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Artificial neural networks for corn and soybean yield prediction

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  • Kaul, Monisha
  • Hill, Robert L.
  • Walthall, Charles

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  • Kaul, Monisha & Hill, Robert L. & Walthall, Charles, 2005. "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, Elsevier, vol. 85(1), pages 1-18, July.
    • Handle: RePEc:eee:agisys:v:85:y:2005:i:1:p:1-18
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    1. Gbadegesin, Adeniyi, 1987. "Soil rating for crop production in the savanna belt of south-western Nigeria," Agricultural Systems, Elsevier, vol. 23(1), pages 27-42.
    2. Basso, B. & Ritchie, J. T. & Pierce, F. J. & Braga, R. P. & Jones, J. W., 2001. "Spatial validation of crop models for precision agriculture," Agricultural Systems, Elsevier, vol. 68(2), pages 97-112, May.
    3. Liang, T. & Akram Khan, M. & Manrique, L. A. & Parmar, Rajbir, 1986. "Rating land for crop introduction," Agricultural Systems, Elsevier, vol. 21(2), pages 107-127.
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    1. Jalilov, Shokhrukh-Mirzo & Rahman, Wakilur & Palash, Salauddin & Jahan, Hasneen & Mainuddin, Mohammed & Ward, Frank A., 2022. "Exploring strategies to control the cost of food security: Evidence from Bangladesh," Agricultural Systems, Elsevier, vol. 196(C).
    2. Renato Domiciano Silva Rosado & Cosme Damião Cruz & Leiri Daiane Barili & José Eustáquio de Souza Carneiro & Pedro Crescêncio Souza Carneiro & Vinicius Quintão Carneiro & Jackson Tavela da Silva & Moy, 2020. "Artificial Neural Networks in the Prediction of Genetic Merit to Flowering Traits in Bean Cultivars," Agriculture, MDPI, vol. 10(12), pages 1-11, December.
    3. Emerson Rodolfo Abraham & João Gilberto Mendes dos Reis & Oduvaldo Vendrametto & Pedro Luiz de Oliveira Costa Neto & Rodrigo Carlo Toloi & Aguinaldo Eduardo de Souza & Marcos de Oliveira Morais, 2020. "Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production," Agriculture, MDPI, vol. 10(10), pages 1-18, October.
    4. Xu, Chang & Katchova, Ani L., 2019. "Predicting Soybean Yield with NDVI Using a Flexible Fourier Transform Model," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 51(3), pages 402-416, August.
    5. Omolola M. Adisa & Joel O. Botai & Abiodun M. Adeola & Abubeker Hassen & Christina M. Botai & Daniel Darkey & Eyob Tesfamariam, 2019. "Application of Artificial Neural Network for Predicting Maize Production in South Africa," Sustainability, MDPI, vol. 11(4), pages 1-17, February.
    6. Kelvin López-Aguilar & Adalberto Benavides-Mendoza & Susana González-Morales & Antonio Juárez-Maldonado & Pamela Chiñas-Sánchez & Alvaro Morelos-Moreno, 2020. "Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter," Agriculture, MDPI, vol. 10(4), pages 1-14, April.
    7. Szulczewski, Wieslaw & Zyromski, Andrzej & Biniak-Pieróg, Malgorzata & Machowczyk, Anna, 2010. "Modelling of the effect of dry periods on yielding of spring barley," Agricultural Water Management, Elsevier, vol. 97(5), pages 587-595, May.
    8. Jules F. Cacho & Jeremy Feinstein & Colleen R. Zumpf & Yuki Hamada & Daniel J. Lee & Nictor L. Namoi & DoKyoung Lee & Nicholas N. Boersma & Emily A. Heaton & John J. Quinn & Cristina Negri, 2023. "Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning," Energies, MDPI, vol. 16(10), pages 1-16, May.
    9. Pourmohammadali, Behrooz & Hosseinifard, Seyed Javad & Hassan Salehi, Mohammad & Shirani, Hossein & Esfandiarpour Boroujeni, Isa, 2019. "Effects of soil properties, water quality and management practices on pistachio yield in Rafsanjan region, southeast of Iran," Agricultural Water Management, Elsevier, vol. 213(C), pages 894-902.
    10. Srinivasagan N. Subhashree & C. Igathinathane & Adnan Akyuz & Md. Borhan & John Hendrickson & David Archer & Mark Liebig & David Toledo & Kevin Sedivec & Scott Kronberg & Jonathan Halvorson, 2023. "Tools for Predicting Forage Growth in Rangelands and Economic Analyses—A Systematic Review," Agriculture, MDPI, vol. 13(2), pages 1-30, February.
    11. Kuruguntu Mohan Krithika & Nachimuthu Maheswari & Manickam Sivagami, 2022. "Models for feature selection and efficient crop yield prediction in the groundnut production," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 68(3), pages 131-141.
    12. Jiménez, Daniel & Cock, James & Jarvis, Andy & Garcia, James & Satizábal, Héctor F. & Damme, Patrick Van & Pérez-Uribe, Andrés & Barreto-Sanz, Miguel A., 2011. "Interpretation of commercial production information: A case study of lulo (Solanum quitoense), an under-researched Andean fruit," Agricultural Systems, Elsevier, vol. 104(3), pages 258-270, March.
    13. Taheri-Rad, Alireza & Khojastehpour, Mehdi & Rohani, Abbas & Khoramdel, Surur & Nikkhah, Amin, 2017. "Energy flow modeling and predicting the yield of Iranian paddy cultivars using artificial neural networks," Energy, Elsevier, vol. 135(C), pages 405-412.
    14. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    15. Bazrafshan, Ommolbanin & Ehteram, Mohammad & Moshizi, Zahra Gerkaninezhad & Jamshidi, Sajad, 2022. "Evaluation and uncertainty assessment of wheat yield prediction by multilayer perceptron model with bayesian and copula bayesian approaches," Agricultural Water Management, Elsevier, vol. 273(C).
    16. Ji, Li-Qun, 2015. "An assessment of agricultural residue resources for liquid biofuel production in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 561-575.
    17. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    18. García-Alonso, Carlos R. & Torres-Jiménez, Mercedes & Hervás-Martínez, César, 2010. "Income prediction in the agrarian sector using product unit neural networks," European Journal of Operational Research, Elsevier, vol. 204(2), pages 355-365, July.

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