Application of machine learning methods and airborne hyperspectral remote sensing for crop yield estimation
Bibliographic record
Abstract
This study investigated the potential of developing in-season crop yield forecasting and mapping systems based on interpretation of airborne hyperspectral remote sensing imagery by machine learning algorithms. The data used for this study was obtained over a corn (Zea mays L.) field in eastern Canada. The experimental plots were set up at the Emile A. Lods Agronomy Research Center, Montreal, Quebec. Corn was grown under the twelve combinations of three nitrogen application rates (60, 120, and 250 kg N/ha), and four weed control strategies (Broad leaf weed, Grass weed, Broad leaf and grass weed control, and no weed control). The images of the experimental field were taken with a Compact Airborne Spectrographic Imager (CASI) at three times (June 30 for early growth stage, August 5 for tassel stage, and Aug 25 for mature stage) during the year 2000 growing season. Two machine learning algorithms, Artificial Neural Networks (ANN) and Decision Tree (DT) were evaluated. The performance of ANNs was compared with four conventional modeling methods. For the DT algorithms, two different aspects, (i) DT as a classification method, and (ii) DT as a feature selection tool, were explored in this study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".