Weather-Based Crop Yield Prediction

Author(s)	Dhyey Mehta, Juyon Lee
Country	India
Abstract	Agriculture plays a critical role in India’s economy, providing livelihoods to millions of people and contributing significantly to the nation’s GDP. In fact, agriculture is able to support 45% of India’s employed labor force. However, Indian farmers face numerous challenges, including unpredictable weather which leads to poor yields, often leading to financial instability, exacerbating poverty and rural distress. Predicting crop yields using machine learning models offers a promising solution to this problem. The model this paper proposes leverages meteorological data (temperature, rainfall, etc.) as well as farming practice data (use of pesticide, fertilizer etc.) to help farmers predict their yield. The model presented in this paper ultimately had a mean squared error of 4.16 and a correlation value of 0.761 when predicting yields.
Keywords	Crop yield prediction, Machine Learning Model, Environmental Science
Field	Biology > Agriculture / Botany
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-06-02
Cite This	Weather-Based Crop Yield Prediction - Dhyey Mehta, Juyon Lee - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.21688
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.21688
Short DOI	https://doi.org/gtxrqz

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