scholarly journals An Agricultural Tele-Monitoring Method in Detecting Nutrient Deficiencies of Oil Palm Leaf

2018 ◽  
Vol 7 (4.11) ◽  
pp. 266
Author(s):  
Muhammad Asraf H. ◽  
Nooritawati Md Tahir ◽  
Nur Dalila K.A. ◽  
Aini Hussain
Keyword(s):  
Oil Palm ◽  
Predictive Value ◽  
Nutrient Management ◽  
Absolute Error ◽  
Performance Measure ◽  
Image Processing Technique ◽  
Nutritional Deficiencies ◽  
Support Vector ◽  
Nutrient Deficiencies ◽  
Monitoring Method

Nutrient management in oil palm plantation is considered as one of the prominent issues especially for smallholder farmer. The nutrient contained in the tress has always been neglected and untreated and these may cause the trees to suffer from nutrient deficiencies. Therefore, in leveraging the oil yield at the maximum, a telemonitoring system is developed to assess and monitor the lack of nutrients for respective trees. This is done using image processing technique and artificial intelligence in detecting the nutritional deficiencies by analyzing the leaf. The categorization focused by classifying into four major types either as magnesium deficiencies, potassium deficiencies, nitrogen deficiencies or healthy that is based on the oil palm’s leaf surface. This is achieved by extracting the features namely number of red pixels, entropy and correlations. Further, two classifiers specifically support vector machine and artificial neural network is used for classification purpose along with performance measure using accuracy(ACC), Mean Square Error (MSE), Mean Absolute Error (MAE), Sensitivity (SN), Specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV) based on ten-fold cross-validation. Results attained showed that the best classifier is SVM using RBF kernel (SVM-RBF) that is capable to accurately recognize the nutrient deficiencies with 100% accuracy. 

scholarly journals An Agricultural Tele-Monitoring Method in Detecting Nutrient Deficiencies of Oil Palm Leaf

2018 ◽  
Vol 7 (4.11) ◽  
pp. 227
Author(s):  
Muhammad Asraf H ◽  
Nooritawati Md Tahir ◽  
Nur Dalila K.A. ◽  
Aini Hussain
Keyword(s):  
Oil Palm ◽  
Predictive Value ◽  
Nutrient Management ◽  
Absolute Error ◽  
Performance Measure ◽  
Image Processing Technique ◽  
Nutritional Deficiencies ◽  
Support Vector ◽  
Nutrient Deficiencies ◽  
Monitoring Method

Nutrient management in oil palm plantation is considered as one of the prominent issues especially for smallholder farmer. The nutrient contained in the tress has always been neglected and untreated and these may cause the trees to suffer from nutrient deficiencies. Therefore, in leveraging the oil yield at the maximum, a telemonitoring system is developed to assess and monitor the lack of nutrients for respective trees. This is done using image processing technique and artificial intelligence in detecting the nutritional deficiencies by analyzing the leaf. The categorization focused by classifying into four major types either as magnesium deficiencies, potassium deficiencies, nitrogen deficiencies or healthy that is based on the oil palm’s leaf surface. This is achieved by extracting the features namely number of red pixels, entropy and correlations. Further, two classifiers specifically support vector machine and artificial neural network is used for classification purpose along with performance measure using accuracy(ACC), Mean Square Error (MSE), Mean Absolute Error (MAE), Sensitivity (SN), Specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV) based on ten-fold cross-validation. Results attained showed that the best classifier is SVM using RBF kernel (SVM-RBF) that is capable to accurately recognize the nutrient deficiencies with 100% accuracy. 

scholarly journals Classification of Leaf Disease from Image Processing Technique

2018 ◽  
Vol 10 (1) ◽  
pp. 191 ◽  
Author(s):  
Mahanijah Md Kamal ◽  
Ahmad Nor Ikhwan Masazhar ◽  
Farah Abdul Rahman
Keyword(s):  
Image Processing ◽  
Palm Oil ◽  
Oil Palm ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Support Vector ◽  
Leaf Disease ◽  
Clustering And Classification ◽  
Oil Sector ◽  
Palm Leaf

