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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: | Leaf Disease DEtection |
| Authors Name: | L. Indu Reddy , yasaswi kota , Lahari Ravi |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_197019
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| Published Paper Id: | IJNRD2306248 |
| Published In: | Volume 8 Issue 6, June-2023 |
| DOI: | |
| Abstract: | : Farming supplies food for all humans, especially in times of rapid growing populations. This is advised that illnesses of plants be predicted at the earliest stages in this field of farming in order to provide food to the whole population. However, it is difficult to forecast illnesses in the initial phases of plant growth. The purpose of this study is to educate farmers about cutting-edge technology for reducing illnesses in plant leaves. Tomato is a common vegetable, techniques based on image processing and machine learning along with a reliable algorithm have been found to identify leaf illnesses in tomato plants. Defective tomato leaf samples are considered in this study. These pathological samples taken from tomato leaves allow farmers to identify diseases based on early signs. First, the tomato leaf sample is scaled down to his 256x256 pixels. Histogram equalization is then performed to improve sample quality. Clustering with K-Means is used to divide the data space into voronoi cells. Edge tracking is used to determine the bounds of the sheet collection. Several classifiers such as discrete wavelet transform, principal component analysis, and grayscale co-occurrence matrix are used to determine important characteristics of leaf data. Finally, the derived features are classified using Convolutional Neural Networks (CNN) with tomato leaf disease detection dataset from kaggle with 92% accuracy |
| Keywords: | Cutting–edge technology Convolutional Neural Network, clustering, K-Means, Voronoi cells, Gray scale co factor matrix. Discrete wavelets transform, Kaggle. |
| Cite Article: | "Leaf Disease DEtection", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 6, page no.c463-c469, June-2023, Available :http://www.ijnrd.org/papers/IJNRD2306248.pdf |
| Downloads: | 000118756 |
| 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:IJNRD2306248 Registration ID: 197019 Published In: Volume 8 Issue 6, June-2023 DOI (Digital Object Identifier): Page No: c463-c469 Country: TADEPALLI, Andhra Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2306248 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2306248 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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