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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREMWP1017

AI Driven Greentech Innovation in Agriculture

Stream/Area:

-

Subject:

-

Paper ID:

IJSREMWP1017

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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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

AKP

Ajith Kumar. P

BS

Barath. S

GS

Gowtham. S

DG

D. GuruPandi

DSSP

Dr. S. Sathiya Priya

DVJR

Dr. V. Jeya Ramya

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Abstract

Agriculture is a primary concern of every developing nation. But most of the processes involved in agricultural sector are still orthodox and inefficient. Though the technology is improving day by day, it is high time to incorporate technology innovations into the field of agriculture for better productivity and resource utilization. We propose an intelligent farming technology with the aid of Internet of Things (IoT) and Machine Learning to make various farming techniques smarter and efficient. Various sensors were deployed in the fields to remotely monitor the parameters of soil, water and air. The sensed information is fed to an Android app and it will process the data and alert the farmer if any unusual events occur. We are also using convolutional neural network for detecting various diseases affected by the plants. Experimental results validate that our model can be used for efficient utilization of agricultural resources like water and fertilizers and thereby improve the productivity and profit.

KEYWORDS

Internet of Things Smart farming Machine Learning Plant disease identification IoT in Agriculture.

Cite This Article

Ajith Kumar. P, Barath. S, Gowtham. S, D. GuruPandi, Dr. S. Sathiya Priya, Dr. V. Jeya Ramya, "AI Driven Greentech Innovation in Agriculture," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025.

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Publication Date

5/21/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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3

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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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