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 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 6
November-December 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

Crop Prediction Analysis And Plant Disease Detection Using Machine Learning

Author(s) Nivargi Anil Basavant, Gururaj V, Kavya N L
Country India
Abstract The development of a comprehensive agricultural support system, integrating plant disease classification, crop prediction, and fertilizer recommendation models. Leveraging the ResNet-9 architecture, the plant disease classification model accurately identifies diseases in crop leaves. Additionally, the system incorporates crop prediction capabilities to forecast suitable crops based on environmental conditions and historical data. Furthermore, the fertilizer recommendation model suggests optimal fertilizers based on soil composition, crop type, and nutrient requirements. This multifaceted approach facilitates precision agriculture by enabling farmers to make informed decisions regarding crop health management, crop selection, and fertilization practices. The integration of these models provides a holistic solution for enhancing agricultural productivity, optimizing resource utilization, and promoting sustainable farming practices. Future enhancements may involve refining the prediction algorithms, incorporating real-time sensor data for dynamic recommendations, and scaling the system for broader adoption in diverse agricultural settings. Overall, the project demonstrates the potential of AI-driven solutions to address complex challenges in agriculture and contribute to global food security efforts.
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-13
Cite This Crop Prediction Analysis And Plant Disease Detection Using Machine Learning - Nivargi Anil Basavant, Gururaj V, Kavya N L - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.20123
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.20123
Short DOI https://doi.org/gtt8t2
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
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6Cover Page Vol 6 Isu 5Cover Page Vol 6 Isu 4

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