Improving Crop Residue Biomass Estimation through Ensemble Modeling and Optimized Feature Selection Using UAV Multispectral Imagery
Bibliographic record
Abstract
Crop residue plays a vital role in maintaining soil health, reducing erosion, enhancing water retention, and contributing to carbon sequestration in agricultural systems. Accurate estimation of crop residue biomass is essential for understanding its distribution patterns, advancing sustainable agricultural practices and improving land management. This study integrates high-resolution UAV multispectral imagery, advanced feature selection methods, and machine learning models to develop a scalable framework for crop residue biomass prediction. An ensemble model, combining predictions from CatBoost, Support Vector Regression, Random Forest, and K-Nearest Neighbor, was created and compared to these individual models to evaluate its performance for predicting crop residue biomass. A variety of predictor variables, including spectral indices, topographic features, textural features, and raw bands, were used in these models. To improve modeling efficiency and accuracy, four feature selection techniques—Recursive Feature Elimination, Pearson correlation, Least Absolute Shrinkage and Selection Operator regression—were tested and compared to identify the most relevant predictor features. Results show that red band and variance from the blue band emerged as consistently selected top predictors across methods. Additionally, the results highlighted the importance of integrating topographic and textural features alongside spectral features to enhance crop residue biomass estimation accuracy. The ensemble approach, combined with Recursive Feature Elimination-selected features, produced the most accurate crop residue biomass predictions (R 2 = 0.425, RMSE = 243.465 g/ha). This study demonstrates the potential of ensemble models with optimized feature selection to enhance crop residue monitoring for precision agriculture and sustainable land management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.000 | 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".