PREDICTION OF UNCERTAIN TRENDS AND PRICES IN AGRICULTURE USING MACHINE LEARNING TECHNIQUES Article Swipe
K. Ramesh
,
Surekha Venkata Mullapudi
,
A. Pavithra
,
Kolla Gnapika Sindhu
,
B. Pranay
,
R. Subhash Reddy
,
P. V. Sudheer Kumar Reddy
,
Preetham Reddy
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.46647/ijetms.2025.v09i02.068
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.46647/ijetms.2025.v09i02.068
Agriculture remains the cornerstone of our nation's economy, yet farmers continue to struggle withunpredictable climate patterns, fluctuating crop prices, and limited access to actionable insights.These challenges often result in poor crop selection, misinformed decisions, and significantfinancial losses. To combat these issues, we introduce AgroSage — an intelligent, AI-poweredagricultural assistant designed to empower farmers with data-driven decisions. At its core,AgroSage utilizes the Decision Tree Regression algorithm to accurately predict crop prices based onkey factors such as rainfall, wholesale price index, month, and year. By forecasting price trends inadvance, the system enables farmers to optimize their crop choices and maximize profitability.AgroSage is more than just a prediction tool. It is a comprehensive ecosystem tailored formodern agriculture. Key modules include real- time weather forecasting, crop and fertilizerrecommendations, and an integrated platform featuring a digital marketplace, expert-guided chatportal, and a knowledge base. The system is intuitive, scalable, and built with the end-user in mind— ensuring accessibility even for those with limited technological literacy. With AgroSage, webridge the gap between traditional farming and modern technology, enabling smarter agriculturalpractices and contributing to a stronger, more resilient economy.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.46647/ijetms.2025.v09i02.068
- https://doi.org/10.46647/ijetms.2025.v09i02.068
- OA Status
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- References
- 2
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- 10
- OpenAlex ID
- https://openalex.org/W4409708747
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4409708747Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.46647/ijetms.2025.v09i02.068Digital Object Identifier
- Title
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PREDICTION OF UNCERTAIN TRENDS AND PRICES IN AGRICULTURE USING MACHINE LEARNING TECHNIQUESWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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K. Ramesh, Surekha Venkata Mullapudi, A. Pavithra, Kolla Gnapika Sindhu, B. Pranay, R. Subhash Reddy, P. V. Sudheer Kumar Reddy, Preetham ReddyList of authors in order
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https://doi.org/10.46647/ijetms.2025.v09i02.068Publisher landing page
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https://doi.org/10.46647/ijetms.2025.v09i02.068Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.46647/ijetms.2025.v09i02.068Direct OA link when available
- Concepts
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Agriculture, Economics, Econometrics, Machine learning, Artificial intelligence, Agricultural economics, Computer science, Agricultural engineering, Engineering, Geography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.technological | 157 |
| abstract_inverted_index.insights.These | 24 |
| abstract_inverted_index.withunpredictable | 13 |
| abstract_inverted_index.significantfinancial | 35 |
| abstract_inverted_index.agriculturalpractices | 172 |
| abstract_inverted_index.AI-poweredagricultural | 47 |
| abstract_inverted_index.profitability.AgroSage | 98 |
| abstract_inverted_index.fertilizerrecommendations, | 123 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.15948346 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |