Identification of Bacterial Diseases in Rice Plants Leaves by the Use of Spectroscopic Imaging
Why this work is in the frame
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Bibliographic record
Abstract
<p class="1Body">Rice is staple in the African habitats menu. Bacterial wilt (BLB) and leaf streak (BLS) are some of the phytopathological diseases which restrain rice production around the world. In this paper, multi-spectral and multi-modal imaging techniques have been developed to characterize the rice leaves with symptoms of bacterial wilt (BLB) and leaf streak (BLS), and to provide information on their effects, in order to reduce their spread. First, we recorded microscopic and spectroscopic images of the samples using multimodal and multispectral microscope, with spectral region ranging from UV to NIR, for each mode. Then, we extracted the spectral footprints of the cells constituents, in transmission, reflection and scattering from the spectral images. Applying multivariate statistical analysis methods to this optical spectra allowed us to characterize the effect of bacterial rice leaves caused by <em>Xanthomonas oryzae</em> strains. The results of the proposed technique can be useful for easy identification of this type of infection, and can serve as routine approach in biochemical and agronomic laboratories.</p>
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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 | 0.000 | 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 | 0.000 |
| 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 it