The exponential growth of global poultry production highlights the critical need for efficient flock management, particularly in accurately counting chickens to optimize operations and minimize economic losses. This study advances the application of artificial intelligence (AI) in agriculture by developing and validating an AI-driven automated poultry flock management system using the YOLOv8 object detection model. The scientific objective was to address challenges such as occlusions, lighting variability, and high-density flock conditions, thereby contributing to the broader understanding of computer vision applications in agricultural environments. The practical objective was to create a scalable and reliable system for automated monitoring and decision-making, optimizing resource utilization and improving poultry management efficiency. The prototype achieved high precision (93.1%) and recall (93.0%), demonstrating its reliability across diverse conditions. Comparative analysis with prior models, including YOLOv5, highlights YOLOv8’s superior accuracy and robustness, underscoring its potential for real-world applications. This research successfully achieves its objectives by delivering a system that enhances poultry management practices and lays a strong foundation for future innovations in agricultural automation.

The poultry industry faces significant challenges in efficiently monitoring large populations, especially under resource constraints and limited connectivity. This paper introduces the Automated Counting and Monitoring System for Poultry Tracking (ACMSPT), an innovative solution that integrates edge computing, Artificial Intelligence (AI), and the Internet of Things (IoT). The study begins by collecting a custom dataset of 1300 high-resolution images from real broiler farm environments, encompassing diverse lighting conditions, occlusions, and growth stages. Each image was manually annotated and used to train the YOLOv10 object detection model with carefully selected hyperparameters. The trained model was then deployed on an Orange Pi 5B single-board computer equipped with a Neural Processing Unit (NPU), enabling on-site inference and real-time poultry tracking. System performance was evaluated in both small- and commercial-scale sheds, achieving a precision of 93.1% and recall of 93.0%, with an average inference time under 200 milliseconds. The results demonstrate that ACMSPT can autonomously detect anomalies in poultry movement, facilitating timely interventions while reducing manual labor. Moreover, its cost-effective, low-connectivity design supports broader adoption in remote or resource-limited environments. Future work will focus on improving adaptability to extreme conditions and extending this approach to other livestock management contexts.

Just like at home, breathing clean air is vital in the workplace. Unfortunately, dust, gases, and other pollutants can compromise air quality in industrial buildings, posing risks to worker health, safety, productivity, and compliance with regulations. To address this, a new research project developed an intelligent air pollution monitoring system for industrial settings. This system leverages artificial intelligence technologies and software to analyze real-time sensor data, identifying potential health hazards. Implemented in a real industrial building, the prototype performed successfully, accurately detecting air pollution, and alerting workers to potential risks.

Although lightweight deep learning models have shown promise for livestock monitoring, there is still limited evidence regarding their comparative performance and practical deployment under real broiler production conditions characterized by high stocking density, severe occlusion, and constrained computational resources. In this context, the present study aimed to evaluate three lightweight object detection architectures for broiler monitoring and to determine their suitability for low-cost edge deployment in settings relevant to small and medium-sized producers. A novel dataset, publicly released through Zenodo to support reproducibility, was constructed from images acquired in both a prototype farm and a high-density commercial facility. These environments captured the visual complexity of intensive broiler production, where overlapping individuals and frequent occlusion challenge detection performance. YOLOv10s, Faster R-CNN, and EfficientDet-D0 were trained and evaluated for detection accuracy and computational efficiency. YOLOv10s achieved the best results, with a mean Average Precision (mAP) of 0.95, whereas Faster R-CNN and EfficientDet-D0 were less suitable for crowded scenes due to region proposal saturation and limited feature-extraction capacity. The selected model was further implemented on a Raspberry Pi 5, achieving a stable latency of 392.17 ms. These results demonstrate that YOLOv10s provides a robust balance between accuracy and efficiency for local broiler monitoring on affordable hardware, while also indicating that active thermal management is necessary to maintain operational stability under real-world conditions.

Real-time vessel tracking and environmental assessment in developing regions face significant challenges due to the high cost and proprietary constraints of commercial Automatic Identification System (AIS) services. We introduce MAGI, an open-source, low-cost, IoT-distributed architecture that integrates Orange Pi 5 edge nodes with software-defined radio (SDR) AIS receivers and containerized microservices to capture, preprocess, and stream AIS messages. During a ten-day field campaign in Panama, our decentralized deployment processed over 500,000 AIS transmissions, achieving 99% uptime and delivering vessel position and speed updates with sub-second latency. Based on the collected data, we also evaluated system scalability, energy consumption, and per node cost, demonstrating that a complete coastal network can be deployed for under USD 1200 per site. These results confirm that MAGI is a scalable, secure, and affordable IoT solution for AIS-based vessel tracking and environmental monitoring in resource-constrained settings.