GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzzy logic, which takes the information from the sensors and correct the vehicle’s absolute position according to its latitude and longitude. This correction is performed by two fuzzy systems, one to correct the latitude and the other to correct the longitude, which are trained using the MATLAB ANFIS tool. The positioning correction system is trained and tested with two different datasets. One of them collected with a Pmod GPS sensor and the other a public dataset, which was taken from routes in Brazil. To compare our proposal, an unscented Kalman filter (UKF) was implemented. The main finding is that the proposed fuzzy systems achieve a performance of 69.2% higher than the UKF. Furthermore, fuzzy systems are suitable to implement in an embedded system such as the Raspberry Pi 4. Another finding is that the logical operations facilitate the creation of non-linear functions because of the ‘if else’ structure. Finally, the existence justification of each fuzzy system section is easy to understand.

In this paper, a method to monitor the growth of bean plants from images taken in their vegetative stage is presented. Through a fuzzy system and the RGB image components, the growth stages of the plant are classified to achieve a powerful tool in the precision agricultural field, which can reduce cost and increase system portability. This also serves as a reference for the growth of the plant according to its age, vigor, and healthy that could help to create the necessary environmental conditions. To carry out this research, the development of twenty bean plants was periodically monitored from their germinal to the first trifoliate leaf stage. Images were obtained with controlled background and lighting; later they were segmented by color to count the pixel’s average for each case. These data were used to propose six different fuzzy systems to choose the best one within them. Finally, it was found that the artificial vision system can identify the vegetative stages of germination, emergence, primary leaves, and first trifoliate leaf.