Paper Title
Potato Leaf disease detection using Deep learning
Article Identifiers
Authors
Pranav Goyal , Pranjal Singh , Arpit Jaiswal , Satyam Singh
Keywords
Convolutional Neural Network (CNN), Confusion matrix, Machine Learning
Abstract
Analysing plant phenotype stands as a pivotal component in understanding plant development. This study introduces a streamlined methodology aimed at discerning between healthy and diseased or infected leaves through the integration of image processing and machine learning methodologies. Diverse ailments impair leaf chlorophyll, manifesting as discoloured spots on the leaf surface, typically brown or black in hue. The proposed approach employs a sequence of techniques including image preprocessing, segmentation, feature extraction, and machine learning-based classification. Notably, the incorporation of a convolutional neural network (CNN) notably enhances detection accuracy.
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How To Cite (APA)
Pranav Goyal, Pranjal Singh, Arpit Jaiswal, & Satyam Singh (April-2024). Potato Leaf disease detection using Deep learning . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), d90-d92. https://ijnrd.org/papers/IJNRD2404312.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : d90-d92
Other Publication Details
Paper Reg. ID: IJNRD_217899
Published Paper Id: IJNRD2404312
Downloads: 000121982
Research Area: Engineering
Country: Ghaziabad, Uttar Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404312.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404312
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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