The increasing broiler demand due to overpopulation and meat imports presents challenges in poultry farming, including management, disease control, and chicken observation in varying light conditions. To address these issues, the development of AI-based management processes is crucial, especially considering the need for detecting pathological phenomena in intensive rearing. In this study, a dataset consisting of visual and thermal images was created to capture pathological phenomena in broilers. The dataset contains 10,000 images with 50,000 annotations labeled as lethargic chickens, slipped tendons, diseased eyes, stressed (beaks open), pendulous crop, and healthy broiler. Three versions of the YOLO-based algorithm (v8, v7, and v5) were assessed, utilizing augmented thermal and visual image datasets with various augmentation methods. The aim was to develop thermal- and visual-based models for detecting broilers in complex environments, and secondarily, to classify pathological phenomena under challenging lighting conditions. After training on acknowledged pathological phenomena, the thermal YOLOv8-based model demonstrated exceptional performance, achieving the highest accuracy in object detection (mAP50 of 0.988) and classification (F1 score of 0.972). This outstanding performance makes it a reliable tool for both broiler detection and pathological phenomena classification, attributed to the use of comprehensive datasets during training and development, enabling accurate and efficient detection even in complex environmental conditions. By employing both visual- and thermal-based models for monitoring, farmers can obtain results from both thermal and visual viewpoints, ultimately enhancing the overall reliability of the monitoring process.

Optimizing feeding rates in recirculating aquaculture systems (RAS) is crucial for ensuring fish growth, health, and system efficiency. This research introduces a novel approach to RAS feeding control using a Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm. The developed system integrates feeding rate optimization with comprehensive water quality management to improve overall performance and stability. The DDPG controller demonstrated superior tracking accuracy, reduced feed consumption, and improved operational stability compared to traditional control methods such as Model Predictive Control (MPC), PID, and Bang-Bang control. The learned policy-maintained feeding rates within optimal ranges while adapting to dynamic system requirements and environmental conditions. The integration of water quality monitoring and control further enhanced system stability, ensuring critical parameters remained within target ranges. Comparative analysis revealed the DDPG controller’s advantages in terms of faster recovery times after environmental perturbations, improved long-term stability, and significant economic benefits through reduced operational costs and increased efficiency. The robustness and adaptability of the system were validated through comprehensive testing under various fault conditions, growth phases, and system scales. The successful development and evaluation of the DDPG-based RAS feeding control system represents a significant advancement in aquaculture management. The demonstrated improvements in efficiency, stability, and economic performance establish the potential for this approach to revolutionize feeding practices in commercial RAS operations. Further research and development efforts can build upon these findings to advance the state-of-the-art in intelligent aquaculture management systems and promote the sustainability and profitability of the industry.

Degradation performance of photovoltaic modules (SPV) by real conditions has become increasingly problematic. In dusty areas, dust accumulation is one of the main concerns that may cause a significant determination of SPV efficiency. In the current study, the effect of four dust-accumulated densities of 6, 12, 18, and 24 g/m² have been investigated in outdoor conditions in Cairo, Egypt. The performance evaluation of SPV modules in the form of front and backside temperatures of the SPV module has been evaluated in addition to current, voltage, power, and efficiency of the SPV modules. The results showed that, as compared with a clean SPV module, with increasing dust density from 6 to 24 g/m² the frontside temperature of SPV modules were lower by 6–8 ℃. While their backside temperatures were found to be higher by 2–6 ℃. In addition, the difference between the backside and frontside temperatures of the SPV module ranged from 5 to 14 ℃ for dust modules as compared with 3 ℃ for the clean SPV module. The output power and efficiency of dusty SPV modules were found to be lower by 6–45% and 13–38%, respectively as compared with clean SPV module. The results clearly showed the importance of properly maintaining and servicing the SPV modules to avoid their degradation by dust accumulated.

Recirculating aquaculture systems (RAS) represent a sustainable approach to intensive fish production, but their operational complexity poses significant challenges for fish farmers. Addressing these challenges requires managing the complexity of multi-objective optimization with shifting priorities, ensuring interpretability of sophisticated AI models for practical users, and adapting control strategies to the dynamic requirements of fish development stages. This study introduces an adaptive multi-objective reinforcement learning framework that dynamically adjusts priorities across different growth stages, addressing a critical gap in current RAS management approaches. The proposed system implements a hierarchical Deep Deterministic Policy Gradient architecture with growth stage-specific policies, complemented by an interpretable visualization framework that makes complex AI decisions accessible to aquaculture practitioners. Experimental validation in a commercial-scale RAS facility with tilapia demonstrated substantial improvements in both production efficiency and system usability. Key findings include an 18.7% improvement in feed conversion ratio compared to standard implementations, while maintaining exceptional water quality stability (98.3% of time within optimal ranges). Particularly noteworthy was the system’s performance during growth phase transitions—historically challenging periods in RAS management—where adjustment time was reduced by 42.5% while maintaining stable environmental conditions. User studies revealed that the interpretable visualization components significantly enhanced farmers’ trust and understanding, with 89% of participants expressing confidence in the system compared to 65% for previous approaches. These results demonstrate how adaptive reinforcement learning with appropriate interpretability tools can bridge the gap between advanced AI techniques and practical aquaculture management, potentially accelerating adoption of intelligent control systems in commercial aquaculture.

Poultry farming plays a vital role in meeting the rising global demand for animal protein. However, traditional techniques for monitoring broiler chickens have limitations. Manual broiler counting is time-consuming and prone to errors. This study explores using thermal imaging and advanced deep learning for automated broiler detection and counting. A dataset of 5000 thermal image frames of 2000 chickens was created for training, validation, and testing. Two deep learning models, YOLOv7 and YOLOv8, were compared. Thermal image features were extracted using these models to capture broiler-related thermal patterns. The study addresses the challenge of automating broiler detection and counting with high accuracy and efficiency. YOLOv8 outperformed YOLOv7, achieving significantly higher mean Average Precision (mAP) of 95% compared to 85%. Faster convergence within 20 epochs compared to 60 epochs for YOLOv7. In addition, YOLOv8 exhibited lower error rates of 2% vs. 5% for broiler counting tasks. This research demonstrates the effectiveness of YOLOv8 for real-time precision agriculture applica-tions using thermal imaging and deep learning for poultry monitoring. The findings pave the way for implementing automated broiler detection and counting systems in poultry farms, improving efficiency and data accuracy.