Evaluation of Machine Learning Regression Techniques for Estimating Winter Wheat Biomass Using Biophysical, Biochemical, and UAV Multispectral Data
Bibliographic record
Abstract
Crop above-ground biomass (AGB) estimation is a critical practice in precision agriculture (PA) and is vital for monitoring crop health and predicting yields. Accurate AGB estimation allows farmers to take timely actions to maximize yields within a given growth season. The objective of this study is to use unmanned aerial vehicle (UAV) multispectral imagery, along with derived vegetation indices (VI), plant height, leaf area index (LAI), and plant nutrient content ratios, to predict the dry AGB (g/m2) of a winter wheat field in southwestern Ontario, Canada. This study assessed the effectiveness of Random Forest (RF) and Support Vector Regression (SVR) models in predicting dry ABG from 42 variables. The RF models consistently outperformed the SVR models, with the top-performing RF model utilizing 20 selected variables based on their contribution to increasing node purity in the decision trees. This model achieved an R2 of 0.81 and a root mean square error (RMSE) of 149.95 g/m2. Notably, the variables in the top-performing model included a combination of MicaSense bands, VIs, nutrient content levels, nutrient content ratios, and plant height. This model significantly outperformed all other RF and SVR models in this study that relied solely on UAV multispectral data or plant leaf nutrient content. The insights gained from this model can enhance the estimation and management of wheat AGB, leading to more effective crop yield predictions and management.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".