scholarly journals Non-Destructive Detection of Asymptomatic Ganoderma boninense Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine

2021 ◽  
Vol 11 (22) ◽  
pp. 10878
Author(s):  
Siti Khairunniza-Bejo ◽  
Muhamad Syahir Shahibullah ◽  
Aiman Nabilah Noor Azmi ◽  
Mahirah Jahari
Keyword(s):  
Support Vector Machine ◽  
Oil Palm ◽  
Vegetation Index ◽  
Characteristic Curve ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Single Band ◽  
Support Vector ◽  
Planting Materials ◽  
Linear Svm

Breeding programs to develop planting materials resistant to G. boninense involve a manual census to monitor the progress of the disease development associated with various treatments. It is prone to error due to a lack of experience and subjective judgements. This study focuses on the early detection of G. boninense infection in the oil palm seedlings using near infra-red (NIR)-hyperspectral data and a support vector machine (SVM). The study aims to use a small number of wavelengths by using 5, 4, 3, 2, and 1 band reflectance as datasets. These results were then compared with the results of detection obtained from the vegetation indices developed using spectral reflectance taken from the same hyperspectral sensor. Results indicated a kernel with a simple linear separation between two classes would be more suitable for G. boninense detection compared to the others, both for single-band reflectance and vegetation index datasets. A linear SVM which was developed using a single-band reflectance at 934 nm was identified as the best model of detection since it was not only economical, but also demonstrated a high score of accuracy (94.8%), sensitivity (97.6%), specificity (92.5%), and area under the receiver operating characteristic curve (AUC) (0.95).

scholarly journals SPECTRAL FEATURE ANALYSIS FOR QUANTITATIVE ESTIMATION OF CYANOBACTERIA CHLOROPHYLL-A

2016 ◽  
Vol XLI-B7 ◽  
pp. 91-98
Author(s):  
Yi Lin ◽  
Zhanglin Ye ◽  
Yugan Zhang ◽  
Jie Yu
Keyword(s):  
Chlorophyll A ◽  
Vegetation Index ◽  
Quantitative Estimation ◽  
Vegetation Indices ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Single Band ◽  
Band Width ◽  
Band Model ◽  
Cyanobacteria Bloom

In recent years, lake eutrophication caused a large of Cyanobacteria bloom which not only brought serious ecological disaster but also restricted the sustainable development of regional economy in our country. <i>Chlorophyll-a</i> is a very important environmental factor to monitor water quality, especially for lake eutrophication. Remote sensed technique has been widely utilized in estimating the concentration of <i>chlorophyll-a</i> by different kind of vegetation indices and monitoring its distribution in lakes, rivers or along coastline. For each vegetation index, its quantitative estimation accuracy for different satellite data might change since there might be a discrepancy of spectral resolution and channel center between different satellites. The purpose this paper is to analyze the spectral feature of <i>chlorophyll-a</i> with hyperspectral data (totally 651 bands) and use the result to choose the optimal band combination for different satellites. The analysis method developed here in this study could be useful to recognize and monitor cyanobacteria bloom automatically and accrately. <br><br> In our experiment, the reflectance (from 350nm to 1000nm) of wild cyanobacteria in different consistency (from 0 to 1362.11ug/L) and the corresponding <i>chlorophyll-a</i> concentration were measured simultaneously. Two kinds of hyperspectral vegetation indices were applied in this study: simple ratio (SR) and narrow band normalized difference vegetation index (NDVI), both of which consists of any two bands in the entire 651 narrow bands. Then multivariate statistical analysis was used to construct the linear, power and exponential models. After analyzing the correlation between <i>chlorophyll-a</i> and single band reflectance, SR, NDVI respetively, the optimal spectral index for quantitative estimation of cyanobacteria <i>chlorophyll-a</i>, as well corresponding central wavelength and band width were extracted. Results show that: Under the condition of water disturbance, SR and NDVI are both suitable for quantitative estimation of <i>chlorophyll-a</i>, and more effective than the traditional single band model; the best regression models for SR, NDVI with <i>chlorophyll-a</i> are linear and power, respectively. Under the condition without water disturbance, the single band model works the best. For the SR index, there are two optimal band combinations, which is comprised of infrared (700nm-900nm) and blue-green range (450nm-550nm), infrared and red range (600nm-650nm) respectively, with band width between 45nm to 125nm. For NDVI, the optimal band combination includes the range from 750nm to 900nm and 700nm to 750nm, with band width less than 30nm. For single band model, band center located between 733nm-935nm, and its width mustn’t exceed the interval where band center located in. <br><br> This study proved , as for SR or NDVI, the centers and widths are crucial factors for quantitative estimating <i>chlorophyll-a</i>. As for remote sensor, proper spectrum channel could not only improve the accuracy of recognizing cyanobacteria bloom, but reduce the redundancy of hyperspectral data. Those results will provide better reference for designing the suitable spectrum channel of customized sensors for cyanobacteria bloom monitoring at a low altitude. In other words, this study is also the basic research for developing the real-time remote sensing monitoring system with high time and high spatial resolution.

