scholarly journals Leaf Image-Based Plant Disease Identification Using Color and Texture Features

2021 ◽  
Author(s):  
Nisar Ahmad ◽  
Hafiz Muhammad Shahzad Asif ◽  
Gulshan Saleem ◽  
Muhammad Usman Younus ◽  
Sadia Anwar ◽  
...  
Keyword(s):  
Plant Disease ◽  
Texture Features ◽  
Disease Identification ◽  
Leaf Image ◽  
Color And Texture Features

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.

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  

Melanoma detection using color and texture features in computer vision systems

2019 ◽  
Vol 4 (5) ◽  
pp. 16-22 ◽  
Author(s):  
Antonio Fuduli ◽  
Pierangelo Veltri ◽  
Eugenio Vocaturo ◽  
Ester Zumpano
Keyword(s):  
Computer Vision ◽  
Texture Features ◽  
Vision Systems ◽  
Melanoma Detection ◽  
Computer Vision Systems ◽  
Color And Texture Features
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