Potential in detection of cereal yellow dwarf virus in cereals through VisNIR spectroscopy
Bibliographic record
Abstract
Plant disease management often involves timely strategies and practices to prevent the spread and establishment of viral pathogens within plant populations. This study investigated the potential of using proximal sensing technologies for early detection of the yellow dwarf virus (YDV) disease. Four cereal cultivars (two wheat cultivars, Revenue and Mace, and two oat cultivars, Bass and Eurabbie) were grown under glasshouse conditions and inoculated with a YDV strain (Cereal yellow dwarf virus; CYDV) using aphid vectors. Spectral measurements of the third leaf were taken 3 weeks after infection using a FieldSpec spectrometer covering the wavelength range 325–1075 nm. Two machine learning (ML) classification algorithms, namely the eXtreme gradient boosting (XGBOOST) and the support vector machine (SVM), were used following the leave-one-cultivar-out-cross-validation (LOCOCV) approach. According to the results, virus infection in its early stages could be detected for susceptible oats (Eurabbie) with reasonable accuracy but no other cultivars. Best overall accuracy (OA) and kappa coefficient values were achieved for Eurabbie using XGBOOST built with vegetation indices (VIs) (OA = 0.79; kappa = 0.57). Further investigation into cultivar differences and growing conditions would be beneficial. More work is required to confirm the potential of VisNIR spectroscopy for detecting CYDV, especially in cultivars such as Eurabbie.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".