Early Crop Classification Based on Historical Annual Crop Inventory Data and Remote Sensing Data
Bibliographic record
Abstract
With the growth of the global population and food demand, near real-time agriculture monitoring is of great significance to ensure future food security. Early-season crop classification mapping is essential for agricultural planning and management. At the early stages of crop growth, crop classification faces significant challenges due to the difficulty in obtaining ground truth data and the absence of remote sensing images. In this study, we present a method that effectively integrates historical crop inventory data into model construction to assist in the classification of near real-time remote sensing data. We conducted tests on corn, soybeans, and winter wheat near London, Ontario, Canada. Results show that the overall accuracy was 0.84 on June 10, 2021. Among these three crops, winter wheat achieved the highest classification accuracy, with an F1 score of 0.95. Overall, these results highlight the unique advantages of our approach in leveraging historical crop type data and the temporal transferability of the model. This method holds promise for providing new solutions to address challenges such as early-season crop identification and monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".