Evaluation of machine learning approaches for prediction of pigeon pea yield based on weather parameters in India-Reference-Cited by-同舟云学术

Evaluation of machine learning approaches for prediction of pigeon pea yield based on weather parameters in India

Published:2022-11-03 Issue:1 Volume:67 Page:165-180
ISSN:0020-7128
Container-title:International Journal of Biometeorology
language:en
Short-container-title:Int J Biometeorol

Author:

Sridhara Shankarappa^ORCID,Manoj Konapura Nagaraja,Gopakkali Pradeep,Kashyap Girish R.,Das Bappa,Singh Kamlesh Kumar,Srivastava Amit Kumar

Funder

India Meteorological Department

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Atmospheric Science,Ecology

Link

https://link.springer.com/content/pdf/10.1007/s00484-022-02396-x.pdf

Reference51 articles.

1. Amaratunga V, Wickramasinghe L, Perera A, Jayasinghe J, Rathnayake U (2020) Artificial neural network to estimate the paddy yield prediction using climatic data. Mathem Probl Eng Article ID 8627824.https://doi.org/10.1155/2020/8627824

2. Anonymous (2020) https://www.fao.org/faostat/en/#data/QCL. Accessed 3 Apr 2022

3. Anonymous (2021) Pocket Book of Agricultural Statistics. https://eands.dacnet.nic.in/PDF/Pocket%202020-%20Final%20web%20file.pdf. Accessed 6 Apr 2022

4. Bajya DR, Monga D, Tyagi MP, Meena BL (2010) Population dynamics of Helicoverpa armigera on chick pea, pigeon pea and cotton in correlation with weather parameters. Ann Pl Protec Sci 18(1):227–229

5. Bali N, Singla A (2021) Deep learning based wheat crop yield prediction model in Punjab region of North India. App Artificial Intel 35(15):1304–1328. https://doi.org/10.1080/08839514.2021.1976091

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