潜伏期柑橘黄龙病宿主糖代谢及近红外光谱特征

柑橘黄龙病(Citrus Huanglongbing)是柑橘最具有毁灭性的病害,传染性极强,严重影响柑橘产业的健康发展。及时发现潜伏期未显症的病树并挖除,能够更加有效地阻碍黄龙病的传播。为此,特研究潜伏期染病叶片的糖代谢以及叶片组织光学特性的变化规律。结果表明,淀粉、蔗糖、葡萄糖和果糖在染病未显症叶片中已经出现了异常累积,分别是健康叶片的3.58、2.16、3.41、1.70倍,同时,近红外光谱的反射率出现了升高的趋势。进一步采用Random Frog算法选择前6个敏感波段(1 015、1 331、1 065、1 334、1 022、951 nm),并结合Naïve Bayes(NB)模型对潜伏期未显症柑橘黄龙病叶片进行判别,得到了97.5%的分类正确率,对阳性样本的漏检率为0,说明采用近红外高光谱技术能够实现对未显症柑橘黄龙病的检测,可为田间柑橘黄龙病的快速普查提供新的方法。

Carbohydrate metabolism and near-infrared spectral characteristics in asymptomatic Huang-longbing infected leaves

Citrus Huanglongbing (HLB) is one of the most devastating diseases to citrus production, and can spread to various citrus species. It is necessary to establish a more efficient way to detect HLB disease and then timely remove the infection source in latent period. This research was aimed to investigate the impact of bacterium on host carbohydrate metabolism and the potential of hyperspectral imaging for citrus HLB disease in latent period. The results showed that starch, sucrose, glucose and fructose were excessively accumulated in HLB infected leaves with 3.58, 2.16, 3.41 and 1.70 times, respectively as high as to those in healthy samples. In addition, an increasing reflectance of citrus leaves in near-infrared region was observed when subjected to HLB infection. Six sensitive wavelengths (1 015, 1 331, 1 065, 1 334, 1 022 and 951 nm) were selected by Random Frog, and Naïve Bayes (NB) classifier was used for differentiating asymptomatic HLB infected leaves from healthy ones with the overall classification accuracy of 97.5% and the false negative rate of 0. These results demonstrated the feasibility of near-infrared hyperspectral imaging for HLB disease detection in the asymptomatic period.