Advancements in Artificial Intelligence and Deep Learning have impacted numerous fields, particularly through innovations like You Only Look Once for object detection and PaddleOCR for optical character recognition in computer vision. These technologies are pivotal in automating and enhancing the accuracy of tasks, such as detecting and extracting characters from identification badges, which have traditionally been prone to manual effort, time consumption, and errors. This study introduces an automated method for detecting and extracting textual data from identification badges, achieving notable efficiency and precision. Our results indicate a Character Error Rate of 0.028 for name recognition and a flawless score for registration number extraction, with a precision rate of 0.992 for the identification badge detection model. By highlighting the importance of automating character extraction and badge detection, this study showcases the ability of Artificial Intelligence and Deep Learning to revolutionize and improve data extraction processes with digital identification systems in professional environments.

Monitoring water quality in the Amazon River remains a significant challenge due to limited accessibility, high sediment loads, intermittent connectivity, and the lack of continuous data in remote regions. This study presents the development and experimental validation of an IoT-based system for real-time water quality monitoring. The platform integrates an STM32WL-based embedded architecture with multiparameter sensing, LoRaWAN communication, and configurable monitoring strategies to enable autonomous operation in dynamic environments. The system was validated through a comparative study involving 698 manually collected samples over eight months and 49,570 automated measurements collected during a three-month field deployment. The evaluation considered measurement consistency, variability and operational autonomy based on CONAMA Resolution No. 357/2005. The results showed good agreement between manual and automated measurements, with MAE/RMSE values of 0.18/0.20 °C for water temperature, 0.36/0.44 for pH, and 12.99/20.09 NTU for turbidity. Additionally, the energy analysis demonstrated autonomous operation under variable solar irradiance, achieving self-sufficiency under typical conditions and maintaining operation for up to 4.9 days without solar input. Taken together, the study provides a robust and scalable framework for continuous monitoring in sediment-rich tropical river systems.

Edge Artificial Intelligence (Edge AI) is used in wildlife monitoring to reduce communication demand, support decision-making, and enable energy-aware operation, yet embedded inference alone does not establish whether a system can sustain useful service after installation. This scoping review searched four bibliographic databases through 5 August 2026 and complemented these searches with studies identified from prior work on the topic, identifying 41 eligible English-language papers published from 2021 onward on camera-primary intelligent wildlife systems. The review aimed to assess operational maturity, field-validated operational role, energy evidence, and operational continuity. Full texts were charted and analyzed across sensing, decision-making, communication, response, energy, and continuity. Post-installation evidence exposed triggering, environmental, connectivity, maintenance, and energy-supply constraints not established through non-field evaluation. Field-validated alert delivery did not extend to quantitative validation linking human action to mitigation outcomes, while field activation of physical intervention alone did not establish behavioral or management effectiveness. Energy evidence was even more limited, with limited direct operational electrical measurement, rare main-state characterization, and no complete lifetime energy budget evidence. By separating architectural capability from the evidence supporting its operational realization, the review provides a systems-level basis for interpreting how far reported functions are actually validated. Across the corpus, optimization often shifts constraints among computation, triggering, communication, energy supply, and availability rather than eliminating them. Wildlife Edge AI should therefore be evaluated as a complete field system under application-relevant conditions.