Abstract
Data incompleteness and data scarcity are common problems in agroecological modelling. Moreover, agroecological processes depend on historical data that could be fed into a model in a vast number of ways. This work shows a case study of modelling in agroecology using artificial neural networks. The variable to be modelled is sugar cane yield and for this purpose we used climate, soil, and other environmental variables. Regarding the data precision issue, we trained different neural models using monthly and weekly data in order to compare their performance. Furthermore, we studied the influence of using incomplete observations in the training process in order to include them and thus use a larger quantity of input patterns. Our results show that the gain in observations due to the inclusion of incomplete data is preferable in this application.
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Satizábal M., H.F., Jiménez R., D.R., Pérez-Uribe, A. (2007). Consequences of Data Uncertainty and Data Precision in Artificial Neural Network Sugar Cane Yield Prediction. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_139
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DOI: https://doi.org/10.1007/978-3-540-73007-1_139
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