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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM34946

Germination Predication to Access the Quality of Grains for Different Crops Using IoT and Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM34946

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM34946

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

DBM

Dr. Bharathi M

MK

Manish Kumar

MBN

Monisha B N

PMaRF

Preethi M and Rahila Fardeen

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Abstract

The paper expounds on a advanced framework that consistently blends Counterfeit Insights (AI) and Web of Things (IoT) innovations to figure seed germination in rural settings. It underscores the noteworthiness of leveraging real-time information sourced from soil sensors nearby cutting-edge machine learning calculations to supply important bits of knowledge into the designs of edit germination. This imaginative framework is made with the essential objective of preparing ranchers with opportune and significant data, subsequently encouraging educated decision-making forms relating to water system procedures and asset assignment. Central to the system’s engineering is its versatility, encouraged by a cloud-based stage that empowers consistent development and adjustment to shifting rural necessities. This guarantees not as it were the productive investigation of sensor- derived information but moreover permits for the customization of the system’s highlights to suit the different needs of diverse rural settings. Subsequently, the system’s flexibility loans itself to optimizing yields and cultivating feasible nourishment generation hones, subsequently contributing to the overarching objective of guaranteeing nourishment security and natural stewardship.

KEYWORDS

Smart System Seed Germination Prediction AI and IoT Integration Agriculture Machine Learning Models Real-time Data Crop Management Scalable Cloud Platform Customizable Design Sustainable Food Production.

Cite This Article

Dr. Bharathi M, Manish Kumar, Monisha B N, Preethi M and Rahila Fardeen, "Germination Predication to Access the Quality of Grains for Different Crops Using IoT and Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM34946

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Publication Date

12/31/45440

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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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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