soya production make it even more difficult to protect the crop, which further limits production.In order to improve knowledge of soybean diseases in Burkina Faso, an inventory of diseases associated with this crop was carried out using a plant pathology diagnostic application. In this study, the Plantix-Crop Doctor application, based on artificial intelligence with deep learning, was used in an Alpha Lattice experimental device. A disease identification form from the Quebec Agriculture and Agri-Food Research Centre was used as a reference. Among the diseases identified were Septoria leaf spot, grey leaf spot, anthracnose, bacterial blight, soybean blight, sudden death syndrome, downy mildew, powdery mildew and soybean rust. This list provides a database of soybean diseases that must be controlled by methods that consider environmental protection. The Plantix - your crop doctor application can be relied on to diagnose soybean diseases so that they can be treated at an early stage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".