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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM34613

SMART PLANT HEALTH CARE SYSTEM: Image Based Disease Detection and Pesticide Remediation

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM34613

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM34613

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

SKmr

S Krishna manas reddy

RSS

Rohan S Savadakar

SKS

Sannidhi K S

YSG

Yogesh S Gowda

KG

Kavyashri G

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Abstract

Plant and tree populations must be preserved and supported in order to mitigate the growing issues brought about by food and water scarcity brought on by population increase and climate change. The occurrence of plant diseases is a major issue in agriculture as it severely reduces agricultural output. In order to overcome this difficulty, scientists are investigating novel approaches that make use of sensors and imaging to collect data on plant health in order to detect diseases early on. The goal of this project is to create a “Smart Plant Health Care System” that combines embedded technologies such as Arduino, Raspberry Pi, and Jetson Nano for pesticide remediation controlled by Arduino and image-based illness diagnosis. More specifically, convolutional neural networks (CNNs) are implemented for real-time illness diagnosis using the processing capacity and adaptability of the Raspberry Pi.

KEYWORDS

Smart Agriculture Plant Health Monitoring Disease Detection

Cite This Article

S Krishna manas reddy, Rohan S Savadakar, Sannidhi K S, Yogesh S Gowda, Kavyashri G, "SMART PLANT HEALTH CARE SYSTEM: Image Based Disease Detection and Pesticide Remediation," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM34613

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Publication Date

12/31/45433

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Double-Blind Peer Review

Review Rounds: 2

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9

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5

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

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