An Efficient Crop Yield Prediction System Using Machine Learning

Authors

DOI:

https://doi.org/10.4108/eetiot.5333

Keywords:

Farming, Regression, Crop Prediction, Mean Absolute Error, MAE, Root mean square Error, RMSE, R2 Score

Abstract

Farming is considered the biggest factor in strengthening the economy of any country. It also has significant effects on GDP growth. However, due to a lack of information and consultation, farmers suffer from significant crop losses every year. Typically, farmers consult agricultural officers for detecting crop diseases. However, the accuracy of predictions made by agricultural officers based on their experience is not always reliable. If the exact issues are not identified at right time then it results in a heavy crop loss. To address this issue, Computational Intelligence, also known as Machine Learning, can be applied based on historical data. In this study, an intelligent crop yield prediction algorithm is developed using various types of regression-based algorithms. The Crop Yield Prediction Dataset from the Kaggle repository is used for model training and evaluation. Among all different regression methods Random Forest has shown the better performance in terms of R2 score and other errors.

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Published

07-03-2024

How to Cite

[1]
D. Swain, S. Lakum, S. Patel, P. Patro, and Jatin, “An Efficient Crop Yield Prediction System Using Machine Learning”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.

Issue

Vol. 10 (2024): EAI Endorsed Transactions on Internet of Things

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Short communication

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