Advancing Livestock Technology: Intelligent Systemization for Enhanced Productivity, Welfare, and Sustainability

AgriEngineering, Journal Year: 2024, Volume and Issue: 6(2), P. 1479 - 1496

Published: May 28, 2024

The livestock industry is undergoing significant transformation with the integration of intelligent technologies aimed at enhancing productivity, welfare, and sustainability. This review explores latest advancements in systemization (IS), including real-time monitoring, machine learning (ML), Internet Things (IoT), their impacts on farming. aim this study to provide a comprehensive overview how these can address challenges by improving animal health, optimizing resource use, promoting sustainable practices. methods involve an extensive current literature case studies data analytics, automation feeding climate control, renewable energy integration. results indicate that IS enhances well-being through health monitoring early disease detection, optimizes efficiency, reduces operational costs automation. Furthermore, contribute environmental sustainability minimizing waste reducing ecological footprint highlights transformative potential creating more efficient, humane, industry.

Language: Английский

---

Global Sustainable Water Management: A Systematic Qualitative Review

Water Resources Management, Journal Year: 2023, Volume and Issue: 37(13), P. 5255 - 5272

Published: Sept. 7, 2023

Language: Английский

---

Optimized XGBoost Hyper-Parameter Tuned Model with Krill Herd Algorithm (KHA) for Accurate Drinking Water Quality Prediction

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(3)

Published: March 10, 2025

Language: Английский

---

Toward Design of Internet of Things and Machine Learning-Enabled Frameworks for Analysis and Prediction of Water Quality

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 101055 - 101086

Published: Jan. 1, 2023

The degradation of water quality has become a critical concern worldwide, necessitating innovative approaches for monitoring and predicting quality. This paper proposes an integrated framework that combines the Internet Things (IoT) machine learning paradigms comprehensive analysis prediction. IoT-enabled comprises four modules: sensing, coordinator, data processing, decision. IoT is equipped with temperature, pH, turbidity, Total Dissolved Solids (TDS) sensors to collect from Rohri Canal, SBA, Pakistan. acquired preprocessed then analyzed using models predict Water Quality Index (WQI) Class (WQC). With this aim, we designed learning-enabled Preprocessing steps such as cleaning, normalization Z-score technique, correlation, splitting are performed before applying models. Regression models: LSTM (Long Short-Term Memory), SVR (Support Vector Regression), MLP (Multilayer Perceptron) NARNet (Nonlinear Autoregressive Network) employed WQI, classification SVM Machine), XGBoost (eXtreme Gradient Boosting), Decision Trees, Random Forest WQC. Before that, Dataset used evaluating split into two subsets: 1 2. 600 values each parameter, while 2 includes complete set 6000 parameter. division enables comparison evaluation models' performance. results indicate regression model strong predictive performance lowest Mean Absolute Error (MAE), Squared (MSE), Root (RMSE) values, along highest R-squared (0.93), indicating accurate precise predictions. In contrast, demonstrates weaker performance, evidenced by higher errors lower (0.73). Among algorithms, achieves metrics: accuracy (0.91), precision recall (0.92), F1-score (0.91). It also conceived perform better when applied datasets smaller numbers compared larger values. Moreover, comparisons existing studies reveal study's improved consistently For classification, outperforms others, exceptional accuracy, precision, recall, metrics.

Language: Английский

---

Water Quality Monitoring and Assessment for Efficient Water Resource Management through Internet of Things and Machine Learning Approaches for Agricultural Irrigation

Water Resources Management, Journal Year: 2024, Volume and Issue: unknown

Published: June 3, 2024

Language: Английский

---

Secure Integration of IoT-Enabled Sensors and Technologies: Engineering Applications for Humanitarian Impact

Published: June 8, 2023

The Internet of Things (IoT) has revolutionized interaction with technology, enabling seamless connectivity and data exchanges, opening up new possibilities for engineering applications, addressing humanitarian challenges. However, the widespread adoption IoT technologies also raises concerns about network security privacy. study explores secure integration IoT-enabled sensors technologies, connecting applications to context. We discuss importance robust measures protect sensitive ensure reliability integrity systems. Furthermore, we highlight in contexts, including disaster management, healthcare monitoring, environmental infrastructure development. examine challenges opportunities associated deploying solutions these areas present case studies illustrating successful implementations. Additionally, ethical considerations surrounding privacy, user consent, responsible use are discussed too. By considerations, can harness full potential engineer that improve well-being quality life.

