Corn Leaf Disease Detection Using Deep Learning

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ANSARI SAUD AHMED; KHAN ZAID; RUSSEL FERNANDES; JAYA JESWANI

Corn Leaf Disease Detection Using Deep Learning

Authors: ANSARI SAUD AHMED, KHAN ZAID, RUSSEL FERNANDES, JAYA JESWANI

Unique Paper ID: 159584
Volume: 9
Issue: 12
PageNo: 290-295

Keywords: Convolutional neural network (CNN) Feature Extraction Classification

Abstract:
The leaf disease detection system is designed to identify and provide a solution for any disease that may be present in a leaf or plant. This is especially crucial in developing countries where agriculture plays a significant role, and it is essential to recognize unhealthy plant leaves and classify the disease to prevent significant loss of plants. By providing faster and more accurate results, farmers can reduce their losses. The process of discovering the type of disease involves four stages: image pre-processing, feature extraction, and classification. Image pre-processing is used to enhance the quality of the image, and for classification, a Convolution Neural Network (CNN) is utilized, which includes various layers that aid in prediction. Finally, a cure is suggested to the user during the terminal stage

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Cite This Article

ISSN: 2349-6002
Volume: 9
Issue: 12
PageNo: 290-295

Corn Leaf Disease Detection Using Deep Learning

Available:https://ijirt.org/article?manuscript=159584

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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