Canola Yield Estimation Using Remotely Sensed Images and M5P Model Tree Algorithm
Bibliographic record
Abstract
Northwestern Ontario has a shorter growing season but fertile soil, affordable land, opportunities for agricultural diversification, and a demand for canola production. Canola yield mainly varies with spatial heterogeneity of soil properties, crop parameters, and meteorological conditions; thus, existing yield estimation models must be revised before being adopted in Northwestern Ontario to ensure accuracy. Region-specific canola cultivation guidelines are essential. This study utilized high spatial-resolution images to estimate flower coverage and yield in experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada. Spectral profiles were created for canola flowers and pods. During the peak flowering period, the reflectance of green and red bands was almost identical, allowing for the successful classification of yellow flower coverage using a recursive partitioning and regression tree algorithm. A notable decrease in reflectance in the RedEdge and NIR bands was observed during the transition from pod maturation to senescence, reflecting physiological changes. Canola yield was estimated using selected vegetation indices derived from images, the percent cover of flowers, and the M5P Model Tree algorithm. Field samples were used to calibrate and validate prediction models. The model’s prediction accuracy was high, with a correlation coefficient (r) of 0.78 and a mean squared error of 7.2 kg/ha compared to field samples. In conclusion, this study provided an important insight into canola growth using remote sensing. In the future, when modelling, it is recommended to consider other variables (soil nutrients and climate) that might affect crop development.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".