Multi-crop recognition using UAV-based high-resolution NDVI time-series
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
Multi-crop recognition is a highly nonlinear task in nature as it involves many dynamic factors to address. In this paper, a decision tree based approach is presented to classify and recognize 17 different crops. High spatial and temporal normalized difference vegetation index (NDVI) signatures were extracted from multispectral imagery using a multispectral sensor onboard the quadrotor. Detailed datasets were prepared through sampling based on normal distribution with different standard deviations. The impact of reduced dimensions was also tested using principal component analysis. A very high degree of accuracy was achieved for classification. The results also indicate that NDVIs pertaining to early-to-mid season have much more weight in the classification process for multiple crops.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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