scholarly journals The use of an extended set of key texture features Haralick in the diagnosis of plant diseases on leaf images

2019 ◽  
Vol 25 ◽  
pp. 122-127
Author(s):  
V. S. Tutygin ◽  
Х. М. А. Al-Vindi Basim ◽  
D. O. Leliuhin
Keyword(s):  
Texture Features ◽  
Plant Diseases

scholarly journals Recognition and classification of Produce affected by identically looking Powdery Mildew disease

2014 ◽  
Vol 17 (2) ◽  
pp. 29-34 ◽  
Author(s):  
Jagadeesh D. Pujari ◽  
Rajesh Yakkundimath ◽  
Abdulmunaf S. Byadgi
Keyword(s):  
Powdery Mildew ◽  
Classification Accuracy ◽  
Texture Features ◽  
Plant Diseases ◽  
Human Beings ◽  
Average Classification Accuracy ◽  
Knowledge Based ◽  
Saturation Intensity ◽  
Combined Features ◽  
Occurrence Matrix

Abstract Plant diseases are visually observable patterns of a particular plant. Varieties of plant diseases, which are recognized by human beings, are identical or look similar in appearance. In this paper, we have considered recognition of fungal disease symptom like powdery mildew, looking similar in appearance affected on different produce. The powdery mildew symptom affected on produce like grape, mango, chili, wheat, beans and sunflower are considered for classification. Colour and texture features are extracted from image samples of produce affected by powdery mildew symptom. The extracted features are then used as inputs to knowledge-based and artificial neural network (ANN) classifiers and tests are performed to classify image samples. The colour analysis is done using Red, Green, Blue (RGB) and Hue, Saturation, Intensity (HSI) models. Texture analysis is done using gray level co-occurrence matrix (GLCM). The overall average classification accuracy with colour, texture and combined features are 70.48 %, 70.07 % and 76.61 % respectively using the ANN classifier. The overall average classification accuracy has increased to 71.92 %, 80.60 % and 87.80 % with colour, texture and combined features respectively using the knowledge-based classifier.

scholarly journals Leaf Diseases Classification on Peanut Leaves Based on Texture and Colour Features

2021 ◽  
Vol 10 (6) ◽  
pp. 149-155
Author(s):  
Brahma Ratih Rahayu F. ◽  
Panca Mudjirahardjo ◽  
Muhammad Aziz Muslim
Keyword(s):  
Technology Development ◽  
Texture Features ◽  
Plant Diseases ◽  
Food Crop ◽  
Normal Leaf ◽  
Colour Space ◽  
Colour Features ◽  
Hsv Colour Space ◽  
Multiclass Svm ◽  
Development Application

Peanuts are a food crop commodity that Indonesians widely consume as a vegetable fat and protein source. However, the quality and quantity of peanut productivity may decline, one of which is due to plant diseases. Efforts that can be made to maintain peanut productivity are the application of technology to detect peanut plant diseases early; thus, disease control can be carried out earlier. This study presents a technology development application, particularly digital image processing, to identify disease features of infected peanut leaves based on GLCM texture features and colour features in the HSV colour space and classified using the SVM method. The development of the SVM method that is applied is the Multiclass SVM with the DAGSVM strategy, which can classify more than two classes. Based on the experimental results, it confirms that the combination of HSV colour features and GLCM texture features with an angular orientation of 0 degrees and classified by the Multiclass SVM method with polynomial kernels produces the highest accuracy, i.e. 99.1667% for leaf spot class, 97.5% for leaf rust class, 98.8333% for eyespot class, 100% for normal leaf class and 100% for other leaf class.

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.

Spraying for the Prevention of Plant Diseases

1892 ◽  
Vol 33 (853supp) ◽  
pp. 13635-13636
Author(s):  
Joseph F. James
Keyword(s):  
Plant Diseases

Image processing in plant diseases detection

2019 ◽  
Vol 1 (2) ◽  
pp. 10-15
Author(s):  
K Santhasheela ◽  
Deepan Chakravarthi AV
Keyword(s):  
Image Processing ◽  
Plant Diseases

Distinction of Wet Road Surface Condition at Night-time using Texture Features

2012 ◽  
Vol 132 (9) ◽  
pp. 1488-1493 ◽  
Author(s):  
Keiji Shibata ◽  
Tatsuya Furukane ◽  
Shohei Kawai ◽  
Yuukou Horita
Keyword(s):  
Surface Condition ◽  
Texture Features ◽  
Road Surface ◽  
Night Time

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.

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