Advanced Damage Assessment Method for Bacterial Leaf Blight Disease in Rice by Integrating Remote Sensing Data for Agricultural Insurance
Bibliographic record
Abstract
This study aimed to develop a new method for assessing damage to rice plants by pests and diseases using remote sensing data to enable greater efficiency and accuracy for payment of indemnity in the agricultural insurance system of Indonesia, formally operationalized in 2016. The relationships between bacterial leaf blight (BLB) damage ratio in rice crops evaluated by pest observers using the current visual inspection method and the reflectance of each observation band of RapidEye and Sentinel 2, normalized difference vegetation index (NDVI), green NDVI (GNDVI), and red edge multiplied by the green band index (RGI) were studied. The results showed a positive relationship between BLB damage intensity and reflectance of visible wavelength bands, and a particularly strong positive correlation between the red band and BLB damage intensity. The BLB damage intensity can be evaluated based on pixels and paddy parcels. Time series analysis was conducted using Sentinel-2 data acquired during different plant growth periods such as heading, flowering, ripening, maturity, and harvesting. The results showed a strong correlation between the BLB damage intensity and reflectance of the red edge band at the rice heading and flowering stages; the correlation of BLB damage intensity with the reflectance of the visible range became stronger as the rice plant approached the harvesting stage. This study clearly demonstrated that BLB symptoms can be successfully detected and evaluated approximately one or one and a half months before the harvesting period using remote sensing data. We propose that the BLB damage intensity, currently assessed by pest observers through visual inspection methods, can be calculated from satellite data, suggesting that the satellite sensor could play a role similar to the human eye.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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 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".