Detection of Diseases in Oil Palm Plantations in the Brazilian Amazon through Orbital Image
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The detection of diseases in oil palm crops in the Brazilian Amazon represents a great challenge for the management of this crop in Brazil. The plantations in the State of Pará provide inputs for the food, cosmetics, agro-energy and biofuel industries, supplying Brazilian markets. In recent years, several factors such as pests, diseases and climate have interfered in the development of oil palm in the region, generating the need to adopt new techniques to detect and monitor such issues. In this work, spectral enhancements were carried out by simple reflectance and vegetation indices for four plots cropped on Companhia Palmares da Amazônia (CPA) farm, owned by Agropalma S.A. company in the municipality of Acará, in the state of Pará. The results allowed the identification of expressive patterns minimum and maximum reflectances of the studied plots, correlating with occurrences of diseases. The EVI index showed an excellent correlation with the occurrence of diseases. However, the NDVI and SAVI indexes showed adequate adjustments with the occurrence of diseases in 2017. The areas corresponding to the L36 and H27 plots showed higher occurrences of diseases, based on the analysis of reflectance through vegetation indices. It is concluded that the reflectance enhancements, NDVI, SAVI and EVI obtained by orbital sensors are efficient in the detection of diseases in the plots. The results allowed the identification of diagnostic anomalies of stresses in the plots, either by disease or other factor, allowing the decision making in an adequate time, therefore avoiding large scale eradication in the extensive areas in commercial palm oil plantations in Brazil.
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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 | 0.001 |
| 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 it