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

Advanced Crop Recommendation Systems: Leveraging Random Forest and KNN Algorithms

Author(s) Burla Uday Theja, Rajan Kakkar, Sanjay Palegar, Chirumamilla Sriram, Ravi Gowtham Mutyala, Ashhar Alam
Country India
Abstract In the time of precision agriculture, crop selection optimization is crucial to maximizing productivity and resource efficiency. This article explores the combination of Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms to enhance crop recommendation systems. Crop performance and environmental characteristics have complex and non-linear connections that are captured by the reliable and accurate RF technique. Using a sizable dataset that includes crop yields, climate factors, and soil properties, the study assesses the efficacy of the integrated system in comparison to traditional recommendation methodologies. Preliminary we implemented KNN first and got 96% accuracy and then implemented on RF to get 99% accuracy and also created a GUI using tkinter to predict crops on random value.
Keywords Crop Recommendation, Precision Agriculture, Random Forest, K-Nearest Neighbors (KNN), Machine Learning, Data Mining, Agricultural Optimization, Yield Prediction, Environmental Factors, Soil Properties, Climate Conditions, Classification Algorithms, Predictive Modeling, Crop Performance, Data Analytics, Decision Support Systems, Sustainable Agriculture, Hybrid Models, Farming Technology, Computational Agriculture
Field Engineering
Published In Volume 6, Issue 4, July-August 2024
Published On 2024-08-04
Cite This Advanced Crop Recommendation Systems: Leveraging Random Forest and KNN Algorithms - Burla Uday Theja, Rajan Kakkar, Sanjay Palegar, Chirumamilla Sriram, Ravi Gowtham Mutyala, Ashhar Alam - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.25075
DOI https://doi.org/10.36948/ijfmr.2024.v06i04.25075
Short DOI https://doi.org/gt556h
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