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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM40112

AgroSense AI with News Aggregator

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM40112

Volume:

8 |

12 |

Dec 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM40112

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

GV

Ganeshram V

KR

Kaviyarasi R

SJG

Semmozhi Jayam G

RR

Rakshita R

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Abstract

Small-scale farmers face challenges such as plant diseases, pest infestations, soil fertility issues, water mismanagement, and unpredictable weather, which reduce crop yields and profitability, especially in remote areas with limited access to expertise. To address this, we propose an online platform connecting farmers with agricultural experts like agronomists and entomologists, fostering knowledge exchange to tackle these issues and promote sustainable farming. Leveraging deep learning, our study classifies soil textures using Convolutional Neural Networks (CNNs) to analyze soil images, employing techniques like image augmentation to improve datasets and exploring models such as Baseline, Augment, Dropout, and Padding. This AI-powered approach, integrated into our platform alongside a farmer discussion forum, empowers farmers with actionable insights, enhancing crop yields, conserving resources, and advancing food security and economic growth. AgroSense AI merges traditional agricultural wisdom with cutting-edge technology, offering tools and a collaborative community to drive a sustainable future in farming.

KEYWORDS

AI powered Convolutional Neural Networks Deep Learning Padding Plant disease prediction Soil texture classification

Cite This Article

Ganeshram V, Kaviyarasi R, Semmozhi Jayam G, Rakshita R, "AgroSense AI with News Aggregator," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 12, Dec 2024. https://doi.org/10.55041/IJSREM40112

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Publication Date

12/24/2024

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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