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

scholarly journals Leaf Image Recognition Based on Bag of Features

2020 ◽  
Vol 10 (15) ◽  
pp. 5177 ◽  
Author(s):  
Yaonan Zhang ◽  
Jing Cui ◽  
Zhaobin Wang ◽  
Jianfang Kang ◽  
Yufang Min
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Human Life ◽  
Texture Features ◽  
Output Pulse ◽  
Support Vector ◽  
Linear Discriminant ◽  
Bag Of Features ◽  
Good Ability ◽  
Leaf Image

Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods.

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  

scholarly journals A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bhagya M. Patil ◽  
Vishwanath Burkpalli
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Texture Features ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Cotton Leaf ◽  
K Nearest Neighbor ◽  
Color Features ◽  
Leaf Image

Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton leaf image database was used to segment the images from the natural background using modified factorization-based active contour method. First, the color and texture features are extracted from segmented images. Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. Four color features and eight texture features were extracted, and experimentation was done using three cases: (1) only color features, (2) only texture features, and (3) both color and texture features. The performance of classifiers was better when color features are extracted compared to texture feature extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. The accuracies of classifiers such as support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor are 93.38%, 90.91%, 95.86%, 92.56%, and 94.21%, respectively, whereas that of the multilayer perceptron classifier is 96.69%.

scholarly journals A Comparative Analysis of Kernel-Based Target Tracking Methods using Different Colour Feature Based Target Models

2012 ◽  
pp. 106-111
Author(s):  
Nirja Magoch Thakur ◽  
MM Kuber
Keyword(s):  
Comparative Analysis ◽  
Target Tracking ◽  
Tracking System ◽  
Texture Features ◽  
Feature Space ◽  
Color Feature ◽  
Feature Based ◽  
Real Time Tracking ◽  
Colour Feature ◽  
Effective Use

An effective target modeling is the root of a robust and efficient tracking system. Color feature is widely used feature space for target modeling in real time tracking applications because of its computational efficiency and invariance towards change in shape, scale and rotation. The effective use of this feature with kernel-based target tracking can lead to a robust tracking system. This paper provides a comparative analysis of the performance of three variants of kernel-based tracking system using color feature. The simulation results show that the target modeling using transformed background weighted target model will perform efficiently when initialized target has similar color feature with background while the combination of color-texture will be more accurate and robust when texture features are prominently present.

scholarly journals Classification Techniques for Plant Disease Detection

2020 ◽  
Vol 8 (6) ◽  
pp. 5423-5430
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Plant Disease ◽  
Agricultural Sector ◽  
Early Stage ◽  
Support Vector ◽  
Disease Detection ◽  
Neural Network Approach ◽  
Traditional Approaches

Production of crops with better quality is the necessary attribute for the economic growth of any country. The agricultural sector provides employment to many people and accounts for major portion of gross domestic product in many countries around the world. Therefore, for enhanced agricultural productivity the detection of diseases in plants at an early stage is quite significant. The traditional approaches for disease detection in plants required considerable amount of time, intense research, and constant monitoring of the farm. However, optimized solutions have been obtained over the past few years due to technological advances that have resulted in better yields for the farmers. Machine learning and image processing are used to detect the disease on the agricultural harvest. The image processing steps for plant disease identification include acquiring of images, pre-processing, segmentation and feature extraction. In this review paper, we focused mainly on the most utilized classification mechanisms in disease detection of plants such as Convolutional Neural Network, Support Vector Machine, KNearest Neighbor, and Artificial Neural Network. It has been observed from the analysis that Convolutional Neural Network approach provides better accuracy compared to the traditional approaches.

scholarly journals Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Mingxing Gao ◽  
Xu Wang ◽  
Shoulin Zhu ◽  
Peng Guan
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Driving Safety ◽  
Support Vector ◽  
Cement Concrete ◽  
Transverse Cracks ◽  
Processing Efficiency ◽  
Using Data ◽  
Overlap Rate ◽  
Pothole Detection

Potholes are the most common form of distress on cement concrete pavements, which can compromise pavement safety and ridability. Thus, timely and accurate pothole detection is an important task in developing proper maintenance strategies and ensuring driving safety. This paper proposes a method of integrating the processing of grayscale and texture features. This method mainly combines industrial camera to realize rapid and accurate detection of pothole. Image processing techniques including texture filters, image grayscale, morphology, and extraction of the maximum connected domain are used synergistically to extract useful features from digital images. A machine learning model based on the library for support vector machine (LIBSVM) is constructed to distinguish potholes from longitudinal cracks, transverse cracks, and complex cracks. The method is validated using data collected from agricultural and pastoral areas of Inner Mongolia, China. The comprehensive experiments for recognition of potholes show that the recall, precision, and F1-Score achieved are 100%, 97.4%, and 98.7%, respectively. In addition, the overlap rate between the extracted pothole region and the original image is estimated. Images with an overlap rate greater than 90% accounted for 76.8% of the total image, and images with an overlap rate greater than 80% accounted for 94% of the total image. A comparison discloses that the proposed approach is superior to the existing method not only from the perspective of the accuracy of pothole detection but also from the perspective of the segmentation effect and processing efficiency.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification

Classification of Red Blood Cell Morphology Using Image Processing and Support Vector Machine

2020 ◽  
Author(s):  
Paul Daniel C. Divina ◽  
John Philip T. Felices ◽  
Carlos C. Hortinela ◽  
Janette C. Fausto ◽  
Flordeliza L. Valiente ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Blood Cell ◽  
Cell Morphology ◽  
Red Blood Cell ◽  
Support Vector ◽  
Blood Cell Morphology
Sign in / Sign up

Export Citation Format

Share Document