Journal of Digital Engineering and Business Management

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Agricultural Yield Forecasting Using Machine Learning Algorithms

Authors: Nakka Shivani, Dr. M. Srinivas

Abstract

Fertilizer value updates are useful because they warn producers to changes in fertiliser market pricing, which can affect agricultural output. The major point of this essay is that crop selection can be extremely beneficial to farmers and producers. The Indian economy gains from higher agricultural output. Various terrain conditions exist. As a result, a classification system is implemented to determine the quality of fertiliser. This method also shows the rate of high- and low-grade fertiliser. Combining multiple classifiers can generate an ensemble of classifiers capable of producing more accurate predictions. To determine the classifiers’ outputs, a decision-making classification mechanism is also implemented. This approach is employed to forecast future crops.

Keywords

Crop Yield Prediction, Machine Learning, Time Series Analysis, Smart Farming, Precision Farming, Predictive Analytics, Regression Models, Remote Sensing

Article Information

Volume: 2
Issue: 2
Published Date: 27/05/2026
DOI: https://doi.org/10.5281/zenodo.20604766
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