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Semi-automatic leaf disease detection and classification system for soybean culture

IET IMAGE PROCESSING 2018年第6期12卷 1038-1048页

作者： Kaur, Sukhvir Pandey, Shreelekha Goel, Shivani Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala 147004 Punjab India Bennett Univ Comp Sci & Engn Dept Greater Noida UP India

Development of automatic disease detection and classification system is significantly explored in precision agriculture. In the past few decades, researchers have studied several cultures exploiting different parts of a plant. A similar study is performed for Soybean using leaf images. A rule based semi-automatic system using concepts of k-means is designed and implemented to distinguish healthy leaves from diseased leaves. In addition, a diseased leaf is classified into one of the three categories (downy mildew, frog eye, and Septoria leaf blight). Experiments are performed by separately utilising colour features, texture features, and their combinations to train three models based on support vector machine classifier. Results are generated using thousands of images collected from PlantVillage dataset. Acceptable average accuracy values are reported for all the considered combinations which are also found to be better than existing ones. This study also attempts to discover the best performing feature set for leaf disease detection in Soybean. The system is shown to efficiently compute the disease severity as well. Visual examination of leaf samples further proves the suitability of the proposed system for detection, classification, and severity calculation.

关键词： plant diseases image classification agricultural engineering image colour analysis image texture support vector machines food products feature extraction semiautomatic leaf disease detection and classification system soybean culture automatic disease detection and classification system precision agriculture leaf image k-means concepts colour feature texture feature support vector machine classifier PlantVillage dataset feature set

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Data Mining Applications in Understanding Electricity Consumers' Behavior: A Case Study of Tulkarm District, Palestine

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ENERGIES 2019年第22期12卷 4287-4287页

作者： AbuBaker, Maher An Najah Natl Univ Dept Management Informat Syst Fac Engn & Informat Technol Nablus Palestine An Najah Natl Univ POB 7 Nablus Palestine

This paper presents a comprehensive data analysis and visualization of electricity consumers' prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers' prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers' behavior in electricity consumption and their behavior in charging their electricity meter's smart cards in terms of quantities charged and charging frequencies in different time periods, areas and tariffs are used. Understanding consumers' behavior will support planning and decision making at strategic, tactical and operational levels. This analysis is useful for predicting and forecasting future demand with a certain degree of accuracy. Monthly, weekly, daily and hourly time periods are covered in the analysis. Outliers detection using visualization tools such as box plot is applied. K-means unsupervised machine learning clustering algorithm is implemented. The support vector machine classification method is applied. As a result of this study, electricity consumers' behavior in different areas, tariffs and timing periods is understood and presented by numbers and graphs and new electricity consumer segmentation is proposed.

关键词： data mining data visualization K-means clustering support vector machine classifier principal components analysis elbow method silhouette analysis

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Extraction of Urban Objects in Cloud Shadows on the basis of Fusion of Airborne LiDAR and Hyperspectral Data

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REMOTE SENSING 2019年第6期11卷 713-713页

作者： Man, Qixia Dong, Pinliang Shandong Normal Univ Coll Geog & Environm Jinan 250014 Shandong Peoples R China Univ North Texas Dept Geog & Environm Denton TX 76203 USA

Feature extraction in cloud shadows is a difficult problem in the field of optical remote sensing. The key to solving this problem is to improve the accuracy of classification algorithms by fusing multi-source remotely sensed data. Hyperspectral data have rich spectral information but highly suffer from cloud shadows, whereas light detection and ranging (LiDAR) data can be acquired from beneath clouds to provide accurate height information. In this study, fused airborne LiDAR and hyperspectral data were used to extract urban objects in cloud shadows using the following steps: (1) a series of LiDAR and hyperspectral metrics were extracted and selected;(2) cloud shadows were extracted;(3) the new proposed approach was used by combining a pixel-based support vector machine (SVM) and object-based classifiers to extract urban objects in cloud shadows;(4) a pixel-based SVM classifier was used for the classification of the whole study area with the selected metrics;(5) a decision-fusion strategy was employed to get the final results for the whole study area;(6) accuracy assessment was conducted. Compared with the SVM classification results, the decision-fusion results of the combined SVM and object-based classifiers show that the overall classification accuracy is improved by 5.00% (from 87.30% to 92.30%). The experimental results confirm that the proposed method is very effective for urban object extraction in cloud shadows and thus improve urban applications such as urban green land management, land use analysis, and impervious surface assessment.

关键词： LiDAR data hyperspectral data shadow extraction decision fusion multi-resolution object-based classifier support vector machine classifier

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WiFi-based passive sensing system for human presence and activity event classification

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IET WIRELESS SENSOR SYSTEMS 2018年第6期8卷 276-283页

作者： Li, Wenda Tan, Bo Piechocki, Robert J. Univ Bristol Dept Elect & Elect Engn Bristol Avon England Coventry Univ Sch Comp Elect & Math Coventry W Midlands England

Detection of human presence and activity event classification are of importance to a variety of context-awareness applications such as e-Healthcare, security, and low impact building. However, existing radio frequency identification tags, wearables, and passive infrared approaches require the user to carry dedicated electronic devices that suffer from problems of low detection accuracy and false alarms. This study proposes a novel system for non-invasive human sensing by analysing the Doppler information contained in the human reflections of WiFi signal. Doppler information is insensitive to stationary objects, thus there is no need for any scenario-specific calibration which makes it ideal for human sensing. We also introduce the time-frequency domain feature vectors of WiFi Doppler information for the support vector machine classifier towards activity event recognition. The proposed methodology is evaluated on a software defined radio system together with the experiment of five different events. The results indicate that the proposed system is sufficient for indoor context awareness, with 95.3% overall accuracy for event classification and 93.3% accuracy for human presence detection, which outperforms the traditional received signal strength approach where accuracy is 69.3% for event classification and 83.3% for human presence detection.

关键词： software radio wireless LAN ubiquitous computing support vector machines wireless sensor networks Doppler effect calibration time-frequency analysis signal classification vectors WiFi-based passive sensing system activity event classification passive infrared approaches noninvasive human sensing WiFi signal time-frequency domain feature vectors activity event recognition software defined radio system human presence detection radiofrequency identification tags e-healthcare electronic devices WiFi Doppler information analysis calibration support vector machine classifier indoor context-awareness applications received signal strength approach

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Activity recognition with cooperative radar systems at C and K band

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JOURNAL OF ENGINEERING-JOE 2019年第20期2019卷 7100-7104页

作者： Shrestha, Aman Li, Haobo Fioranelli, Francesco Le Kernec, Julien Univ Glasgow Sch Engn Commun Sensing & Imaging Grp Glasgow Lanark Scotland Univ Elect Sci & Technol China Sch Informat & Elect Chengdu Sichuan Peoples R China

Remote health monitoring is a key component in the future of healthcare with predictive and fall risk estimation applications required in great need and with urgency. Radar, through the exploitation of the micro-Doppler effect, is able to generate signatures that can be classified automatically. In this work, features from two different radar systems operating at C band and K band have been used together co-operatively to classify ten indoor human activities with data from 20 subjects with a support vector machine classifier. Feature selection has been applied to remove redundancies and find a set of salient features for the radar systems, individually and in the fused scenario. Using the aforementioned methods, we show improvements in the classification accuracy for the systems from 75 and 70% for the radar systems individually, up to 89% when fused.

关键词： support vector machines Doppler radar pattern classification feature extraction cooperative communication feature selection radar imaging health care radar computing cooperative radar systems salient features feature selection support vector machine classifier indoor human activities microDoppler effect risk estimation applications remote health monitoring activity recognition

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Target classification using Renyi entropy features of cyclic bispectrum

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JOURNAL OF ENGINEERING-JOE 2019年第21期2019卷 7717-7720页

作者： Wang, Cai Li, Yan Gao, Meiguo Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China

In this study, a target classification method is proposed based on a third-order cyclic statistics technique. The authors introduce cyclic bispectrum (CBS) to reveal the non-linear cyclic nature contained by the micro-Doppler signal, and it is observed that the non-zero peaks generated by some cyclic non-linear nature form unique distribution patterns on CBS slices for different targets. Then, a Renyi entropy is calculated for each CBS slice to measure the information content and thus achieve an entropy sequence. Subsequently, considering the entropy sequence as a feature vector, the support vector machine classifier is used to perform the target classification. Experimental results based on real measured data validate the effectiveness of the method.

关键词： entropy feature extraction support vector machines signal classification pattern classification statistical analysis target classification method third-order cyclic statistics technique microDoppler signal nonzero peaks cyclic nonlinear nature form unique distribution patterns CBS slice entropy sequence feature vector Renyi entropy features cyclic bispectrum support vector machine classifier

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Identification of winter land use in temperate agricultural landscapes based on Sentinel-1 and 2 times-series. 38

Identification of winter land use in temperate agricultural ...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Denize, Julien Hubert-Moy, Laurence Corgne, Samuel Betbeder, Julie Pottier, Eric Univ Rennes Rennes France IETR UMR 6164 Rennes France LETG UMR 6554 Rennes France Cirad UPR 105 Forests & Soc Montpellier France

ISBN: (纸本)9781538671504

Land cover and land use monitoring, particularly during winter season, is still a major environmental and scientific issue in agricultural areas. From an environmental point of view, the presence and type of vegetation cover in winter have an impact on pollutant transport to water bodies. From a methodological point of view, characterizing spatio-temporal dynamics of winter land cover and land use at a field scale remains a challenge due to the diversity of farming strategies and practices. The objective of this study was to evaluate the potential of optical and SAR time-series to improve the monitoring of winter land use in an area of 130 km(2). For that purpose, Sentinel-1 and 2 time-series were classified using SVM and RF algorithms. Winter land use was identified with an overall accuracy of 81% and a kappa index of 0.77 from a combination of Sentinel-1 and 2 images.

