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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM30707

Automated Leaf Disease Diagnosis with Deep Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM30707

Volume:

8 |

4 |

Apr 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SA

Sakshi Abhale

SB

Shrutika Bhavsar

KG

Kadambari Ghadge

AS

Akash Sale

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Abstract

Agriculture is crucial for India’s economy, but one problem is identifying plant diseases, especially in remote areas where contacting experts is difficult. Wrong disease identification can lead to crop loss. Even government agriculture departments sometimes give incorrect advice to farmers. To tackle this, a dataset of apple crop images has been created to help identify healthy and diseased leaves accurately. These images come from different angles and directly from apple fields. The early disease detection in agriculture, particularly focusing on diseases affecting apple crops. Specifically, it mentions diseases like early blight, late blight, black scurf, common scab that can affect apple plants. It discusses using automatic methods like Convolutional Neural Networks (CNN) to detect and classify these diseases based on a dataset. The approach involves automatic feature extraction and classification using colur information.

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Cite This Article

Sakshi Abhale, Shrutika Bhavsar, Kadambari Ghadge, Akash Sale, "Automated Leaf Disease Diagnosis with Deep Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 4, Apr 2024.

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12/31/45394

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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