Abstract
Since agriculture is the foundation of all economies, accurately predicting crop yield is essential to determining farmers' financial security and managing risks. In the following research, we explore how supervised machine learning algorithms are revolutionizing forecasting of crops' yield and the ensuing implications for agriculture. Crop yield predictions enable farmers and policymakers to make data-driven, well-informed decisions that promote a more resilient and sustainable agricultural landscape. This study examines the supervised machine learning approach used in crop yield prediction, highlighting how well they capture the intricate patterns and trends present in agricultural markets. Equipped with dependable projections, farmers may maximize planting choices, effectively allocate resources, and minimize hazards, thereby augmenting their earnings. The ability to anticipate future crop prices facilitates proactive measures to address potential challenges such as food inflation and market volatility. Machine learning models offer a strong basis for forecasting by combining historical data, weather trends, and market indicators. This paper's primary contributions include research on machine learning-based crop yield prediction, as well as a critical assessment of the benefits and drawbacks of various machine learning methods.
Original language | English (US) |
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Title of host publication | 2024 2nd World Conference on Communication and Computing, WCONF 2024 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350395327 |
State | Published - 2024 |
Externally published | Yes |
Event | 2nd World Conference on Communication and Computing, WCONF 2024 - Raipur, India Duration: Jun 12 2024 → Jun 14 2024 |
Publication series
Name | 2024 2nd World Conference on Communication and Computing, WCONF 2024 |
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Conference
Conference | 2nd World Conference on Communication and Computing, WCONF 2024 |
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Country/Territory | India |
City | Raipur |
Period | 6/12/24 → 6/14/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Crops yield prediction
- Forecasting
- Linear Regression
- Supervised Machine Learning