关键词： Agricultural monitoring Multi-temporal classifications support vector machine classifier Random Forest classifier Earth observing sensors optical and SAR time series

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Manufacturing Services Classification in a Decentralized Supply Chain Using Text Mining 1

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17th International Conference on Hybrid Intelligent Systems (HIS)

作者： Akhtar, M. D. Manupati, V. K. Varela, M. L. R. Putnik, G. D. Madureira, A. M. Abraham, Ajith VIT Univ Sch Mech Engn Div Mfg Vellore Tamil Nadu India Univ Minho Sch Engn Dept Prod & Syst Guimaraes Portugal Polytech Inst Porto ISEP IPP Porto Portugal Sci Network Innovat & Res Excellence Machine Intelligence Res Labs Washington DC 98071 USA

ISBN: (数字)9783319763514

ISBN: (纸本)9783319763514;9783319763507

With the recent development of weblogs and social networks, many supplier industries share their data on different websites and weblogs. Even the Small-to-Medium sized enterprises (SMEs) in the manufacturing sector (as well as non-manufacturing sector) are rapidly strengthening their web presence in order to improve their visibility, customer reachability and remain competitive in the global market. Our study aims to classify data into various groups so that users can identify the most appropriate content based on their choice at any given time. To classify and characterize manufacturing suppliers in supply chain through their capability narratives and textual portfolios obtained from websites of such suppliers online source portals for testing and Naive Bayes and support vector machine (SVM) Classification method at term-level for classification has been used. The performance of the proposed classifier was tested experimentally based on the standard metrics such as precision, recall, and F-measure.

关键词： Classification Naive bayes classifier support vector machine classifier Small and medium scale enterprises

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Thermal Face Recognition Under Disguised Conditions

Thermal Face Recognition Under Disguised Conditions

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International Conference on machine Learning and Cybernetics (ICMLC)

作者： Shinfeng D. Lin Kuanyuan Chen National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9781728128177

A thermal face recognition under disguised conditions using model fusion is proposed. The proposed model fusion has three main approaches: Linear support vector machine classifier (Linear SVC), Convolutional Neural Network (CNN) and Ordinary Least Square (OLS). A grid of 22 thermal face points based on physiological information is extracted for training Linear SVC. The support vectors of testing images using Linear SVC are calculated to find the hyperplane for classification. The novelty is that we firstly apply temperature information in face recognition. In CNN model, the multiple layers of convolutional layer are utilized to extract more effective features. The fully connected layer (FC layer) is trained using the feature matrix of the last convolutional layer. This FC layer is then a classifier to identify the category of the test image. In the training phase, the predicted values from above two approaches are provided to the OLS for linear regression. The OLS assigns weighting values to these two approaches. This can effectively compensate for the advantages and disadvantages of two approaches. In addition to the comparison with the traditional thermal face recognition, an experiment under disguised conditions was conducted. Experimental results of the proposed method outperform the existing methods.

关键词： Thermal face recognition Thermal image support vector machine classifier Convolutional neural network Ordinary least square Facial Recognition THERMAL IMAGES Thermal Static VAr compensators second approximation support vector Network classifiers Hyperplane

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Predict Driver Fatigue Using Facial Features

Predict Driver Fatigue Using Facial Features

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Berkati, Oussama Srifi, Mohamed Nabil Ibn Tofail Univ Natl Sch Appl Sci Elect & Telecommun Res Grp Kenitra Morocco

ISBN: (纸本)9781538673287

Fatigue on board is amoung the causes of fatal accidents today, however drained driver means that there a metal box without control that threatens lives in our roads. Nowadays, there is no effective non-intrusive method to detect driver tiredness. This paper presents a method for early prediction signs of fatigue during driving. Luckily our face characteristics reflects our current state, therefore we will base on facial features to monitor the driver state. To localize driver's face we had chosen Viola-Jones for faces detection and for face tracking Kanade-Lucas-Tomasi is the best alternative due to their implementation simplicity using just standard phone camera equipped by IR LED. Then all extracted frame characteristics will be presented to SVM for classification that separate the normal state from the critical state. Our objective is to avoid false alerts and early fatigue detection in real-time, for this reason we will combine HOG+SVM, eyes blink rate/duration and PERCLOS. The driver state detection and fatigue alert are not the final steps in our method because a bad reaction can cause disasters that is why we include different road users notification message.

关键词： Fatigue detection support vector machine classifier Viola-Jones Kanade-Lucas-Tomasi Histograms of Oriented Gradients

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