Using UAV hyperspectral imagery and deep learning for Object-Based quantitative inversion of Zanthoxylum rust disease index
Bibliographic record
Abstract
• First UAV-based hyperspectral quantitative dataset established for ZR detection. • Pioneering object-based quantitative inversion for tree diseases. • Unveiling the spectral characteristics of ZR. Zanthoxylum rust (ZR) poses a significant threat to Zanthoxylum bungeanum Maxim.(ZBM) production, impacting both the yield and quality. The lack of current research on ZR using unmanned aerial vehicle (UAV) remote sensing poses a challenge to achieving precise management of individual ZBM plant. This study acquired six UAV hyperspectral images to create a ZR inversion dataset . This dataset, to our knowledge, is the first dataset for remote sensing deep learning (DL) of ZR using UAV. To facilitate automated extraction of individual ZBM plant and the quantitative inversion of ZR disease index (DI), we introduced the object-based quantitative inversion framework (OQIF). OQIF achieved high accuracy in recognizing ZBM (average precision at an intersection over union threshold of 0.5 was 90.0 %). Remarkably, OQIF demonstrates outstanding quantitative inversion results for ZR DI (R 2 = 0.90, RMSE = 3.97, n = 8166). For DI < 10, the RMSE was 2.48, showcasing early detection capability. Our research has significant implications for ZBM cultivation and precision management, pioneering object-based quantitative inversion for tree diseases and yield estimation, with potential for early ZR detection.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".