The present work demonstrates the implementation of image processing techniques to analyze the color and texture of agroindustrial products, in this case, of the piloncillo or panela in its granular and cone variety. A technological tool based on software was developed, capable of quantifying color and texture and thus implementing quality controls to support the commercialization of this product made in Huasteca Potosina towards global markets. An unsupervised classification method (clustering) is proposed to define the color or colors of the piloncillo, called K-means. In the case of texture analysis, second-order statistical methods derived from the co-occurrence matrix and six Haralick textural descriptors are used: contrast, homogeneity, energy, ASM, correlation and dissimilarity. The developed prototype was validated in 24 producing communities of granulated and cone-shaped piloncillo belonging to the municipalities of Tanlajas and Tancanhuitz, S.L.P. The results of the analysis of color and texture have been related to the manufacturing methods that are currently used, to identify improvements that help standardize the production process.

This research work shows that with the use of remote sensing technology it is possible to more effectively fulfill two of the purposes pursued by farmers in the field; manage crops more efficiently and include environmental care in decision-making. Specifically, remote sensing is applied in the context of precision agriculture through geographic information systems (GIS), unmanned aerial vehicles (UAV), multispectral sensors that capture the reflectance of the infrared band of the light spectrum (for interpretation of the biochemical state of the crop), global geopositioning systems (GPS), among others. This study limits the use of this technology to the processing of multispectral images obtained by aerial photogrammetry, and its subsequent treatment for the generation of orthoimages, the calculation of the NDVI vegetation index and the classification of land cover by clustering. Finally, the effect of classification with RGB and multispectral images is analyzed.

It’s presented the prototype of a monitoring system to measure the increase in the level of course of rivers for the timely prevention before a contingency of flooding. Are used hardware and software open platforms, which allow the integration and exchange of sensor modules to increase the functionality and scope, they also employ standard communication protocols that facilitate the scalability and reconfigurability of the nodes. It’s considered a solar system cells to supply energy ensuring continuous operation, also includes a real-time data transmission using wireless technology. As an Early warning System its main function is to reduce or prevent the damage caused by threats of floods. The Huasteca area has a large amount of rivers, which tend to be near populated areas or settlements, in times of rain, the majority are likely to grow suddenly and exponentially. The functional tests of the prototype were conducted in a controlled environment, identifying the appropriate process for the collection, reading and transfer of data, as well as their interpretation. The technology presented is feasible for their deployment, it allows the integration of more sensors that could form a monitoring network of larger scope.