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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | RECOGNITION AND CLASSIFICATION OF CASSAVA LEAF DISEASES USING MACHINE LEARNING TECHNIQUES |
| Authors Name: | Nisha P , Dr.J.Vijayakumar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_181076
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| Published Paper Id: | IJNRD2204095 |
| Published In: | Volume 7 Issue 4, April-2022 |
| DOI: | http://doi.one/10.1729/Journal.30119 |
| Abstract: | Recently, one of the active research areas in agriculture is the productivity and quality of a crop. Image processing and deep learning techniques are being used to recognize plant diseases, which is a hot research topic right now. The majority of research has focused on identifying illnesses using images of complete leaves. Plants are considered essential because they supply mankind with a source of energy. Between seeding and harvesting, plants can be affected at any time. The plant is affected by several infections like viruses, bacteria, and fungal. If pre-preparing is not followed, it will have serious consequences for the plants, as well as a reduction in product quality, quantity, and productivity. Image processing is an early stage of plant disease detection role-playing the well. The goal of this research work is to develop an image recognition system that can recognize plant diseases. Nowadays we need automatic plant disease detection for increasing the food crops and, easily diagnosis the disease. The cassava plant is a worldwide food crop, and it is the third-largest source of food carbohydrates. The early stage of cassava leaf disease detection is very important in the agriculture field. The cassava leaf images are used for the disease identification process. The hybrid algorithm includes the pre-processing steps and the segmentation process is done using the CLAHE. Then the K-means cluster and GLCM are used for the feature affected area identification. The diseased image is classified by the SVM classifiers. Finally, the disease grade is measured by fuzzy logic. The result, we have achieved are more useful and they prove to the helpful for farmers during the cultivation of cassava, which is a major food crop in the world. |
| Keywords: | CLAHE, SVM, Plant disease, FUZZY logic |
| Cite Article: | "RECOGNITION AND CLASSIFICATION OF CASSAVA LEAF DISEASES USING MACHINE LEARNING TECHNIQUES", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 4, page no.783-791, April-2022, Available :http://www.ijnrd.org/papers/IJNRD2204095.pdf |
| Downloads: | 000118758 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2204095 Registration ID: 181076 Published In: Volume 7 Issue 4, April-2022 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.30119 Page No: 783-791 Country: Coimbatore, Tamilnadu, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2204095 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2204095 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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