Early-Season Crop Classification Utilizing Time Series Based Deep Learning with Multi-Sensor Remote Sensing Data
Bibliographic record
Abstract
Early-season crop type classification provides crucial information for monitoring in-season crop growth and predicting crop diseases, supporting global food security. In remote sensing practices for agriculture, it is technically challenging to perform early-season prediction mainly due to factors such as significant soil interference to crops on the ground and limited availability of remote sensing data. In this work, we propose a time series based machine/deep learning approach which is able to utilize all useful features of satellite images captured by multiple sensors (RCM, Sentinel-1, and Sentinel-2) in the early season. The algorithm is implemented iteratively on data sequences such that when a new satellite image becomes available, new features will be appended to existing time series data, thus providing a more accurate prediction map in updated regions. In the study, four (4) machine/deep learning algorithms, including XGBoost, CNN, and LSTM, are evaluated for their performance in terms of prediction accuracy and computational complexity. The proposed approach is tested in three study areas in the Canadian Prairie provinces in the 2021 season. Crop type mapping results are obtained from the available early-season images from June 10 to June 30, 2021, with extended results to July 31, 2021. The validation results on independent crop survey data show that the proposed algorithms can achieve crop type classification accuracy around 85% at the end of June and above 90% at the end of July.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".