An IoT Ecosystem-Based Architecture of a Smart Livestock Farm

Moutaouakil, Khalid El; Jdi, Hamza; Jabir, Brahim; Falih, Noureddine

doi:10.1007/978-3-031-29313-9_25

Cited by 1 publication

(1 citation statement)

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“…This layer can be further divided into several units included in Table 4. The decision-making layer uses insights gained from data analysis to make informed decisions about farm management and cattle care [20]. Examples of decisions that can be made using smart cattle monitoring systems include: identifying sick or injured cattle and providing appropriate medical care, adjusting feeding schedules and rations based on individual cattle needs, detecting signs of heat stress and taking preventive measures and optimizing grazing patterns and pasture management.…”

Section: Figure 4 Data Analysis and Decision-making Layermentioning

confidence: 99%

A design of a smart farm system for cattle monitoring

El Moutaouakil,

Falih

2023

IJEECS

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<span>The integration of technology in agriculture has led to the adoption of smart farming systems, which are becoming increasingly popular for optimizing resources, reducing labor costs, and improving efficiency. This article presents a design of a smart cattle monitoring farm system, which focuses on monitoring individual animal behavior and health, improving resource management, and optimizing overall farm efficiency. The proposed system integrates various internet of things (IoT) sensors, communication technologies, and cloud computing to provide a real-time monitoring solution <span>for cattle farms. The system uses machine learning (ML) algorithms to analyze data on cattle behavior, health, and performance, which can be accessed through web and mobile applications by farmers to proactively monitor their herd. The adoption of the system promises good results for intelligently transforming traditional cattle farming by reducing manual monitoring time and achieving a good accuracy in early detection of diseases, automated tracking of estrus cycles, and location mapping of cattle across the farm fields.</span></span>

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Section: Figure 4 Data Analysis and Decision-making Layermentioning

confidence: 99%