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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM13932

CLASSIFICATION OF PLANT LEAF DISEASES USING MACHINE LEARNING AND IMAGE PREPROCESSING TECHNIQUES WITH SMART AGRICULTURE

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM13932

Volume:

6 |

6 |

Jun 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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10.55041/IJSREM13932

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

YS

Yashodhan Sonawane

DK

Dhiraj Khachane

OK

Onkar Khaire

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Abstract

Farm crops are often ruined by various diseases. This means the farmers have to incur huge losses. They can’t barricade their entire fields or remain on the field all day. We propose here a leaf disease detection system with smart agriculture. In this system, the various sensor detects wildlife and environmental conditions on the eld, as well as possible intruders. When any suspicious activity or condition is detected, it reports to the owner of the field. It acts as a system that is adaptable and provides farmers with a practical means of protecting their crops from major diseases and land from attacks. This system proposes a machine learning approach that is based on improved convolutional neural networks (CNNs) for the real-time detection of leaf diseases. The smart Agriculture part checks the water level in the soil if it is low the system will automatically send readings to farmers. It also has a smoke sensor if smoke is detected the system will automatically sprinkle water in the field. This ensures the complete safety of crops thus protecting the farmer’s loss.

KEYWORDS

machine learning CNN algorithm detection monitors report moisture smoke sensor

Cite This Article

Yashodhan Sonawane, Dhiraj Khachane, Onkar Khaire, "CLASSIFICATION OF PLANT LEAF DISEASES USING MACHINE LEARNING AND IMAGE PREPROCESSING TECHNIQUES WITH SMART AGRICULTURE," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 6, Jun 2022. https://doi.org/10.55041/IJSREM13932

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Publication Date

6/20/2022

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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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