A review of techniques for detecting Huanglongbing (greening) in citrus
Bibliographic record
Abstract
Huanglongbing (HLB) is the most destructive disease of citrus worldwide. Monitoring of health and detection of diseases in trees is critical for sustainable agriculture. HLB symptoms are virtually the same wherever the disease occurs. The disease is caused by Candidatus Liberibacter spp., vectored by the psyllids Diaphorina citri Kuwayama and Trioza erytreae. Electron microscopy was the first technique used for HLB detection. Nowadays, scientists are working on the development of new techniques for a rapid HLB detection, as there is no sensor commercially accessible for real-time assessment of health conditions in trees. Currently, the most widely used mechanism for monitoring HLB is exploration, which is an expensive, labor-intensive, and time-consuming process. Molecular techniques such as polymerase chain reaction are used for the identification of HLB disease, which requires detailed sampling and processing procedures. Furthermore, investigations are ongoing in spectroscopic and imaging techniques, profiling of plant volatile organic compounds, and isothermal amplification. This study recognizes the need for developing a rapid, cost-effective, and reliable health-monitoring sensor that would facilitate advancements in HLB disease detection. This paper compares the benefits and limitations of these potential methods for HLB detection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".