A Band Grouping Based Approach for Phenotype-Class Mapping of Tree Genotypes Using Spectro-Temporal Information in Hyperspectral Time-Series UAV Data
Bibliographic record
Abstract
Modern genomic tree breeding studies and forest health monitoring programs demand accurate mapping of tree phenotype. However, conventional field-based approaches to map phenotype are costly in terms of time and money. The recent phenomenal advances in the low-flying Unmanned Airborne Vehicle (UAV) remote sensing platforms together with the availability of high-resolution hyperspectral cameras allow to periodically capture a huge amount of crown spectral details of individual trees. These details can be exploited to map phenotypic class of tree genotypes. State-of-the-art methods that maps tree phenotype often underexploit the information in hyperspectral time-series data by using only a few specific band-based remote sensing (RS) indices to model phenological tree parameters that define the phenotypic response. Thus, we propose a wavelet-based approach to map tree phenotype of trees that a) maximally exploits the spectral and temporal information in band-groups by addressing data redundancy problem, and b) uses spectro-temporal/phenological information in the hyperspectral time-series data to map phenotype class of tree genotype. The improved performance of the proposed method over a RS index-based state-of-the-art one to map trees to a phenotypic class, on a set of 100 trees from 10 genotypes, proves the method to be performing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".