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<p>Citrus Huanglongbing (HLB), one of the most destructive diseases of citrus, is caused by the phloem-limited, uncultured α-proteobacteria of the genus Candidatus Liberibacter (predominantly <i>Candidatus</i> Liberibacter asiaticus, <i>C</i>Las) and is transmitted by the Asian citrus psyllid and by grafting. Because no curative treatment exists, disease management is critically dependent on rapid, sensitive and field-deployable detection so that infected trees can be removed before they serve as new inoculum sources. However, prevailing detection workflows, including visual scouting, PCR-based methods, ELISA and emerging biosensing platforms, still face unresolved trade-offs among sensitivity, specificity, throughput, cost, and operability under realistic orchard conditions. Most existing reviews on this topic predate the rapid expansion of imaging-, spectroscopy-, volatile-organic-compound- and deep-learning-based approaches. This review therefore concentrates specifically on HLB detection technologies. We (i) systematically compare traditional, molecular, VOC-based, spectral/imaging, microfluidic and deep-learning methods in terms of sensitivity, specificity, time-to-result, hardware cost and field applicability; (ii) examine practical bottlenecks such as orchard environmental robustness, cost-per-tree and accessibility for smallholder growers; and (iii) outline future directions including multimodal detection platforms, portable biosensors, edge-computing for in-field deployment, and the integration of effector-protein, VOC, imaging and molecular signals into unified diagnostic frameworks. The goal is to provide a focused, analytically rigorous reference for the design of next-generation real-time HLB monitoring systems.</p>

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A review of citrus huanglongbing detection technologies: methods, challenges, and prospects

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Xiaofeng Zhang,
  • Yongjun Gu,
  • Youyu Su,
  • Qian Xu

摘要

Citrus Huanglongbing (HLB), one of the most destructive diseases of citrus, is caused by the phloem-limited, uncultured α-proteobacteria of the genus Candidatus Liberibacter (predominantly Candidatus Liberibacter asiaticus, CLas) and is transmitted by the Asian citrus psyllid and by grafting. Because no curative treatment exists, disease management is critically dependent on rapid, sensitive and field-deployable detection so that infected trees can be removed before they serve as new inoculum sources. However, prevailing detection workflows, including visual scouting, PCR-based methods, ELISA and emerging biosensing platforms, still face unresolved trade-offs among sensitivity, specificity, throughput, cost, and operability under realistic orchard conditions. Most existing reviews on this topic predate the rapid expansion of imaging-, spectroscopy-, volatile-organic-compound- and deep-learning-based approaches. This review therefore concentrates specifically on HLB detection technologies. We (i) systematically compare traditional, molecular, VOC-based, spectral/imaging, microfluidic and deep-learning methods in terms of sensitivity, specificity, time-to-result, hardware cost and field applicability; (ii) examine practical bottlenecks such as orchard environmental robustness, cost-per-tree and accessibility for smallholder growers; and (iii) outline future directions including multimodal detection platforms, portable biosensors, edge-computing for in-field deployment, and the integration of effector-protein, VOC, imaging and molecular signals into unified diagnostic frameworks. The goal is to provide a focused, analytically rigorous reference for the design of next-generation real-time HLB monitoring systems.