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首页> 外文期刊>International journal of remote sensing >Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms
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Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms

机译：使用模式识别算法基于高光谱反射率数据早期检测油棕中的基底茎腐烂病（灵芝）

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摘要

Basal stem rot (BSR) is a fatal fungal (Ganoderma) disease of oil palm plantations and has a significant impact on the production of palm oil in Malaysia. Because there is no effective treatment to control this disease, early detection of BSR is vital for sustainable disease management. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for rapid diagnosis of this disease. The aim of this work was to develop a procedure for early and accurate detection and differentiation of Ganoderma disease with different severities, based on spectral analysis and statistical models. Reflectance spectroscopy analysis ranging from the visible to near infrared region (325-1075 nm) was applied to analyse oil palm leaf samples of 47 healthy (G0), 55 slightly damaged (G1), 48 moderately damaged (G2), and 40 heavily damaged (G3) trees in order to detect and quantify Ganoderma disease at different levels of severity. Reflectance spectra were pre-processed, and principal component analysis (PCA) was performed on different pre-processed datasets including the raw dataset, first derivative, and second derivative datasets. The classification models: linear and quadratic discrimination analysis, k-nearest neighbour (kNN), and Naieeve-Bayes were applied to PC scores for classifying four levels of stress in BSR-infected oil palm trees. The analysis showed that the kNN-based model predicted the disease with a high average overall classification accuracy of 97% with the second derivative dataset. Results confirmed the usefulness and efficiency of the spectrally based classification approach in rapid screening of BSR in oil palm.
机译：基础茎腐病（BSR）是油棕人工林的致命真菌（灵芝）病，对马来西亚的棕榈油生产具有重大影响。由于没有有效的控制方法可以控制这种疾病，因此尽早发现BSR对于可持续疾病管理至关重要。视觉检测的局限性引起了人们对基于光谱的检测技术发展的兴趣，以快速诊断该疾病。这项工作的目的是基于光谱分析和统计模型，开发一种程序，用于早期和准确检测和区分具有不同严重度的灵芝疾病。反射光谱分析从可见光到近红外区域（325-1075 nm），用于分析47个健康（G0），55个轻微损坏（G1），48个中度损坏（G2）和40个严重损坏的油棕叶样品（G3）树，以检测和量化不同严重程度的灵芝疾病。预处理反射光谱，并对包括原始数据集，一阶导数和二阶导数数据集在内的不同预处理数据集执行主成分分析（PCA）。分类模型：线性和二次判别分析，k最近邻（kNN）和Naieeve-Bayes应用于PC得分，以对感染BSR的油棕树的四个应力水平进行分类。分析表明，基于kNN的模型使用二阶导数数据集预测疾病的平均总体分类准确率高达97％。结果证实了基于光谱的分类方法在快速筛选油棕中BSR方面的有用性和有效性。

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  • 来源
    《International journal of remote sensing》 |2014年第10期|3427-3439|共13页
  • 作者

    Shohreh Liaghat; Reza Ehsani; Shattri Mansor; Helmi Z.M. Shafri; Sariah Meon; Sindhuja Sankaran; Siti H.M.N. Azam;

  • 作者单位

    Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia;

    Citrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL 33850, USA;

    Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia;

    Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia;

    Department of Plant Protection, Faculty of Agriculture, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia;

    Department of Biological Systems Engineering, Washington State University, PO Box 646120, Pullman, WA 99164, USA;

    R&D Plantation, Plant Protection, Sime Darby Research Sdn Bhd, 42700 Banting, Selangor, Malaysia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
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  • 正文语种 eng
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