Language: Английский

---

Smart Agricultural–Industrial Crop-Monitoring System Using Unmanned Aerial Vehicle–Internet of Things Classification Techniques

Sustainability, Journal Year: 2023, Volume and Issue: 15(14), P. 11242 - 11242

Published: July 19, 2023

Unmanned aerial vehicles (UAVs) coupled with machine learning approaches have attracted considerable interest from academicians and industrialists. UAVs provide the advantage of operating monitoring actions performed in a remote area, making them useful various applications, particularly area smart farming. Even though expense controlling is key factor farming, this motivates farmers to employ while This paper proposes novel crop-monitoring system using learning-based classification UAVs. research aims monitor crop below-average cultivation climatic conditions region. First, data are pre-processed via resizing, noise removal, cleaning then segmented for image enhancement, edge normalization, smoothing. The was pre-trained convolutional neural networks (CNN) extract features. Through process, abnormalities were detected. When an abnormality input detected, these classified predict stage. Herein, fast recurrent network-based technique used classify crops. experiment conducted by providing present weather as values; namely, sensor values temperature, humidity, rain, moisture. To obtain results, around 32 truth frames taken into account. Various parameters—namely, accuracy, precision, specificity—were employed determine accuracy proposed approach. Aerial images considered data. collected detect based on pre-historic field. will differentiate between weeds

Language: Английский

---

A Comprehensive Study on Cyber Attacks in Communication Networks in Water Purification and Distribution Plants: Challenges, Vulnerabilities, and Future Prospects

Sensors, Journal Year: 2023, Volume and Issue: 23(18), P. 7999 - 7999

Published: Sept. 20, 2023

In recent years, the Internet of Things (IoT) has had a big impact on both industry and academia. Its profound is particularly felt in industrial sector, where Industrial (IIoT), also known as Industry 4.0, revolutionizing manufacturing production through fusion cutting-edge technologies network-embedded sensing devices. The IIoT revolutionizes several industries, including crucial ones such oil gas, water purification distribution, energy, chemicals, by integrating information technology (IT) with control automation systems. Water, vital resource for life, symbol advancement technology, yet knowledge potential cyberattacks their catastrophic effects treatment facilities still insufficient. Even seemingly insignificant errors can have serious consequences, aberrant pH values or fluctuations concentration hydrochloric acid (HCI) water, which result fatalities diseases. distribution been target numerous hostile cyber security attacks, some identified, revealed, documented this paper. Our goal to understand range threats that are present industry. Through lens IIoT, survey provides technical investigation covers attack models, actual cases intrusions difficulties encountered, preventative solutions. We explore upcoming perspectives, illuminating predicted advancements orientations dynamic subject. For practitioners aspiring scholars alike, our work useful, enlightening, current resource. want promote thorough grasp cybersecurity landscape combining key insights igniting group efforts toward safe dependable digital future.

Language: Английский

---

Harnessing artificial intelligence to address diseases attributable to unsafe drinking water: challenges, potentials, and recommendations

Discover Water, Journal Year: 2025, Volume and Issue: 5(1)

Published: March 13, 2025

Language: Английский

---

Introducing and evaluating SWI-FEED: A smart water IoT framework designed for large-scale contexts

Computer Communications, Journal Year: 2025, Volume and Issue: unknown, P. 108146 - 108146

Published: March 1, 2025

Language: Английский

---