Paper Title
GRAPE LEAF DISEASE PREDICTION USING CONVOLUTIONAL NEURAL WORK USING SUPPLEMENT SUGGESTION
Article Identifiers
Authors
Boopathi , S. Prabakaran
Keywords
Pytorch,Flask,CNN
Abstract
The cultivation of grapes encounters various challenges, such as the occurrence of pests and diseases, which have the possible to considerably diminish agricultural productivity. Plant diseases posture a significant impediment, resulting in diminished agricultural productivity and economic setbacks, thereby affecting the quality of crop yields. Hence, the precise and timely identification of plant diseases holds significant importance. The prediction of grape leaf diseases using Convolutional Neural Networks (CNNs) represents a transformative approach to modern agricultural management. This system explores the application of CNNs to automatically detect and classify grape leaf diseases from images, aiming to enhance early disease detection, optimize intervention strategies, and improve overall grapevine health. The use of CNNs leverages their ability to learn hierarchical features from image data, enabling the accurate identification of various disease symptoms that may be subtle and difficult to detect manually. The CNN-based model is trained on a diverse dataset of grape leaf images, encompassing various disease conditions and growth stages. The System demonstrates high accuracy in classifying disease types and distinguishing between healthy and affected leaves.
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How To Cite (APA)
Boopathi & S. Prabakaran (August-2024). GRAPE LEAF DISEASE PREDICTION USING CONVOLUTIONAL NEURAL WORK USING SUPPLEMENT SUGGESTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), d254-d259. https://ijnrd.org/papers/IJNRD2408330.pdf
Issue
Volume 9 Issue 8, August-2024
Pages : d254-d259
Other Publication Details
Paper Reg. ID: IJNRD_226649
Published Paper Id: IJNRD2408330
Downloads: 000122008
Research Area: Engineering
Country: Erode, Tamilnadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2408330.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408330
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