Detecting advanced stages of winter wheat yellow rust and aphid infection using RapidEye data in North China Plain
Why this work is in the frame
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Bibliographic record
Abstract
Yellow rust (Puccinia striiformis f. sp. Tritici) and aphid (Sitobion avenae F.) are two major biotic factors threatening winter wheat growth in the main growing region in northern China. The goal of this study was to develop a remote sensing based approach to reliably detect and discriminate yellow rust and aphid infection. The study was conducted in the North China Plain in 2017 based on RapidEye satellite images using three supervised classification algorithms, the maximum-likelihood classifier, the support vector machine, and the random forest. An overall accuracy of above 60% for aphid and above 70% for yellow rust can be achieved using a single image (May 7 or 10, 2017) with any of the three algorithms. With multi-temporal images, the overall accuracies both increased for aphid and yellow rust (above 70% and above 78%). Using the image acquired on 23 May 2017, joint infections by yellow rust and aphid can be detected with satisfaction (>73% overall accuracy), although confusion exists between the two infections. This study demonstrates that winter wheat disease/pest infection can be detected with remote sensing technologies, providing decision support to farmers, insurance companies, and government organizations in the agriculture sector.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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