Machine learning approach for satellite-based subfield canola yield prediction using floral phenology metrics and soil parameters
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
Abstract Early monitoring of within-field yield variability and forecasting yield potential is critical for farmers and other key stakeholders such as policymakers. Remote sensing techniques are progressively being used in yield prediction studies due to easy access and affordability. Despite the increasing use of remote sensing techniques for yield prediction in agriculture, there is still a need for medium-resolution satellite imagery when predicting canola yield using a combination of crop and soil information. In this study, we investigated the utility of remotely sensed flowering information from PlanetScope (at 4 m) satellite imagery combined with derived soil and topography parameters to predict canola yield. Our yield prediction model was trained and validated using data from 21 fields managed under variable rate seed and fertilizer application, including cleaned harvester yield maps, soil, and topography maps. To quantify the flowering intensity of canola, 9 vegetation indices (VIs) were calculated using spectral bands from PlanetScope imagery acquired for the reproductive stages of canola. We created five random forest regression models using different subsets of covariates, including VIs, soil, and topography features, to predict canola yield within the season. Using a random forest regression algorithm, we recorded accuracies ranging from poor to best performing using coefficient of determination and root mean squared error (R 2 : 0.47 to 0.66, RMSE: 325 to 399 kg ha −1 ). The optimal subset of covariates identified electrical conductivity (EC), Normalized Difference Yellowness Index, and Canola Index as the key variables explaining within-spatial variability in canola yield. Our final model exhibited a validation R 2 of 0.46 (RMSE = 730 kg ha −1 ), demonstrating the potential of medium-resolution satellite imagery during the flowering stage to detect and quantify sub-field spatial and temporal floral phenology changes when predicting canola yield.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it