Image Classification for Papaya Disease Detection Using Deep Learning

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Varghese Chacko

Image Classification for Papaya Disease Detection Using Deep Learning

Authors: Varghese Chacko

Unique Paper ID: 167880
Volume: 11
Issue: 4
PageNo: 611-615

Keywords: AI in Agriculture Agricultural Image Analysis Automated Disease Detection Convolutional Neural Networks (CNNs) Crop Health Monitoring Deep Learning Image Classification Papaya Disease Detection Plant Disease Identification Precision Agriculture.

Abstract:
The agricultural sector faces challenges from diseases affecting crop yields, including papaya. Early and accurate disease detection is vital for preventing damage. This paper introduces a deep learning approach for papaya disease detection using convolutional neural networks (CNNs). We developed a model that identifies common papaya diseases, like leaf spots, mold, and fruit rot, with high accuracy. Trained, validated, and tested on a dataset of healthy and diseased papayas, the model delivered promising results. This study enhances detection accuracy and supports precision agriculture by providing an automated, scalable tool for monitoring papaya health. The results highlight the value of AI-driven image classification in helping farmers make timely, informed decisions to protect their crops.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 4
PageNo: 611-615

Image Classification for Papaya Disease Detection Using Deep Learning

Available:https://ijirt.org/article?manuscript=167880

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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