Maize Crop Yield Prediction Using Machine Learning Regression Approach

Author:

Bedane Tarikwa Tesfa,Kebede Kidistie Bizuneh,Mohapatra Sudhir Kumar,Das Tapan Kumar,Tripathy Asis Kumar

Publisher

Springer Nature Singapore

Reference16 articles.

1. World Bank (2021) Africa’s Pulse, No. 23, April 2021: the future of work in Africa: emerging trends in digital technology adoption. World Bank, Washington, DC

2. Pareek S (2021) Agriculture sector in Ethiopia: challenges, progress, and potential. LinkedIn [Online]. Available at: https://www.linkedin.com/pulse/agriculture-sector-ethiopia-challenges-progress-potential-pareek

3. Gebremedhin M, Osgood S, Mohammed M, De Groote H (2022) Enhancing smallholder wheat yield prediction through sensor fusion and phenology with machine learning and deep learning methods. Agriculture 12(9):1352

4. Kaneko S, Zhu J, Kang S, Miura M (2022) Maize yield prediction using machine learning: a review. Remote Sens 14(9):1990

5. Reddy S, Gowda S (2020) Design and implementation of crop yield prediction model in agriculture. Int J Sci Technol Res 8(01):545–549

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