Mapping sunflower yield as affected by Ridolfia segetum patches and elevation by applying evolutionary product unit neural networks to remote sensed data

Recent advances in remote sensing technology have triggered the need for highly flexible modelling methods to estimate several crop parameters in precision farming. The aim of this work was to determine the potential of evolutionary product unit neural networks (EPUNNs) for mapping in-season yield and forecasting systems of sunflower crop in a natural weed-infested farm. Aerial photographs were taken at the late vegetative (mid-May) growth stage. Yield, elevation and weed data were combined with multispectral imagery to obtain the dataset. Statistical and EPUNNs approaches were used to develop different yield prediction models. The results obtained using different EPUNN models show that the functional model and the hybrid algorithms proposed provide very accurate prediction compared to other statistical methodologies used to solve that regression problem.