 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: April 2026
Volume 11 | Issue 4
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23355
Article Published : 9033
Total Authors : 23952
Total Reviewer : 831
Total Countries : 162
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Machine Learning Based Crop Yield Prediction and Agricultural Advisory System |
| Authors Name: | K Sampath reddy , K Varshith Reddy , K Sathwik Krishna , Mr. Uppula Nagaiah |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_210323
|
| Published Paper Id: | IJRTI2604043 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Agriculture plays a crucial role in the economic development and food security of many countries, particularly in developing nations where a significant portion of the population depends on farming. Accurate crop yield prediction is essential for efficient agricultural planning and resource management. Traditional yield prediction methods rely heavily on farmer experience and manual estimation, which often lead to inaccurate outcomes and financial losses. This research presents a Machine Learning–based Crop Yield Prediction and Agricultural Advisory System designed to assist farmers in making data-driven agricultural decisions. The proposed system utilizes machine learning algorithms such as Linear Regression, Decision Tree, and Random Forest to analyze historical agricultural datasets containing soil properties, rainfall, temperature, and humidity parameters. The system predicts crop yield and provides advisory recommendations through a web-based application. By integrating predictive analytics with an intelligent decision-support interface, the proposed platform enables farmers to optimize crop selection, improve productivity, and reduce agricultural risks. Experimental results indicate that the Random Forest algorithm provides higher prediction accuracy compared to other models. The system demonstrates the potential of machine learning technologies in improving agricultural productivity and promoting smart farming practices. |
| Keywords: | Machine Learning, Crop Yield Prediction, Random Forest, Smart Agriculture, Data Analytics, Agricultural Advisory System |
| Cite Article: | "Machine Learning Based Crop Yield Prediction and Agricultural Advisory System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a308-a319, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604043.pdf |
| Downloads: | 00031 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2604043
Registration ID:210323
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a308-a319 Country: Hyderabad, Telangana, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604043 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604043 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||