Marriage between variable selection and prediction methods to model plant disease risk

Author:

Suarez FrancoORCID,Bruno Cecilia,Kurina Giannini Franca,Giménez Pecci M. Paz,Rodriguez Pardina Patricia,Balzarini Mónica

Publisher

Elsevier BV

Subject

Plant Science,Soil Science,Agronomy and Crop Science

Reference52 articles.

1. Agresti, A., & Kateri, M., 2014, Some Remarks on Latent Variable Models in Categorical Data Analysis. Https://Doi.Org/10.1080/03610926.2013.814783, 43(4), 801–814. https://doi.org/10.1080/03610926.2013.814783.

2. Effect of missing values on variance component estimates in multienvironment trials;Aguate;Crop Sci.,2019

3. Incidence of begomoviruses and climatic characterisation of Bemisia tabaci-geminivirus complex in soybean and bean in Argentina [Incidencia de begomovirus y caracterización climática del complejo Bemisia tabaci-geminivirus en soja y poroto en Argentina];Alemandri;AgriScientia,2012

4. Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming;Ayoub Shaikh;Comput. Electron. Agric.,2022

5. Balzarini, M., Bruno, C., Córdoba, M., & Teich, I. (2015). Herramientas en el análisis estadístico multivariado. Córdoba, Argentina.

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