Comparison between the performance of artificial neural network and adaptive neuro-fuzzy inference system in modelling crop evapotranspiration of a maize crop in soil amended with biochar and inorganic fertilizer-Reference-Cited by-同舟云学术

Comparison between the performance of artificial neural network and adaptive neuro-fuzzy inference system in modelling crop evapotranspiration of a maize crop in soil amended with biochar and inorganic fertilizer

Published:2023-04-05 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG)
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Author:

Faloye Oluwaseun Temitope¹,Ajayi Ayodele Ebenezer²,Babalola Toju³,Adabembe Bolaji⁴,Adeyeri O. E.⁵,Ogunrinde Akinwale Tope⁶,Okunola Abiodun⁶,Fashina Abayomi⁷

Affiliation:

1. Landmark University,Department of Agricultural and Biosystems Engineering,Omu Aran,Kwara State,Nigeria

2. Federal University of Technology,Agricultural and Environmental Engineering,Akure,Nigeria

3. Water Resources Water Resources and Agrometeorology,Oye,Ekiti,Nigeria

4. Federal University,Water Resources and Agrometeorology,Oye Oye,Ekiti,Nigeria

5. School of Energy and Environment, City University of Honk Kong,Kowloon,Hong Kong Special Administrative Region

6. Landmark University,Agricultural and Bio-systems Engineering,Omu Aran,Nigeria

7. Federal University,Soil Science and Land Resources Management,Oye Oye,Ekiti,Nigeria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10124379/10124380/10124578.pdf?arnumber=10124578

Reference25 articles.

1. The effect of autocorrelation on the meteorological parameters trend

2. Evapotranspiration evaluation models based on machine learning algorithms—A comparative study

3. Maize yield and nutrition during 4 years after biochar application to a Colombian savanna oxisol

4. Synergistic effects of biochar and inorganic fertiliser on maize (zea mays) yield in an alfisol under drip irrigation

5. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

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