Artificial intelligence analysis of contributive factors in determining blackleg disease severity in canola farmlands
Bibliographic record
Abstract
Canola (Brassica napus L.) production is threatened by blackleg disease caused by Leptosphaeria maculans.Disease outcome is determined by interactions among pathogens, plants, farming practices, and environmental factors.Although the gene-for-gene interactions between the pathogen and its plant host are relatively clear, how precisely the pathogen interacts with the environment and farming practices is still poorly understood, making disease forecasting challenging for commercial farmlands.In recent years, artificial intelligence (AI) has been successful in forecasting disease risks based on environmental factors.In this study, we evaluated two AI methods and a data augmentation method to forecast disease risk using a dataset collected from 116 farmlands in Alberta in 2021 and 2022.We first assessed a machine learning model (support vector machine or SVM) and a deep-learning model (convolutional neural network or CNN) to predict blackleg severity based on five weather variables, flea beetle damage, root maggot damage, and crop-rotation variables.Both SVM and CNN predicted the disease risk with an accuracy of over 66%.The data augmentation method did not improve model performance.Flea beetle feeding and maggot damage contribute little to the model's performance, and omitting these data did not appear to affect the results.In contrast, crop rotation contributes substantially to model performance.The five weather variables contribute roughly equally to the model's performance, and removing any of the individual weather variables did not impact prediction ability for both models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".