Characterization of multispectral aerial images of sugarcane

In this paper we present a implementation and characterization the status of sugarcane plantations based on the analysis of multispectral aerial images. Currently there are no precise techniques to estimate objectively the cane area fall or overturned, and this causes significant losses in crop productivity and industrialization. For the realization of this work was made a dataset benchmark images, and implemented a software from which were obtained indicators related to agronomic phenomenon, and analyzes of the data generated. In addition was used Principal Component Analysis to visualize and integrate statistical texture features. The results indicate the statistical features used characterize partially the sugar cane phenomena and suggest include another texture focus to complement this feature set, previous to built an cane identification process.