scholarly journals Analysis of Leaf Features in Chili Plants Using Automated Color Equalization (ACE)

2018 ◽  
Vol 7 (2.13) ◽  
pp. 457
Author(s):  
Basiroh . ◽  
Nuning Kurniasih ◽  
Dian Asmara Jati ◽  
Nina Zulida Situmorang ◽  
Heni Sukrisno ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Plant Disease ◽  
Texture Features ◽  
Support Vector ◽  
Color Feature ◽  
Disease Identification ◽  
Agricultural Yield

Chili is a variety of crop groups that have promising business prospects. To obtain optimal agricultural yield, then the process of plant care and how to planting should be maximal. Constraints often experienced by farmers in the process of planting chili in Magelang regency of Indonesia is a disease of yellow leaves. Some diseases in plants can be identified using precision technology, one of them is by using an image or image-based technology. In previous studies, no one has analyzed using feature extraction using ACE as an analysis to detect plant disease in chili. In this study will extract features using Automated Color Equalization (ACE) which is then classified using SVM (Support Vector Machine) for disease identification based on its leaves. With this method, the accuracy of the extraction results in a combination of 80% texture features, color feature extraction, and a combination of 80% color feature texture  

Desease Identification In Plant Leaf Image of Chili (Capsicum Annum (L)) Using Image Processing and Automated Colour Equalization (ACE) Algorithm

2018 ◽  
Vol 1 (2) ◽  
pp. 99
Author(s):  
Basiroh Basiroh
Keyword(s):  
Image Processing ◽  
Agricultural Sector ◽  
Texture Features ◽  
Support Vector ◽  
Color Feature ◽  
Colour Feature ◽  
Agricultural Yield ◽  
Accuracy Of Results ◽  
Ace Algorithm ◽  
Leaf Image

The world of agriculture becomes one of the vital objects and one of the promising business prospects. To obtain optimal agricultural yield, the process of plant care and the way of planting should be really - maximal, because the main key in seeking maximum results in terms of quality and quantity. Harvest failures are the least desirable to farmers and crop failures are the number one scariest specter for cultivating farmers. Today's informatics technology has been developed in an effort to support increased yields in the agricultural sector. This study measured the level of accuracy of results ekstraksi texture and colour feature. This research method using SVM classification ( Support Vector Machine ) seeks image processing through analyzing with Automated Color Equalization (ACE). With this method the accuracy of the extraction results a combination of 80% texture features, color feature extraction, and a combination of 80% color feature texture

Classification of Tumor of MRI Brain Image Using Hybrid Feature Extraction Method and Support Vector Machine Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2558-2565
Author(s):  
K. Kavinkumar ◽  
T. Meeradevi
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Extraction Method ◽  
Support Vector Machine Classifier ◽  
Texture Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Tumor Type ◽  
Feature Extraction Method ◽  
Hybrid Feature Extraction

Brain tumors Analysis is problematic somewhat due to varied size, shape, location of tumor and the appearance and presence of brain tumor. Clinicians and radiologist have difficulty in identifying the tumor type. An efficient hybrid feature extraction method to classify the type of tumor accurately as meningioma, gliomas and pituitary tumor using SVM (support vector machine) classifier is proposed. The modified Non-Local Means (NLM) filter may be effectively used to get the pure image. The NLM filter is compared with common filters like median and wiener. From the denoised image the classification is done by training SVM using the texture features from the hybrid and efficient feature extraction technique.The accuracy of the classification is calculated and the SVM classifier training individual type of texture features and also with combined texture features and the performance is analyzed.

Pedestrian Detection Based on Hand-crafted Features and Multi-layer Feature Fused-ResNet Model

2021 ◽  
Vol 30 (05) ◽  
pp. 2150028
Author(s):  
Sweta Panigrahi ◽  
U. S. N. Raju
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Texture Features ◽  
Detection Methods ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Research Areas ◽  
Proposed Modification

One of the most sought-after research areas in object detection is pedestrian detection owing to its applications especially in automated surveillance and robotics. Traditional methods use hand-crafted features to characterize pedestrians. In this work, we have pro-posed a new hand-crafted feature extraction method that concatenates shape, color and texture features; which is then classified by using Support Vector Machine (SVM). As in recent years, deep learning models such as Convolutional Neural Networks (CNNs) have become an eminent state of the art in detection challenges, which unlike the manually designed feature extraction mechanism, results in more accuracy. Therefore, we have also proposed a CNN network, a modification of the pre-trained ResNet-18 named as Multi-layer Feature Fused-ResNet (MF2-ResNet). We have used the proposed modification for (1) feature extraction; which is then classified by using Support Vector Machine (SVM); (2) End-to-End feature extraction and classification by the CNN network and (3) MF2-ResNet based Faster-RCNN to include region proposals in the detection pipeline. To evaluate the proposed method, it is compared with existing pre-trained CNNs. The MF2-ResNet based Faster R-CNN is compared with state-of-the-art detection methods. Three benchmark pedestrian datasets are used in this work: INRIA, NICTA and Daimler.

scholarly journals Leaf Image-based Plant Disease Identification using Color and Texture Features

2021 ◽  
Author(s):  
Nisar Ahmed ◽  
Hafiz Muhammad Shahzad Asif ◽  
Gulshan Saleem ◽  
Muhammad Usman Younus ◽  
Sadia Anwar ◽  
...  
Keyword(s):  
Plant Disease ◽  
Texture Features ◽  
Plant Diseases ◽  
Handheld Devices ◽  
Support Vector ◽  
Prototype System ◽  
Automated Identification ◽  
Disease Identification ◽  
Feature Based ◽  
Occurrence Matrix

Abstract Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time identification is complete. On the other hand, complex deep learning models perform the task with reasonable performance but due to their large size and high computational requirements, they are not suited to mobile and handheld devices. Our proposed approach contributes automated identification of plant diseases which follows a sequence of steps involving pre-processing, segmentation of diseased leaf area, calculation of features based on the Gray-Level Co-occurrence Matrix (GLCM), feature selection and classification. In this study, six color features and twenty-two texture features have been calculated. Support vector machines is used to perform one-vs-one classification of plant disease. The proposed model of disease identification provides an accuracy of 98.79% with a standard deviation of 0.57 on 10-fold cross-validation. The accuracy on a self-collected dataset is 82.47% for disease identification and 91.40% for healthy and diseased classification. The reported performance measures are better or comparable to the existing approaches and highest among the feature-based methods, presenting it as the most suitable method to automated leaf-based plant disease identification. This prototype system can be extended by adding more disease categories or targeting specific crop or disease categories.

Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection

2020 ◽  
Vol 39 (4) ◽  
pp. 4847-4858
Author(s):  
Lei Wang ◽  
Jinhai Sun ◽  
Tuojian Li
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Extraction Method ◽  
Feature Recognition ◽  
Texture Feature ◽  
Texture Features ◽  
Spatial Domain ◽  
Image Texture ◽  
Support Vector ◽  
Texture Feature Extraction

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

2010 ◽  
Vol 36 (3) ◽  
pp. 1503-1510 ◽  
Author(s):  
U. Rajendra Acharya ◽  
E. Y. K. Ng ◽  
Jen-Hong Tan ◽  
S. Vinitha Sree
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Support Vector
Sign in / Sign up

Export Citation Format

Share Document