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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: | DETECTING PLANT SPECIES HEALTH USING ARTIFICIAL INTELLIGENCE |
| Authors Name: | Mr. RAMBABU ATMAKURI , Ms. BOMMAREDDY VENKATA SANTHOSHI , Mr. AGALDIVITY SUMITH , Ms. KOTHWAL AISHWARYA , Ms. YERRA GOWRI GAYATHRI |
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IJNRD_215982
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| Published Paper Id: | IJNRD2403362 |
| Published In: | Volume 9 Issue 3, March-2024 |
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| Abstract: | A lot of study in computer vision and agriculture is done on using Convolutional Neural Networks (CNN) and the VGG16 architecture to find diseases in rice plants. One of the main goals is to make a model that can correctly tell the difference between pictures of different rice plant diseases. CNN is a deep learning method that is often used to recognize pictures. VGG16 is a type of CNN that is known for how well it does at classifying images. To train the model, we need a set of pictures of both healthy and sick rice plants, with enough of each type of disease shown. The computer can then learn to tell the difference between the diseases. We use the VGG16 architecture to teach the CNN model once we have the information. The model learns to find patterns in pictures that show whether a plant is healthy or sick during training. We can use a different set of pictures to test the model and see how well it does after training. We use precision, memory, and the F1 score to measure how accurate it is. This study could help farmers a lot by finding and treating diseases in rice plants early on. This could lead to higher crop output and more food security. One interesting way to use technology to help farmers is in this way. Deep Convolutional Neural Networks (DCNN) and transfer learning are used in the suggested method, which seems to be good at correctly identifying six types of diseases in rice plants. Farmers can keep an eye on their fields more efficiently by using IoT and drone technology. This cuts down on the need for costly manual checks. The proposed method seems like it could be used in the real world because it is very accurate. It's interesting that this method works better than similar ones that have been used in previous studies. Overall, using this method could increase crop output and help ensure food security by making it easier to find and treat rice plant diseases early on |
| Keywords: | Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming. |
| Cite Article: | "DETECTING PLANT SPECIES HEALTH USING ARTIFICIAL INTELLIGENCE", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.d447-d454, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403362.pdf |
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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:IJNRD2403362 Registration ID: 215982 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: d447-d454 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403362 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403362 |
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ISSN: 2456-4184
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