Plant Disease Prediction Using Convolutional Neural Networks

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Chetan Choudhari; Dr. Prakash Prasad; Khemant Borkar; Abhishek Shende; Aniket Randive; Saurabh Nagrare

Plant Disease Prediction Using Convolutional Neural Networks

Authors: Chetan Choudhari, Dr. Prakash Prasad, Khemant Borkar, Abhishek Shende, Aniket Randive, Saurabh Nagrare

Unique Paper ID: 176038
Volume: 11
Issue: 11
PageNo: 6845-6849

Keywords: Plant disease Feature extraction Deep learning Machine learning Classification.

Abstract:
In Agriculture plant disease detection is global and leading to crop losses and food security. Early accurate identifying of plant health is critical for effective disease prevention. In this project, we developed a deep learning-based model to automatically detect health of plant. The model uses CNN, including VGG16, ResNet, Inception, and a custom CNN architecture, to classify images into various disease categories. We trained the models on the publicly available dataset, which contains thousands of labeled images representing different plant species and their corresponding diseases. Image augmentation, normalization, and preprocessing techniques (rotation, flipping, zooming) were applied to improve model performance and generalization. The model identified and evaluated based on their accuracy, precision, recall, F1-score, and confusion matrix.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 6845-6849

Plant Disease Prediction Using Convolutional Neural Networks

Available:https://ijirt.org/article?manuscript=176038

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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