Leaf Disease DEtection

Registration ID: IJNRD_197019

Published ID: IJNRD2306248

DOI: Click Here to Get

L. Indu Reddy , yasaswi kota , Lahari Ravi

Cutting–edge technology Convolutional Neural Network, clustering, K-Means, Voronoi cells, Gray scale co factor matrix. Discrete wavelets transform, Kaggle.

: 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

Published Paper   E-Certificate

L. Indu Reddy, yasaswi kota, & Lahari Ravi (June-2023). Leaf Disease DEtection. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(6), c463-c469. https://ijnrd.org/papers/IJNRD2306248.pdf

Volume 8 Issue 6, June-2023

Pages : c463-c469

Paper Reg. ID: IJNRD_197019

Published Paper Id: IJNRD2306248

Downloads: 000121983

Research Area: Computer Science & Technology 

Country: TADEPALLI, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2306248.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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