Field weed emergence modeling

http://pronostico-malezas.frbb.utn.edu.ar/

Goal: The objective of this current project is to develop and validate different modelling approaches, such as non linear and non-parametric regressions, artificial neural networks and Genetic Algorithms to develop more accurate models with the ultimate goal to make more precise predictions of weed seedling emergence to provide growers with consistent tools to make better weed management decisions

Publications

Germination behaviour of Conyza bonariensis to constant and alternating temperatures across different populations

Francisco Valencia-Gredilla, Mar{\'\i}a L Supiciche, Guillermo R Chantre, Jordi Recasens, Aritz Royo-Esnal

Annals of Applied Biology, vol. 176, Wiley Online Library, 2020, pp. 36--46

A flexible and practical approach for real-time weed emergence prediction based on Artificial Neural Networks

Guillermo R. Chantre, Mario R. Vigna, Juan P. Renzi, Aníbal M. Blanco

Biosystems Engineering, vol. 170, 2018, pp. 51-60

Vicia villosa ssp. villosa Roth field emergence model in a semiarid agroecosystem

Juan Pablo Renzi, GR Chantre, MA Cantamutto

Grass and Forage Science, vol. 73, Wiley Online Library, 2018, pp. 146-158

Predicting field weed emergence with empirical models and soft computing techniques

Jose Luis Gonzalez-Andujar, Guillermo R Chantre, C Morvillo, Anibal Manuel Blanco, Frank Forcella

Weed research, vol. 56, Wiley Online Library, 2016, pp. 415--423

Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach

Aníbal M. Blanco, Guillermo R. Chantre, Mariela V. Lodovichi, J. Alberto Bandoni, Ricardo L. López, Mario R. Vigna, Ramón Gigón, Mario R. Sabbatini

Ecological Modelling, vol. 272, 2014, pp. 293-300

A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

G. R. CHANTRE, A. M. BLANCO, F. FORCELLA, R. C. VAN ACKER, M. R. SABBATINI, J. L. GONZALEZ-ANDUJAR

The Journal of Agricultural Science, vol. 152, Cambridge University Press, 2014, pp. 254–262

Modeling Avena fatua seedling emergence dynamics: An artificial neural network approach

Guillermo R. Chantre, Aníbal M. Blanco, Mariela V. Lodovichi, Alberto J. Bandoni, Mario R. Sabbatini, Ricardo L. López, Mario R. Vigna, Ramón Gigón

Computers and Electronics in Agriculture, vol. 88, 2012, pp. 95-102