Random Forest Outperformed Convolutional Neural Networks for Shrub Willow Above Ground Biomass Estimation Using Multi-Spectral UAS Imagery
Bibliographic record
Abstract
Shrub willow is a valuable source of hardwood biomass feedstock which is used for the production of bioenergy, biofuels, and renewable bio-based products. The biomass produced from this short-rotation woody plant can be used for heat and electricity generation. Thus, an accurate estimation of shrub willow above-ground biomass (AGB) is of paramount importance. This paper aimed to estimate shrub willow AGB using multi-spectral unmanned aerial system (UAS) imagery and machine learning techniques. To accomplish this goal, a machine learning model (i.e., random forest (RF)) and a deep learning method (i.e., convolutional neural network (CNN)) were applied to the spectral bands and some vegetation indices over a site in Camillus, NY, US in July 2019. The results demonstrated the superiority of the RF model (RMSE of 1.73 Mg/ha and R2 of 0.95) compared to the CNN (RMSE of 2.69 Mg/ha and R2 of 0.89) technique. Adding vegetation indices to spectral bands and using a convolutional approach for training purposes could significantly improve the modeling efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".