Crop scouting using UAV imagery: a case study for potatoes
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
Crop scouting is essential to manage crops such as potatoes and to detect stresses. In fact, conventional approaches require a lot of time and staff. The rise of unmanned aerial vehicle imagery offers interesting perspectives in this area. However, the development of interpreted and generalizable cartographic products that can be used directly by producers still poses challenges in terms of processing complexity and production time. The purpose of this study was to develop a tool for the phytosanitary surveillance of potato crops using scouting maps in support of conventional methods. The approach is based on relatively simple and efficient preprocessing methods when no radiometric correction data are available. The first step establishes the best correlations between some biophysical parameters directly related to phytosanitary problems and vegetation indices in the visible and infrared domains. The second step allows the development of an approach to classify the following three stresses: pests, diseases, and development problems. Validated by scouting field sites, the developed approach makes it possible to quickly produce accurate scouting maps that producers can use directly thanks to their high potential for generalization to other areas and crop productions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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