Optimizing Crop Prediction using ML and DL

Authors: N.L. Bhale, Sahil V. Shaikh, Ayush M. Wagh, Amar D. Patait, Moiz S. Shaikh

Country: India

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Abstract: The Crop Yield Prediction and Fertilizer Recommendation System using Hybrid Machine Learning Algorithms is an innovative solution designed to enhance agricultural practices and increase crop productivity. This system leverages a combination of machine learning techniques, including both traditional statistical models and advanced deep learning algorithms, to accurately forecast crop yields. By analyzing historical data, environmental factors, and crop-specific information, the system predicts future yields, helping farmers make informed decisions about planting, harvesting, and resource allocation. Furthermore, the system incorporates a fertilizer recommendation component, which suggests the optimal type and quantity of fertilizers based on soil nutrient analysis and crop requirements, promoting efficient resource management and sustainability. This hybrid approach offers a comprehensive and data-driven solution for precision agriculture, improving crop yield while minimizing the environmental impact of excessive fertilizer use.

Keywords: Crop Yield Prediction, Machine Learning, Crop Disease Prediction, Fertilizer Recommendation

Paper Id: 230426

Published On: 2024-01-01

Published In: Volume 12, Issue 1, January-February 2024

Cite This: Optimizing Crop Prediction using ML and DL - N.L. Bhale, Sahil V. Shaikh, Ayush M. Wagh, Amar D. Patait, Moiz S. Shaikh - IJIRMPS Volume 12, Issue 1, January-February 2024.

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