Bibliographic record
Abstract
Knowledge of in-field biomass can help optimize crop production efficiency through well-informed farm management decisions and practices. In this project, research was conducted on canopy characterization to estimate the biomass of foliage-based crops, such as vegetables and forage. Emphasis was placed on nondestructive methods, such as canopy reflectance and plant height sensing for in-season resource allocation. Two feasibility studies were described in this thesis.In the first experiment, a portable tripod system with a rotation ultrasonic proximity sensor was used to estimate the in-situ biomass of baby arugula and spinach grown on a high rotation raised plot. A calibration study was conducted in Sherrington, QC by scanning the canopy profile within 50 by 50 cm areas. A correlation model was built by relating the percentile of the measured height, or average maxima within 10o angular displacements, with manually determined biomass. The generated linear regression models produced the coefficient of determination (r2) values of up to 0.80 for arugula and 0.92 for spinach. The second experiment was conducted in the farm fields of Macdonald Campus of McGill University, located in Ste-Anne-de-Bellevue, QC. An integrated sensor system comprised of an ultrasonic proximity sensor and an active canopy sensor were tested individually and in combination to estimate fresh forage biomass. The system was conducted on a variety of alfalfa and grass/legume mixtures through: 1) stationary sensor sampling and 2) on-the-go field mapping. Calibration data were obtained by using stationary samples and the interpolation of mapped data using an inverse distance weighting (IDW) method. The r2 of 0.76 and 0.65 was found when predicting biomass using NDVI and plant height measurements, respectively. However, it was observed that each sensor has advantages and mutually they can compensate for their shortcomings. Thus, plant height was less affected by changes in the field and vegetation, and it predicted biomass at full canopy coverage. NDVI was more sensitive to changes in vegetation density and vegetation type that height measurement would not be able to take into account. As such, the combined sensor regression model explained 78% of the variability using field-specific biomass-predicting calibration models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".