scholarly journals SPECTRAL FEATURE ANALYSIS FOR QUANTITATIVE ESTIMATION OF CYANOBACTERIA CHLOROPHYLL-A

2016 ◽  
Vol XLI-B7 ◽  
pp. 91-98
Author(s):  
Yi Lin ◽  
Zhanglin Ye ◽  
Yugan Zhang ◽  
Jie Yu
Keyword(s):  
Chlorophyll A ◽  
Vegetation Index ◽  
Quantitative Estimation ◽  
Vegetation Indices ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Single Band ◽  
Band Width ◽  
Band Model ◽  
Cyanobacteria Bloom

In recent years, lake eutrophication caused a large of Cyanobacteria bloom which not only brought serious ecological disaster but also restricted the sustainable development of regional economy in our country. &lt;i&gt;Chlorophyll-a&lt;/i&gt; is a very important environmental factor to monitor water quality, especially for lake eutrophication. Remote sensed technique has been widely utilized in estimating the concentration of &lt;i&gt;chlorophyll-a&lt;/i&gt; by different kind of vegetation indices and monitoring its distribution in lakes, rivers or along coastline. For each vegetation index, its quantitative estimation accuracy for different satellite data might change since there might be a discrepancy of spectral resolution and channel center between different satellites. The purpose this paper is to analyze the spectral feature of &lt;i&gt;chlorophyll-a&lt;/i&gt; with hyperspectral data (totally 651 bands) and use the result to choose the optimal band combination for different satellites. The analysis method developed here in this study could be useful to recognize and monitor cyanobacteria bloom automatically and accrately. &lt;br&gt;&lt;br&gt; In our experiment, the reflectance (from 350nm to 1000nm) of wild cyanobacteria in different consistency (from 0 to 1362.11ug/L) and the corresponding &lt;i&gt;chlorophyll-a&lt;/i&gt; concentration were measured simultaneously. Two kinds of hyperspectral vegetation indices were applied in this study: simple ratio (SR) and narrow band normalized difference vegetation index (NDVI), both of which consists of any two bands in the entire 651 narrow bands. Then multivariate statistical analysis was used to construct the linear, power and exponential models. After analyzing the correlation between &lt;i&gt;chlorophyll-a&lt;/i&gt; and single band reflectance, SR, NDVI respetively, the optimal spectral index for quantitative estimation of cyanobacteria &lt;i&gt;chlorophyll-a&lt;/i&gt;, as well corresponding central wavelength and band width were extracted. Results show that: Under the condition of water disturbance, SR and NDVI are both suitable for quantitative estimation of &lt;i&gt;chlorophyll-a&lt;/i&gt;, and more effective than the traditional single band model; the best regression models for SR, NDVI with &lt;i&gt;chlorophyll-a&lt;/i&gt; are linear and power, respectively. Under the condition without water disturbance, the single band model works the best. For the SR index, there are two optimal band combinations, which is comprised of infrared (700nm-900nm) and blue-green range (450nm-550nm), infrared and red range (600nm-650nm) respectively, with band width between 45nm to 125nm. For NDVI, the optimal band combination includes the range from 750nm to 900nm and 700nm to 750nm, with band width less than 30nm. For single band model, band center located between 733nm-935nm, and its width mustn’t exceed the interval where band center located in. &lt;br&gt;&lt;br&gt; This study proved , as for SR or NDVI, the centers and widths are crucial factors for quantitative estimating &lt;i&gt;chlorophyll-a&lt;/i&gt;. As for remote sensor, proper spectrum channel could not only improve the accuracy of recognizing cyanobacteria bloom, but reduce the redundancy of hyperspectral data. Those results will provide better reference for designing the suitable spectrum channel of customized sensors for cyanobacteria bloom monitoring at a low altitude. In other words, this study is also the basic research for developing the real-time remote sensing monitoring system with high time and high spatial resolution.

