Application of Information and Electronic Technology for Best Practice Management in Livestock Production System

Haldar, Avijit; Mandal, Satyendra Nath; Deb, Suman; Roy, Rakesh; Laishram, Menalsh

doi:10.1007/978-3-030-93262-6_11

Cited by 8 publications

(9 citation statements)

References 150 publications

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“…A WSs executes various tasks such as heat-sensing, soil sensing, and moisture sensing. All these tasks are executed with a sensor that is controlled through a centralized machine and all backend operation is performed by IoT devices that are connected through the internet[50,54,55].To exchange data between different IoT systems the WSs protocols have worked as the nerve center of livestock farming such as Code Division Multiple Access (CDMA), WiFi[25], LoraWan [26], and ZigBee[27] technology. ZigBee plays a key role in enabling communication over long distances when third-party services are not available like LTE, GSM, and CDMA.…”

mentioning

confidence: 99%

WITHDRAWN: Internet of Things in Livestock Farming: Implementation and Challenges

Vijay

Sharma

THAKUR

et al. 2023

Preprint

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The Internet of Things (IoT) has the potential to revolutionize the livestock farming industry by improving efficiency, reducing costs and increasing productivity. IoT plays an important role in providing innovative solutions to revolutionize the agriculture & farming sectors. Eminent researchers are constantly working to provide novel solutions and systems using IoT to address different challenges in the agriculture domain. The core application domains of IoT in agriculture are Livestock, Precision farming, and greenhouses that are further assembled into different domain monitoring applications, which assist the agriculturists and researchers to make better decisions. This paper explores the ways in which IoT can be implemented in livestock farming, including the use of sensor technology and automation. Additionally, the paper also discusses the challenges that must be overcome in order to fully realize the potential of IoT in the livestock farming industry. These challenges include the cost of implementation, lack of standardization in the industry, and the need for further research and development to address these issues. The paper concludes that while IoT has the potential to greatly benefit the livestock farming industry, overcoming these challenges.

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mentioning

confidence: 99%

WITHDRAWN: Internet of Things in Livestock Farming: Implementation and Challenges

Vijay

Sharma

THAKUR

et al. 2023

Preprint

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“…Several examples of applications of machine learning in precision agriculture [51] are reported, i.e., soil properties detection [52][53][54], crop yield predictions [55][56][57][58][59], disease [60][61][62][63] and weed detection [64][65][66], site-specific irrigation [67][68][69], and livestock production and management [70][71][72]. One of the most in-depth topics is the analysis of plant health with hyperspectral data [73].…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

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“…Any physical objects connected over the internet, such as computers, cameras, smartphones, sensors, etc., are referred to as "things." IoT have become more adaptable and well-liked in the present era of technological advancement thanks to a variety of smart gadgets like sensors, smartphones, tablets, smart wristbands, and Radio Frequency Identification (RFID) [15]. IoT are a very promising technology that offers many creative ways to modernize cattle husbandry.…”

Section: Internet Of Things (Iotmentioning

confidence: 99%

Modeling Livestock Procedures Toward Precision and Sustainable Livestock Farm in Era of Virtual Technologies: Lessons, Opportunities, Avenues of Digitization

Salem,

Mohamed

2024

Precision Liv.

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Presently, contemporary and virtual technologies that are being embraced by many businesses have a favorable influence. For instance, biometric sensors of the Internet of Things (IoT), artificial intelligence (AI), big data (BD), blockchain technology (BCT), and so on in livestock permit the real-time observation of the health and behavior of animals. Also, monitoring helps prevent deterioration, diagnose injuries, and stress, and sustain productivity. The collected data from biometric sensors is analyzed by using BD securing the processed information and making it transparent for partners using BCT. Achieving safety and security through collaborating human robots (cobots) Accordingly, these technologies turn traditional livestock into precision livestock farms (PrLFs). Currently, the concept of precision serves as the cornerstone for the advancement of sustainable and user-friendly livestock farm management in many nations as well as globally. Hence, the objective of this study is to embrace the concept of precision and illustrate its influence on livestock farms toward sustainability of livestock farm. These objectives consider catalysts for analyzing and evaluating the sustainability of livestock farms that embrace contemporary technologies in their operations and practices. Moreover, we are constructing an evaluation model for obtaining the most sustainable livestock farm through harassing Multi-Criteria Decision Making (MCDM) techniques where each technique is responsible for a certain function. For instance, CRiteria Importance Through Inter-criteria Correlation (CRITIC) is utilized for obtaining the criteria’s weights and the combined compromise solution (CoCoSo) leverages the obtained weights from CRITIC for ranking the alternatives of livestock farms and recommends the most sustainable and worst alternative. The evaluation for alternatives is performed based on rating four main criteria related to sustainability’s pillars and thirteen sub-criteria. Finally, the utilized MCDM techniques are working under the authority of Triangular Neutrosophic Sets (TrNSs) to bolster these techniques when handling incomplete information and perplexed situations.

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Application of Information and Electronic Technology for Best Practice Management in Livestock Production System

Cited by 8 publications

References 150 publications

WITHDRAWN: Internet of Things in Livestock Farming: Implementation and Challenges

WITHDRAWN: Internet of Things in Livestock Farming: Implementation and Challenges

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Modeling Livestock Procedures Toward Precision and Sustainable Livestock Farm in Era of Virtual Technologies: Lessons, Opportunities, Avenues of Digitization

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