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首页> 外文期刊>Plant Disease >Early Detection of Ganoderma Basal Stem Rot of Oil Palms Using Artificial Neural Network Spectral Analysis
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Early Detection of Ganoderma Basal Stem Rot of Oil Palms Using Artificial Neural Network Spectral Analysis

机译：利用人工神经网络谱分析早期检测油棕榈树的Ganoderma基础杆腐烂

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摘要

Ganoderma boninense is a causal agent of basal stem rot (BSR) and is responsible for a significant portion of oil palm (Elaeis guineensis) losses, which can reach US$500 million a year in Southeast Asia. At the early stage of this disease, infected palms are symptomless, which imposes difficulties in detecting the disease. In spite of the availability of tissue and DNA sampling techniques, there is a particular need for replacing costly field data collection methods for detecting Ganoderma in its early stage with a technique derived from spectroscopic and imagery data. Therefore, this study was carried out to apply the artificial neural network (ANN) analysis technique for discriminating and classifying fungal infections in oil palm trees at an early stage using raw, first, and second derivative spectroradiometer datasets.These were acquired from 1,016 spectral signatures of foliar samples in four disease levels (Tl: healthy, T2: mildly-infected, T3: moderately infected, and T4: severely infected). Most of the satisfactory results occurred in the visible range, especiallyin the green wavelength. The healthy oil palms and those which were infected by Ganoderma at an eariy stage (T2) were classified satisfactorily with an accuracy of 83.3%, and 100.0% in 540 to 550 nm, respectively, by ANN using first derivative spectraldata. The results further indicated that the sensitive frond number modeled by ANN provided the highest accuracy of 100.0% for frond number 9 compared with frond 17. This study showed evidence that employment of ANN can predict the early infection of BSRdisease on oil palm with a high degree of accuracy.
机译：Ganoderma Boninense是基底茎腐肉（BSR）的因果因子，负责大部分油棕（ElaeisGuineensis）损失，其在东南亚每年可以达到5亿美元。在这种疾病的早期阶段，受感染的棕榈树是无症状的，这在检测疾病时难以困难。尽管有组织和DNA采样技术的可用性，但是特别需要更换用于检测灵芝在其早期阶段的昂贵的现场数据收集方法，其具有源自光谱和图像数据的技术。因此，进行该研究以应用人工神经网络（ANN）分析技术，用于使用原始的，第一和第二衍生光谱放射性计数据集在早期阶段在油棕榈树上区分和分类真菌感染。这些是从1,016个光谱签名中获得的四种疾病水平的叶面样本（TL：健康，T2：轻微感染，T3：中等感染，T4：严重感染）。大多数令人满意的结果发生在可见范围内，尤其是绿色波长。健康的油棕榈树和由Ganoderma感染的那些通过ANN使用第一衍生物光谱分别在ANN的精度下令人满意地分类为83.3％和100.0％，分别为540至550nm。结果进一步表明，与弗朗17相比，ANN模型的敏感融化率为100.0％的最高精度为100.0％。这项研究表明，ANN的就业可以预测高度的油棕榈早期感染Bsrdisease的早期感染准确性。

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  • 来源
    《Plant Disease》 |2017年第6期|共8页
  • 作者

    Parisa Ahmadi; Farrah Melissa Muharam; Khairulmazmi Ahmad; Shattri Mansor; Idris Abu Seman;

  • 作者单位

    Department of Agriculture Technology Faculty of Agriculture Universiti Putra Malaysia 43400 Serdang Selangor Malaysia;

    Department of Agriculture Technology Faculty of Agriculture Universiti Putra Malaysia 43400 Serdang Selangor Malaysia;

    Geospatial Information Science Research Centre Faculty of Engineering Universiti Putra Malaysia 43400 Serdang Selangor Malaysia and Institute of Plantation Studies Universiti Putra Malaysia 43400 Serdang Selangor Malaysia;

    Department of Plant Pathology Faculty of Agriculture Universiti Putra Malaysia 43400 Serdang Selangor Malaysia;

    Department of Civil Engineering Faculty of Engineering Universiti Putra Malaysia 43400 Serdang Selangor Malaysia and Geospatial Information Science Research Centre Faculty of Engineering Universiti Putra Malaysia 43400 Serdang Selangor Malaysi;

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