Artificial vision systems (AVS) have become very important in precision agriculture applied to produce high-quality and low-cost foods with high functional characteristics generated through environmental care practices. This article reported the design and implementation of a new fuzzy classification architecture based on the RGB color model with descriptors. Three inputs were used that are associated with the average value of the color components of four views of the tomato; the number of triangular membership functions associated with the components R and B were three and four for the case of component G. The amount of tomato samples used in training were forty and twenty for testing; the training was done using the Matlab© ANFISEDIT. The tomato samples were divided into six categories according to the US Department of Agriculture (USDA). This study focused on optimizing the descriptors of the color space to achieve high precision in the prediction results of the final classification task with an error of 536,995×10^-6. The Computer Vision System (CVS) is integrated by an image isolation system with lighting; the image capture system uses a Raspberry Pi 3 and Camera Module Raspberry Pi 2 at a fixed distance and a black background. In the implementation of the CVS, three different color description methods for tomato classification were analyzed and their respective diffuse systems were also designed, two of them using the descriptors described in the literature.

The orange (Citrus sinensis) is a fruit of the Citrus genus, which is part of the Rutaceae family. The orange has gained considerable importance due to its extensive range of applications, including the production of juices, jams, sweets, and extracts. The consumption of oranges confers several nutritional benefits, including flavonoids, vitamin C, potassium, beta-carotene, and dietary fiber. It is crucial to acknowledge that the primary quality criterion employed by consumers and producers is maturity, which is correlated with the visual quality associated with the color of the epicarp. This study proposes the implementation of a computer vision system that estimates the degree of ripeness of oranges Valencia using fuzzy logic (FL); the soluble solids content was determined by refractometry, while the firmness of the fruit was evaluated through the fruit firmness test. The proposed method was divided into five distinct steps. The initial stage involved the acquisition of RGB images. The second stage presents the segmentation of the fruit, which entails the removal of extraneous noise and backgrounds. The third and fourth steps involve determining the centroid of the fruit, and five regions of interest were obtained in the centroid of the fruit of the Citrus Color Index (CII), ranging from 3 × 3 to 11 × 11 pixels. Finally, in the fifth step, a model was created to estimate maturity, °Brix, and firmness using Matlab 2024 and the Fuzzy Logic Designer and Neuro-Fuzzy Designer applications. Consequently, a statistically significant correlation was established between maturity, degree Brix, and firmness, with a value greater than 0.9, using the Citrus Color Index (CII), which reflects the physical–chemical changes that occur in the orange.

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 work, we present a study of non-linear optical response in thin films elaborated with Gelite Bloom and extract of Hibiscus Sabdariffa. Non-linear refraction and absorption effects were studied experimentally (Z-scan technique) and numerically, by considering the transmittance as non-linear absorption and refraction contribution. We observe large phase shifts to far field, and diffraction due to self-phase modulation of the sample. Diffraction and self-diffraction effects were observed as time function. The aim of studying non-linear optical properties in thin films is to eliminate thermal vortex effects that occur in liquids. This is desirable in applications such as non-linear phase contrast, optical limiting, optics switches, etc. Finally, we find good agreement between experimental and theoretical results.