The smallholder farms located in sub-Sahara Africa are typically characterize by heterogeneous mosaic of crops, soils, weather and farm practices. Automatic crop identification over smallholder fields is challenging when one only uses the spectral information of very high spatial resolution image time series. The extraction of spatial-spectral information is important to reach classifier accuracy. We deploy cloud computing techniques to allow working with thousands of features derived from an image time series. However, this number of extracted features is forces one to select the most important features for identification routines. This paper introduces a simple feature selection method based on Random Forest - the Guided Regularized Random Forest (GRRF) - which reduces feature dimensionality without loss data information. Preliminary experiments show that we can reach an overall accuracy by around 63%, and the results using random forests trained by GRRF features improve by around 2.5% the results by a Random Forest classifier that uses all the features.