Infrared thermography (IRT) is a technique used to diagnose Photovoltaic (PV) installations to detect sub-optimal conditions. The increase of PV installations in smart cities has generated the search for technology that improves the use of IRT, which requires irradiance conditions to be greater than 700 W/${\mathrm{m}}^2$, making it impossible to use at times when irradiance goes under that value. This project presents an IoT platform working on artificial intelligence (AI) which automatically detects hot spots in PV modules by analyzing the temperature differentials between modules exposed to irradiances greater than 300 W/${\mathrm{m}}^2$. For this purpose, two AI (Deep learning and machine learning) were trained and tested in a real PV installation where hot spots were induced. The system was able to detect hot spots with a sensitivity of 0.995 and an accuracy of 0.923 under dirty, short-circuited, and partially shaded conditions. This project differs from others because it proposes an alternative to facilitate the implementation of diagnostics with IRT and evaluates the real temperatures of PV modules, which represents a potential economic saving for PV installation managers and inspectors.

This paper compares multiple techniques to detect suboptimal conditions in the PV system. Detection of suboptimal conditions in the PV system is required to achieve optimal photovoltaic (PV) systems. Therefore, maintenance managers need to choose the most suitable techniques objectively. However, there is a lack of objective information comparing the effectiveness of the methods. This article calculates and compares the effectiveness of Infrared thermography (IRT), visual inspection (VI), and electrical analysis (EA) in detecting soiling, partial shadows, and electrical faults experimentally. The results showed that the VI was the best at detecting soiling and partial shading with 100% of effectiveness. IRT and EA had an effectiveness of 78% and 73%, respectively, detecting the three types of conditions under study. It was not possible to achieve maximum detection using only one of the techniques, but that VI must be combined with IR or EA. This research represents a significant contribution by achieving an objective comparison between techniques for detecting suboptimal conditions, being very useful to guide PV system maintainers and designers of fault detection techniques.

Sperm morphology analysis is critical for assessing bovine fertility, since it provides insight into bull reproductive potential as well as subfertility and infertility. Traditional sperm morphology analysis is time-consuming, subjective, and prone to human error, all of which highlight the need for automated, objective solutions. This study presents the design and implementation of a computer-aided system for bovine sperm morphology analysis, leveraging deep learning models to detect and classify sperm cells based on their morphological characteristics. Using micrographs of bull sperm, we present a sequential deep learning framework that automatically detects morphological sperm aberrations. The model segments and analyzes each cell, identifying defects in the head, neck/midpiece, tail, and residual cytoplasm. Specifically, the system employs the YOLOv7 object detection framework, trained on a dataset of 277 annotated images comprising six morphological categories, to automatically identify and classify sperm abnormalities. The experimental results demonstrate a global mAP@50 of 0.73, precision of 0.75, and recall of 0.71, indicating a balanced tradeoff between accuracy and efficiency. By reducing reliance on manual analysis, this work enhances efficiency and accuracy in animal reproduction laboratories, contributing to veterinary reproduction through a cost-effective and scalable solution for sperm quality assessment.