aceRaman spectroscopy coupled with interpretable machine learning for early detection of citrus Huanglongbing
Citrus Huanglongbing (HLB), caused by Candidatus Liberibacter asiaticus, threatens global citrus production, while early diagnosis is hindered by a prolonged asymptomatic period and laboratory-intensive methods. This study establishes a non-destructive framework integrating portable Raman spectroscopy, reference compound validation, optimized machine learning, and model interpretability for early HLB detection. A total of 1025 Thai pomelo leaves yielding 3075 spectra were classified into four categories: healthy…
An optimized support vector machine classified healthy, early HLB, symptomatic HLB, and nutrient-deficient pomelo leaves from Raman spectra with about 96% accuracy, giving a non-destructive route to early detection.
The source describes field deployment only as a potential, so real-world performance outside the studied Thai pomelo leaf dataset is not demonstrated.
Evidence
- Peer-reviewedSpectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy2026-10-12
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Truvace Impact Record TRV-2026-2052, v1: “Raman spectroscopy coupled with interpretable machine learning for early detection of citrus Huanglongbing.” Truvace, 2026-10-20. /record/TRV-2026-2052 (accessed at citation time). sha256 2d933fa1529808ec…
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