A multiscale representation learning algorithm for internal pest and disease damage imaging in trees
Bibliographic record
Abstract
Forestry is a key pillar for the sustainable development of the economy and society, and assessing the health of trees is of significant importance for forestry protection. To address the challenges encountered in imaging tree internal pests and diseases, where the target size is small and its dielectric constant is similar to that of the surrounding medium—leading to blurred boundaries and potential target disappearance—this paper proposes the AtteNIS-subspace optimization method (SOM) imaging algorithm. The algorithm uses the output of the SOM algorithm as an estimate to establish a multiscale input–output learning framework. The frequency decoupling module, through an attention-based fusion strategy, combines with channel separation strips unit and frequency strips unit to achieve distributed outputs within the multiscale representation architecture, thereby enabling the inversion of the dielectric constant distribution in tree cross-sections. This study conducts comparative experiments with different percentages of moisture content, employing various quantities and sizes of tree cavities and insects based on models of cavities, insects, and hybrid models. Experimental results show that the proposed AtteNIS-SOM algorithm performs excellently across various models, demonstrating its robustness and adaptability. This provides new optimization insights for the application of electromagnetic wave-based nondestructive testing technologies in tree health monitoring and intelligent forestry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".