<p class="Abstract">Disease in palm oil sector is one of the major concerns because it affects the production and economy losses to Malaysia. Diseases appear as spots on the leaf and if not treated on time, cause the growth of the palm oil tree. This work presents the use of digital image processing technique for classification oil palm leaf disease sympthoms. Chimaera and Anthracnose is the most common symtoms infected the oil palm leaf in nursery stage. Here, support vector machine (SVM) acts as a classifier where there are four stages involved. The stages are image acquisition, image enhancement, clustering and classification. The classification shows that SVM achieves accuracy of 97% for Chimaera and 95% for Anthracnose.</p>

scholarly journals Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

2021 ◽  
Vol 11 (8) ◽  
pp. 3705
Author(s):  
Jie Zeng ◽  
Panayiotis C. Roussis ◽  
Ahmed Salih Mohammed ◽  
Chrysanthos Maraveas ◽  
Seyed Alireza Fatemi ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Particle Velocity ◽  
Basis Function ◽  
Absolute Error ◽  
Peak Particle Velocity ◽  
Support Vector ◽  
Effective Parameters ◽  
Experimental Database ◽  
Interaction Detection ◽  
Radial Basis

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.

scholarly journals PREDICTING THE VARIABILITY OF COPPER AND ZINC IN LEAF AND SOIL OF OIL PALM PLANTED ON A 12 HA LAND USING GEOSPATIAL INFORMATION SYSTEM TECHNOLOGY

2015 ◽  
Vol 77 (24) ◽  
Author(s):  
Nur Shuhada Tajudin ◽  
Mohamed Hanafi Musa ◽  
Idris Abu Seman ◽  
Sivakumar Balasundram
Keyword(s):  
Information System ◽  
Oil Palm ◽  
Nutrient Management ◽  
Elaeis Guineensis ◽  
Management Practice ◽  
Zinc Content ◽  
Crop Productivity ◽  
Low Fertility ◽  
Management Approach ◽  
Geospatial Information

Oil palm (Elaeis guineensis) is an important economic tree crops in the tropic. However, more than 95 % of oil palms grown in Southeast Asia are on acid, low fertility and highly weathered soils. Optimum value of micronutrients in the soil was required to enhance the efficiency of use of macro-nutrients. Hence, to observe and predict the fertility status of the oil palm plantation area, a 12 hectare study site was used and a total of 60 geo-referenced soil and leaf samples were collected for determinations of pH and selected micronutrients of Cu and Zn content. The data were explored and mapped using geostatistic and Geographic Information System (GIS). The study area had acidic type of soil with pH ranged from 3.25-5.85. The analysis showed that almost 78% of the study area had high content of Cu in soil, while another 22% of area was low to moderate in Cu. However, Cu content in leaf were categorized as insufficient as 100% of the area was observed to have Cu less than 3 ppm. About 80% of the study area showed a low to moderate content of Zn in soil, while another 20% of area showed a high content of Zn. Zinc content in leaf ranged from optimum to high categories. However, this value did not reach the excess level of Zn (50 ppm). These results suggest that, this plantation area need a site specific management approach in order to increase its crop productivity in regards to nutrient management. As a preliminary recommendation, a zone management practice would be applied in future as it is beneficial in term of protecting the environment from excessive fertilizer.

scholarly journals Novel Genetic Variants of Hepatitis B Virus in Fulminant Hepatitis

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Jack Bee Chook ◽  
Yun Fong Ngeow ◽  
Kok Keng Tee ◽  
Suat Cheng Peh ◽  
Rosmawati Mohamed
Keyword(s):  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Predictive Value ◽  
Fulminant Hepatitis ◽  
Predictive Accuracy ◽  
Stop Codon ◽  
Acute Infection ◽  
Support Vector ◽  
Svm Model ◽  
B Virus

Fulminant hepatitis (FH) is a life-threatening liver disease characterised by intense immune attack and massive liver cell death. The common precore stop codon mutation of hepatitis B virus (HBV), A1896, is frequently associated with FH, but lacks specificity. This study attempts to uncover all possible viral nucleotides that are specifically associated with FH through a compiled sequence analysis of FH and non-FH cases from acute infection. We retrieved 67 FH and 280 acute non-FH cases of hepatitis B from GenBank and applied support vector machine (SVM) model to seek candidate nucleotides highly predictive of FH. Six best candidates with top predictive accuracy, 92.5%, were used to build a SVM model; they are C2129 (85.3%), T720 (83.0%), Y2131 (82.4%), T2013 (82.1%), K2048 (82.1%), and A2512 (82.1%). This model gave a high specificity (99.3%), positive predictive value (95.6%), and negative predictive value (92.1%), but only moderate sensitivity (64.2%). We successfully built a SVM model comprising six variants that are highly predictive and specific for FH: four in the core region and one each in the polymerase and the surface regions. These variants indicate that intracellular virion/core retention could play an important role in the progression to FH.

scholarly journals Machine learning for identification of surgeries with high risks of cancellation

