Bacterial Leaf Blight Detection in Rice Crops Using Ground-Based Spectroradiometer Data and Multi-temporal Satellites Images
Bibliographic record
Abstract
This study presents a method for detecting rice crop damage due to bacterial leaf blight (BLB) infestation. Rice crop samples are first analyzed using a handheld spectroradiometer. Then, multi-temporal satellite image analysis is used to determine the most suitable vegetation indices for detecting BLB. The results showed that healthy plants have the highest first derivative value of spectral reflectance of the different categories of diseased plants. Significant difference can be found at approximately 690-770 nm (red edge region) which peak or maximum of the first derivative occurs in healthy crop whereas the highest percentage of BLB showed the lowest in that region. Moreover, visible bands such as blue, green, red, and red edge 1 band show variation of correlation in the early (vegetative) to generative stage then getting high especially in early of harvesting stage than the other bands; the NIR band exhibits a low correlation from the early stage of the growing season whereas the red and red edge bands reveal the highest correlations in the later stage of harvesting. Similarly, the satellite image analysis also reveals that disease incidence gradually increases with increasing age of the plant. The vegetation indices whose formulas consist of blue, green, red, and red edge bands (NGRDI, NPCI, and PSRI) exhibit the highest correlation with BLB infestation. NPCI and PSRI indices indicate that crop stress due to BLB is detected from ripening stage of NPCI then the senescence condition is then detected 12 days later. The coefficients of determination between these indices and BLB are 0.44, 0.63, and 0.67, respectively
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".