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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: | Plant Aliment Detection Using Deep Learning And Conventional Neural Network |
| Authors Name: | Pentapati Venkata Surya Kamal , Narava Chitti Dedeepya , Gullapalli Jaswanth Chowdary , Palla Sesha Ratna Kumari , Pillaka Suvarna |
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| Author Reg. ID: |
IJNRD_184893
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| Published Paper Id: | IJNRD2212132 |
| Published In: | Volume 7 Issue 12, December-2022 |
| DOI: | |
| Abstract: | Plant disease detection is an emerging field in which imaging techniques are applied to the field, using images that can detect differences between healthy and diseased plants. The main goal of this research is to quickly capture images of a disease on a plant, with the hope that it can be used to find a cure for it. This ailment can be caused by several factors including insects, bacteria and viruses. The plant disease detection tool detects plant diseases at the genetic level. The tool identifies the causative agent, initial symptoms and pathogen of a crop. It also provides early warning about new crop varieties with unknown genetic resistance for common diseases. Detecting plant diseases using deep learning is an important step toward developing a smart network capable of proactively identifying and monitoring the health of crops, and to ultimately allow farmers to predict future deterioration. We have developed a model that has already been validated on three pipelines of different sizes. A second goal was to use this model's power with high accuracy to distinguish between different classes or types of pathogens such as fungal and oomycetes. We’ve identified three different classes of pathogens that can cause disease in crops. The first class is bacterial, which are single-celled organisms that can be harmful to plants. The second class is fungal, which are also single-celled organisms but with more complex features than bacteria. In order to classify illnesses in plants and illustrate the model's importance, CNN was employed in conjunction with histogram approaches. To diagnose tomato leaf illnesses, simple CNN architectures including AlexNet, GoogLeNet, and ResNet were built. Plots of training and validation accuracy were used to display the model's performance; ResNet was judged to be the best CNN architecture. |
| Keywords: | CNN (CONVENTIONAL NEURAL NETWORK), SVM (SUPOORT VECTOR MACHINE), K-NN (K- NEAREST NEIGHBOURS), DEEP LEARNING, AUTO PLANT PEST DETECTION, PLANT LEAF DISEASE DETECTION |
| Cite Article: | "Plant Aliment Detection Using Deep Learning And Conventional Neural Network", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 12, page no.b326-b329, December-2022, Available :http://www.ijnrd.org/papers/IJNRD2212132.pdf |
| Downloads: | 000118780 |
| 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:IJNRD2212132 Registration ID: 184893 Published In: Volume 7 Issue 12, December-2022 DOI (Digital Object Identifier): Page No: b326-b329 Country: Visakhapatnam, Andhra Pradesh, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2212132 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2212132 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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