scholarly journals TESTING THE POTENTIAL OF VEGETATION INDICES FOR LAND USE/COVER CLASSIFICATION USING HIGH RESOLUTION DATA

2017 ◽  
Vol IV-4/W4 ◽  
pp. 279-283 ◽  
Author(s):  
A. Karakacan Kuzucu ◽  
F. Bektas Balcik
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Rural Areas ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Kappa Statistics ◽  
Suitability Analysis ◽  
Agricultural Field

Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

scholarly journals Algorithmic and data modeling: Will algorithmic modeling improve predictions of traits evaluated on ordinal scales?

2020 ◽  
Author(s):  
Zhanyou Xu ◽  
Andreomar Kurek ◽  
Steven B. Cannon ◽  
Williams D. Beavis
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Ridge Regression ◽  
Genomic Prediction ◽  
Ordinal Data ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Types

AbstractSelection of markers linked to alleles at quantitative trait loci (QTL) for tolerance to Iron Deficiency Chlorosis (IDC) has not been successful. Genomic selection has been advocated for continuous numeric traits such as yield and plant height. For ordinal data types such as IDC, genomic prediction models have not been systematically compared. The objectives of research reported in this manuscript were to evaluate the most commonly used genomic prediction method, ridge regression and it’s equivalent logistic ridge regression method, with algorithmic modeling methods including random forest, gradient boosting, support vector machine, K-nearest neighbors, Naïve Bayes, and artificial neural network using the usual comparator metric of prediction accuracy. In addition we compared the methods using metrics of greater importance for decisions about selecting and culling lines for use in variety development and genetic improvement projects. These metrics include specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. We found that Support Vector Machine provided the best specificity for culling IDC susceptible lines, while Random Forest GP models provided the best combined set of decision metrics for retaining IDC tolerant and culling IDC susceptible lines.

Gaits Classification of Normal vs. Patients by Wireless Gait Sensor and Support Vector Machine (SVM) Classifier

2017 ◽  
Vol 5 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Taro Nakano ◽  
B.T. Nukala ◽  
J. Tsay ◽  
Steven Zupancic ◽  
Amanda Rodriguez ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Human Subjects ◽  
Wearable Sensors ◽  
Normal Subjects ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Dynamic ◽  
Linear Svm ◽  
Dynamic Gait

Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, the authors took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, the authors used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, the authors should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.

scholarly journals Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

2019 ◽  
Vol 40 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Toyohiro Hamaguchi ◽  
Takeshi Saito ◽  
Makoto Suzuki ◽  
Toshiyuki Ishioka ◽  
Yamato Tomisawa ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Peak Velocity ◽  
Hand Movement ◽  
Classification Model ◽  
Support Vector ◽  
Leap Motion ◽  
Stroke Patients ◽  
Related Data ◽  
Testing Dataset ◽  
Linear Svm

Abstract Purpose Traditionally, clinical evaluation of motor paralysis following stroke has been of value to physicians and therapists because it allows for immediate pathophysiological assessment without the need for specialized tools. However, current clinical methods do not provide objective quantification of movement; therefore, they are of limited use to physicians and therapists when assessing responses to rehabilitation. The present study aimed to create a support vector machine (SVM)-based classifier to analyze and validate finger kinematics using the leap motion controller. Results were compared with those of 24 stroke patients assessed by therapists. Methods A non-linear SVM was used to classify data according to the Brunnstrom recovery stages of finger movements by focusing on peak angle and peak velocity patterns during finger flexion and extension. One thousand bootstrap data values were generated by randomly drawing a series of sample data from the actual normalized kinematics-related data. Bootstrap data values were randomly classified into training (940) and testing (60) datasets. After establishing an SVM classification model by training with the normalized kinematics-related parameters of peak angle and peak velocity, the testing dataset was assigned to predict classification of paralytic movements. Results High separation accuracy was obtained (mean 0.863; 95% confidence interval 0.857–0.869; p = 0.006). Conclusion This study highlights the ability of artificial intelligence to assist physicians and therapists evaluating hand movement recovery of stroke patients.

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