Crop Disease Prediction

Registration ID: IJNRD_217479

Published ID: IJNRD2404117

DOI: Click Here to Get

K Raghunatha Reddy , R.Renu sree , D.VivekVardhan , P.Lokeshwer

Crop diseases pose significant threats to agricultural productivity and food security worldwide. This project proposes a novel approach, " DeepCropGuard " which integrates Convolutional Neural Networks (CNN) and Gradient Descent Optimization for predicting crop diseases. The CNN model is trained on a comprehensive dataset of crop images annotated with disease labels to learn discriminative features indicative of various diseases. Gradient Descent Optimization techniques are employed to fine-tune model parameters, enhancing predictive accuracy and generalization. Through extensive experimentation and validation, the effectiveness of the proposed approach is demonstrated in accurately identifying crop diseases, thereby enabling timely intervention and mitigation strategies. The implementation of DeepCropGuard holds promise for revolutionizing crop disease management, empowering farmers with early detection and decision support tools to safeguard agricultural yields and enhance global food security.

Published Paper   E-Certificate

K Raghunatha Reddy, R.Renu sree, D.VivekVardhan , & P.Lokeshwer (April-2024). Crop Disease Prediction . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), b120-b127. https://ijnrd.org/papers/IJNRD2404117.pdf

Volume 9 Issue 4, April-2024

Pages : b120-b127

Paper Reg. ID: IJNRD_217479

Published Paper Id: IJNRD2404117

Downloads: 000121981

Research Area: Computer Science & Technology 

Country: Nandyal , Andhra Pradesh , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404117.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404117

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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Call For Paper - Volume 10 | Issue 10 | October 2025

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