2018 ◽  
Vol 26 (1) ◽  
pp. 141-155 ◽  
Author(s):  
Li Luo ◽  
Fengyi Zhang ◽  
Yao Yao ◽  
RenRong Gong ◽  
Martina Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Sampling Methods ◽  
Characteristic Curve ◽  
Support Vector ◽  
Chi Square ◽  
Stable Performance ◽  
Operating Characteristic Curve

Surgery cancellations waste scarce operative resources and hinder patients’ access to operative services. In this study, the Wilcoxon and chi-square tests were used for predictor selection, and three machine learning models – random forest, support vector machine, and XGBoost – were used for the identification of surgeries with high risks of cancellation. The optimal performances of the identification models were as follows: sensitivity − 0.615; specificity − 0.957; positive predictive value − 0.454; negative predictive value − 0.904; accuracy − 0.647; and area under the receiver operating characteristic curve − 0.682. Of the three models, the random forest model achieved the best performance. Thus, the effective identification of surgeries with high risks of cancellation is feasible with stable performance. Models and sampling methods significantly affect the performance of identification. This study is a new application of machine learning for the identification of surgeries with high risks of cancellation and facilitation of surgery resource management.

scholarly journals Feature Extraction in Six Blocks to Detect and Recognize English Numbers

2021 ◽  
pp. 3790-3803
Author(s):  
Heba Kh. Abbas ◽  
Haidar J. Mohamad
Keyword(s):  
Fuzzy Logic ◽  
Feature Extraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Suggested Method ◽  
Support Vector ◽  
Horizontal Line ◽  
Svm Algorithm ◽  
Fuzzy Logic Method ◽  
Logic Method

    The Fuzzy Logic method was implemented to detect and recognize English numbers in this paper. The extracted features within this method make the detection easy and accurate. These features depend on the crossing point of two vertical lines with one horizontal line to be used from the Fuzzy logic method, as shown by the Matlab code in this study. The font types are Times New Roman, Arial, Calabria, Arabic, and Andalus with different font sizes of 10, 16, 22, 28, 36, 42, 50 and 72. These numbers are isolated automatically with the designed algorithm, for which the code is also presented. The number’s image is tested with the Fuzzy algorithm depending on six-block properties only. Groups of regions (High, Medium, and Low) for each number showed unique behavior to recognize any number. Normalized Absolute Error (NAE) equation was used to evaluate the error percentage for the suggested algorithm. The lowest error was 0.001% compared with the real number. The data were checked by the support vector machine (SVM) algorithm to confirm the quality and the efficiency of the suggested method, where the matching was found to be 100% between the data of the suggested method and SVM. The six properties offer a new method to build a rule-based feature extraction technique in different applications and detect any text recognition with a low computational cost.

scholarly journals MACHINE LEARNING METHODS IN MONITORING OPERATING BEHAVIOUR OF MARINE TWO-STROKE DIESEL ENGINE

Transport ◽  
2020 ◽  
Vol 35 (5) ◽  
pp. 462-473
Author(s):  
Aleksandar Vorkapić ◽  
Radoslav Radonja ◽  
Karlo Babić ◽  
Sanda Martinčić-Ipšić
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Fuel Consumption ◽  
Performance Monitoring ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Operating Parameters ◽  
Detection Model ◽  
Modelling Framework

The aim of this article is to enhance performance monitoring of a two-stroke electronically controlled ship propulsion engine on the operating envelope. This is achieved by setting up a machine learning model capable of monitoring influential operating parameters and predicting the fuel consumption. Model is tested with different machine learning algorithms, namely linear regression, multilayer perceptron, Support Vector Machines (SVM) and Random Forests (RF). Upon verification of modelling framework and analysing the results in order to improve the prediction accuracy, the best algorithm is selected based on standard evaluation metrics, i.e. Root Mean Square Error (RMSE) and Relative Absolute Error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant sensory data, SVM exhibit the lowest RMSE 7.1032 and RAE 0.5313%. RF achieve the lowest RMSE 22.6137 and RAE 3.8545% in a setting when minimal number of input variables is considered, i.e. cylinder indicated pressures and propulsion engine revolutions. Further, article deals with the detection of anomalies of operating parameters, which enables the evaluation of the propulsion engine condition and the early identification of failures and deterioration. Such a time-dependent, self-adopting anomaly detection model can be used for comparison with the initial condition recorded during the test and sea run or after survey and docking. Finally, we propose a unified model structure, incorporating fuel consumption prediction and anomaly detection model with on-board decision-making process regarding navigation and maintenance.

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