Field detection and classification of citrus Huanglongbing based on hyperspectral reflectance
Field detection and classification of citrus Huanglongbing based on hyperspectral reflectance
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基于高光谱反射率的柑橘黄龙病田间检测与分类
DOI：
10.1016/j.compag.2019.105006
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发表时间：
2019-12-01
影响因子：
8.3
通讯作者：
Dai, Fen
中科院分区：
文献类型：
--
作者：
Deng, Xiaoling;Huang, Zi-xiao;Dai, Fen
柑桔黄龙病（Citrus Huanglongbing，HLB）又称柑桔绿病，是柑桔生产上最具破坏性的病害。尽早发现病害，然后根除病根，可以有效地控制其蔓延。以沙糖桔为研究对象，提出了一种基于高光谱反射率的柑橘HLB田间无损检测方法。提出了一种基于熵距离和序贯后向选择的特征波段提取方法。使用几种机器学习算法（逻辑回归、决策树、支持向量机、k-最近邻、线性判别分析和集成学习）基于叶片反射率来区分疾病组：健康的、有症状的HLB感染的和无症状的HLB感染的。结果表明，利用一级高光谱反射率进行分类是非常可行的。在这项研究中提出的波段选择方法提供了一个选择降维，同时仍然提供高的分类精度。在三组分类中，SVM学习器的准确率达到90.8%，而在两组分类（健康与症状HLB叶）中，准确率达到96%。结果还表明，仅使用几个波段不足以进行分类。在这项研究中，所提出的方法提取的13个特征波段提供了最好的性能。
Citrus Huanglongbing (HLB), also called citrus greening, is the most destructive disease in the citrus industry. Detecting the disease as early as possible and then eradicating infected roots can effectively control its spread. For the Shatangju mandarin cultivar, a non-destructive citrus HLB field detection method based on hyperspectral reflectance is proposed in this study. A characteristic band extraction method based on entropy distance and sequential backward selection is explored. Several machine learning algorithms (logistic regression, decision tree, support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning) were used to discriminate between disease groups: healthy, symptomatic HLB-infected, and asymptomatic HLB-infected, based on leaf reflectance. The results showed that the use of primary hyperspectral reflectance is very feasible for such classification. The band selection method proposed in this study provides an option for dimensionality reduction while still providing high classification accuracy. In three-group classification, the SVM learner achieved 90.8% accuracy, while in two-group classification (healthy vs symptomatic HLB leaves), the accuracy reached to 96%. The results also show that using only a few bands is insufficient for classification. In this study, 13 characteristic bands extracted by the proposed method provided the best performance.