Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework
When herbs plants has disease, they can display a range of symptoms such as colored spots, or streaks that can occur on the leaves, stems, and seeds of the plant. These visual symptoms continuously change their color, shape and size as the disease progresses. Once the image of a target is captured d...
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| Format: | Article |
| Language: | English English |
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Engg Journals Publications
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/13603/1/Classification%20of%20herbs%20plant%20diseases%20via%20hierachical%20dynamic%20artificial%20neural%20network%20after%20image%20removal%20using%20kernel%20regression%20framework.pdf |
| _version_ | 1796968470294822912 |
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| author | Abdullah, Lili Nurliyana Khalid, Fatimah Borhan, N.M. |
| author_facet | Abdullah, Lili Nurliyana Khalid, Fatimah Borhan, N.M. |
| author_sort | Abdullah, Lili Nurliyana |
| collection | UPM |
| description | When herbs plants has disease, they can display a range of symptoms such as colored spots, or streaks that can occur on the leaves, stems, and seeds of the plant. These visual symptoms continuously change their color, shape and size as the disease progresses. Once the image of a target is captured digitally, a myriad of image processing algorithms can be used to extract features from it. The usefulness
of each of these features will depend on the particular patterns to be highlighted in the image. A key point
in the implementation of optimal classifiers is the selection of features that characterize the image.
Basically, in this study, image processing and pattern classification are going to be used to implement a
machine vision system that could identify and classify the visual symptoms of herb plants diseases. The image processing is divided into four stages: Image Pre-Processing to remove image noises (Fixed-Valued
Impulse Noise, Random-Valued Impulse Noise and Gaussian Noise), Image Segmentation to identify regions in the image that were likely to qualify as diseased region, Image Feature Extraction and Selection to extract and select important image features and Image Classification to classify the image into different herbs diseases classes. This paper is to propose an unsupervised diseases pattern recognition and classification algorithm that is based on a modified Hierarchical Dynamic Artificial Neural Network which provides an adjustable sensitivity-specificity herbs diseases detection and classification from the analysis of noise-free colored herbs images. It is also to proposed diseases treatment algorithm that is capable to provide a suitable treatment and control for each identified herbs diseases. |
| first_indexed | 2024-03-06T07:28:50Z |
| format | Article |
| id | upm.eprints-13603 |
| institution | Universiti Putra Malaysia |
| language | English English |
| last_indexed | 2024-03-06T07:28:50Z |
| publishDate | 2011 |
| publisher | Engg Journals Publications |
| record_format | dspace |
| spelling | upm.eprints-136032015-10-06T08:26:51Z http://psasir.upm.edu.my/id/eprint/13603/ Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework Abdullah, Lili Nurliyana Khalid, Fatimah Borhan, N.M. When herbs plants has disease, they can display a range of symptoms such as colored spots, or streaks that can occur on the leaves, stems, and seeds of the plant. These visual symptoms continuously change their color, shape and size as the disease progresses. Once the image of a target is captured digitally, a myriad of image processing algorithms can be used to extract features from it. The usefulness of each of these features will depend on the particular patterns to be highlighted in the image. A key point in the implementation of optimal classifiers is the selection of features that characterize the image. Basically, in this study, image processing and pattern classification are going to be used to implement a machine vision system that could identify and classify the visual symptoms of herb plants diseases. The image processing is divided into four stages: Image Pre-Processing to remove image noises (Fixed-Valued Impulse Noise, Random-Valued Impulse Noise and Gaussian Noise), Image Segmentation to identify regions in the image that were likely to qualify as diseased region, Image Feature Extraction and Selection to extract and select important image features and Image Classification to classify the image into different herbs diseases classes. This paper is to propose an unsupervised diseases pattern recognition and classification algorithm that is based on a modified Hierarchical Dynamic Artificial Neural Network which provides an adjustable sensitivity-specificity herbs diseases detection and classification from the analysis of noise-free colored herbs images. It is also to proposed diseases treatment algorithm that is capable to provide a suitable treatment and control for each identified herbs diseases. Engg Journals Publications 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/13603/1/Classification%20of%20herbs%20plant%20diseases%20via%20hierachical%20dynamic%20artificial%20neural%20network%20after%20image%20removal%20using%20kernel%20regression%20framework.pdf Abdullah, Lili Nurliyana and Khalid, Fatimah and Borhan, N.M. (2011) Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework. International Journal on Computer Science and Engineering, 3 (1). pp. 15-20. ISSN 0975-3397 Bayesian statistical decision theory Neural networks (Computer science) English |
| spellingShingle | Bayesian statistical decision theory Neural networks (Computer science) Abdullah, Lili Nurliyana Khalid, Fatimah Borhan, N.M. Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework |
| title | Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework |
| title_full | Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework |
| title_fullStr | Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework |
| title_full_unstemmed | Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework |
| title_short | Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework |
| title_sort | classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework |
| topic | Bayesian statistical decision theory Neural networks (Computer science) |
| url | http://psasir.upm.edu.my/id/eprint/13603/1/Classification%20of%20herbs%20plant%20diseases%20via%20hierachical%20dynamic%20artificial%20neural%20network%20after%20image%20removal%20using%20kernel%20regression%20framework.pdf |
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