Artificial neural networks for corn and soybean yield prediction

By M. Kaul, R. Hill, C. Walthall. 2005

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Prediction of Wheat Production Using Artificial Neural Networks and Investigating Indirect Factors Affecting It: Case Study in Canterbury Province, New Zealand (2015, methodology): “However, using nonlinear methods mainly artificial neural networks (ANNs) and fuzzy logics for yield prediction have become more common in recent studies (Salehi et al., 1998; Kominakis et al., 2002; Kaul et al., 2005; Sharma et al., 2007; Papageorgiou et al., 2011; Papageorgiou et al., 2013).”
Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “Moreover, NNs outperformed the traditional linear regression methods in the prediction of crop yield by using remote sensing vegetation indexes and other factors (Fortin et al., 2011; Kaul et al., 2005; Safa et al., 2014).”
Big Data in Agriculture: A Challenge for the Future (2018, methodology): “Therefore, popular SL algorithms include artificial neural networks (Kaul et al. 2005; Uno 2005; Chen and Mcnairn 2006; Khoshnevisan et al. 2014), decision trees (Veenadhari, Mishra, and Singh 2011), K-means clustering (Shawe-Taylor and Cristianini 2004), support vector machines (Radhika and Shashi ”