International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 3 May-June 2024 Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 3
May-June 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Ai Driven Interactive Agri Bot Providing Realtime Assistance in Cultivation and Market Linkages

Author(s) M.Gomathi, G.A.Monika, M.Nachammai, A.Saranya, R.Shyni Deva Priya
Country India
Abstract This project introduces an integrated system for smart agriculture, employing Internet of Things (IoT) technology for soil type analysis and deep learning methodologies for pest detection. The proposed system leverages a specialized NPK sensor for real-time measurement of soil nutrient levels, facilitating
precision agriculture practices. Additionally, a Convolutional Neural Network (CNN) algorithm is employed to detect pests in crops, enhancing crop management efficiency and yield optimization. The IoT-based NPK sensor enables farmers to monitor essential soil nutrients such as nitrogen (N),
phosphorus (P), and potassium (K) levels remotely and in real-time. This data empowers farmers to make informed decisions regarding fertilization strategies, ensuring optimal nutrient balance for healthy plant growth while minimizing resource wastage and environmental impact. To the deep learning framework, specifically CNN, is utilized for pest detection in crops. By analyzing images captured from smart agricultural cameras, the CNN model can identify and classify various pests and diseases affecting crops. This enables early detection and intervention, thereby mitigating potential crop damage and yield losses. The integration of CNN-based pest detection with IoT infrastructure enables timely and targeted pest management actions, reducing reliance on chemical pesticides and promoting sustainable agricultural practices.
Keywords CNN, NPK sensor, IOT, Deep Learning.
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-27
Cite This Ai Driven Interactive Agri Bot Providing Realtime Assistance in Cultivation and Market Linkages - M.Gomathi, G.A.Monika, M.Nachammai, A.Saranya, R.Shyni Deva Priya - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18409
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.18409
Short DOI https://doi.org/gtsg5x
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 2 Cover Page Vol 6 Isu 1 Cover Page Vol 5 Isu 6 Research Paper Format

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com