Estimating the Aboveground Fresh Weight of Sugarcane Using Multispectral Images and Light Detection and Ranging (LiDAR)
Bibliographic record
Abstract
This study aimed to develop a remote sensing method for estimating the aboveground fresh weight (AGFW) of sugarcane using multispectral images and light detection and ranging (LiDAR). Remotely sensed data were acquired from an unmanned aerial vehicle (drone). Sample plots were harvested and the AGFW of each plot was measured. Sugarcane crown heights and volumes were obtained by isolating individual tree crowns using a LiDAR-derived digital surface model of the area. Multiple linear regression (MLR) and partial least-squares regression (PLSR) models were tested for the field-sampled AGFWs (dependent variable) and individual canopy heights and volumes, and spectral indices were used as independent variables or predictors. The PLSR model showed more promising results than the MLR model when predicting the AGFW over the study area. Although PLSR is well-suited to a large number of collinear predictor variables and a limited number of field samples, this study showed moderate results (R2 = 0.5). The visual appearance of the spatial distribution of the AGFW map is satisfactory. The limited no. of field samples overfitted the MLR prediction results. Overall, this research highlights the potential of integrating remote sensing technologies in the sugarcane industry, thereby improving yield estimation and effective crop management.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".