Spectral unmixing of hyperspectral images revealed pine wilt disease sensitive endmembers
Bibliographic record
Abstract
Throughout the entire cycle of leaf phenological events, leaf colour undergoes changes that are influenced by either abiotic stress or biotic infection. These changes in colouration are closely linked to the quantity and quality of photosynthetic pigments, which directly impact the primary productivity of plants. Therefore, monitoring and quantifying leaf colouration changes are crucial for distinguishing damage caused by pine wilt nematodes from natural tree senescence. In this study, a hyperspectral camera sensor was employed for the non-invasive and non-destructive evaluation of needle colour changes in coniferous trees grown in field tests. Three distinct needle colour variations of six coniferous tree species were selected and monitored using a hyperspectral sensor: those displaying seasonal autumn colours, undergoing nematode-infected necrosis processes, and experiencing natural death. To mitigate the inherently mixed spectral properties of hyperspectral data, endmembers were extracted from individual images using the Purity Pixel Index algorithm under the assumption of linear mixing of endmembers. From a total of 1,321 endmembers extracted from 378 hyperspectral images of six pine species, eight endmembers were ultimately chosen to reconstruct hyperspectral images and generate abundance maps. Among these eight endmembers, four represent varying levels of photosynthetic pigment contents-ranging from very low to high. Consequently, these coniferous endmembers hold promise for assessing seasonal leaf phenology and the extent of damage in pine trees infected by pine wilt nematodes. This comprehensive approach underscores the effectiveness of spectral unmixing of hyperspectral images in advancing precision forestry through meticulous coniferous needle trait analysis.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".