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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
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23468
Article Published : 9055
Total Authors : 24028
Total Reviewer : 831
Total Countries : 162
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| Paper Title: | Crop Yield Prediction Using Machine Learning |
| Authors Name: | Sakshi Pinge , Priti Wankhade , Samruddhi Gawande , Shravani Bijwe , Asmita Gore |
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IJRTI_210484
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| Published Paper Id: | IJRTI2603194 |
| Published In: | Volume 11 Issue 3, March-2026 |
| DOI: | |
| Abstract: | Agricultural productivity is significantly influenced by soil features and climate variability. Traditional yield estimation methods depend on manual soil testing and historical averages that may not reflect real-time field conditions. This research proposes an intelligent crop yield prediction system that integrates soil image analysis, machine learning algorithms, and geo-weather data to improve data-driven agricultural decisions. The system processes soil images using image enhancement techniques and supervised learning models to estimate soil pH and classify soil type. Real-time environmental parameters such as temperature, rainfall, humidity, and solar radiation are retrieved through weather API integration based on geographic coordinates. These features are combined with historical agricultural data to train a Random Forest Regression model for predicting crop yield per hectare. Experimental results show that integrating image-derived soil properties with real-time weather data improves prediction accuracy compared to traditional single-source models. The system also provides yield comparison visualization and recommends suitable crops for specific field conditions, helping farmers make better agricultural decisions and improve productivity. |
| Keywords: | Crop Yield Prediction, Machine Learning in Agriculture, Soil Image Analysis, Random Forest Regression, Geo-Weather Data, Precision Farming |
| Cite Article: | "Crop Yield Prediction Using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b764-b771, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603194.pdf |
| Downloads: | 00067 |
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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 |
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Published Paper ID: IJRTI2603194
Registration ID:210484
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier):
Page No: b764-b771 Country: Amravati, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603194 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603194 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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