Smart Livestock Research Articles

Formation of electronic information management environment as a factor in the development of animal husbandry in the Volgograd region

The article substantiates the need for targeted state support of digitalization of agricultural enterprises of the Volgograd region for the development of digital infrastructure. A model of electronic information management environment of an agricultural enterprise of dairy cattle breeding as a set of applied software systems and complexes designed to process legal, regulatory and reference, commercial, accounting and analytical, planning information in accordance with the principles of forming a single interactive database of managerial decision-making in terms of information security was developed. A link between the applied software systems, smart livestock complexes and the process of managerial decision-making in the electronic information environment of agricultural enterprise management is the information of management accounting and analysis. Special importance in the electronic information environment of agricultural enterprise management is given to corporate communications, providing discussion of proposed solutions, communication control of decision implementation, organization of communication with counterparties (customers, suppliers), potential partners, creditors, investors and government agencies.

A Review on Internet of Things (IoT): Security Challenges, Issuesand the Countermeasures approaches

In the present scenario, Internet of Things is playing vital role in the next era of communication. The IOT applications like smart cities, smart houses, smart livestock, smart health care, smart climate etc.It can be lead to many security challenges and issues. The aim of this survey focus on the main objective of the security challenges and issues in the data privacy, security, confidentiality, integrity, availability, access control, encryption, default password, malware and ransom, botnet, phishing, cloud, routing and trust management and discussed recovery from mention security defies. This study has detailed review of IoT layered design, each of these layers having lot of security challenges such as threats, vulnerabilities and attacks. Understanding these challenges and associated countermeasures mechanism with the help of the secure routing.

Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming.

Remote monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 yr. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; and feed intake and greenhouse gas emissions, to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and covariances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link” to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.

A Case Study on Smart Livestock with Improved Productivity after Information and Communications Technologies Introduction

A Case Study on Smart Livestock with Improved Productivity after Information and Communications Technologies Introduction

Applying a smart livestock system as a development strategy for the animal life industry in the future: A review

Applying a smart livestock system as a development strategy for the animal life industry in the future: A review

Design of Power Efficient Routing Protocol for Smart Livestock Farm Applications

Design of Power Efficient Routing Protocol for Smart Livestock Farm Applications

스마트 축산 농가 확산을 위한 돼지 사양관리 장치의 수집정보 표준화 동향에 관한 연구

스마트 축산 농가 확산을 위한 돼지 사양관리 장치의 수집정보 표준화 동향에 관한 연구

The milk processing: Status, challenges and opportunities in ethiopia

Livestock production in Ethiopia contributes about 16.5% of the national Gross Domestic Product (GDP), 35.6% of the agricultural GDP, 15% of export earnings, and 30% of agricultural employment. The performance of livestock product marketing was poor in the last decade, despite some improvement in recent years, especially in terms of aggressive policy and strategy on export of livestock and livestock products, indicating that the sector is still underexploited. There is need to promote the market-oriented production of milk export. Knowing the common challenges and opportunities are very much important in designing smart livestock and livestock product marketing systems in the country. The common milk processing products market constraints in Ethiopia include lack of clear milk marketing system, factors of culture and religion, inaccessibility of market and lack of transport, seasonal based demand, lack of effective extension service to use newer technologies and practices, lack of market information, lack of holistic intervention and localized markets. Despite these challenges, there are some golden opportunities that increase the milk demand and to some extent increase milk consumption trend. Hence, the need to review this work was to access literatures available regarding milk processing, status, challenges and opportunities in Ethiopia.

Livestock Disease Forecasting and Smart Livestock Farm Integrated Control System based on Cloud Computing

Livestock Disease Forecasting and Smart Livestock Farm Integrated Control System based on Cloud Computing

Development of Smart Livestock Disease Control Strategies and Policy Priorities

With massive and dense production, the livestock industry is rapidly moving into a large-scale, capital-intensive industry especially in swine, poultry, and ducks. However, livestock epidemics can pose a serious threat to the livestock industry and the lives of the people. The government has established and operates the National Animal Protection and Prevention System (KAHIS) since 2013 in order to control the threat, in accordance with the five stages. The digitalized data and information are excellent in ease of management, but it is also pointed out that it is difficult to take countermeasures through linkage with the data in an emergency situation. Recently, the technology of the fourth industrial revolution such as Internet of Things (IoT), Big Data, Artificial intelligence (AI) has been rapidly implemented to the livestock industry, which makes smart livestock disease control system possible. Therefore, this study investigated the domestic and overseas cases which apply 4th Industrial Revolution technology in the industry, and derived 13 possible candidate tasks in the near future. In order to ascertain the priority of policy formulation, we surveyed the expert groups and examined the priority of each of the five stages of the prevention and the priority of each stage. The results of this study are expected to contribute to the establishment of policies for the advancement of smart livestock disease control research and livestock protection.

Digital Twin and Shadow in Smart Pork Fetteners

This paper introduces our ongoing research in the field of smart livestock farming. The base idea in this research is to take some industry based concepts, and adapt them into agriculture. In Industry we live the fourth industrial revolution, which is not a technical, but an organization revolution. So in this concept there are two key ideas, which are digital twin, and digital shadow. This article is about creating digital twin and digital shadow of a modern pig fattener. These models are based on the literature, and are identified on the data shared in these. However, these models can’t be used on real systems before identifying them on the actual circumstances. At this stage of research only digital twin of the product was created, and only some aspects of it. This however can simulate an average porkers feed consumption, weight growth and calculate the weight on certain stages of breeding. Further development is ongoing and our target is to make a fully functional model of a pig fattener.

Standardization Plan of Smart livestock Cattle Breeding Management device and Collected Information

Standardization Plan of Smart livestock Cattle Breeding Management device and Collected Information

A Reduction Method of Multi-path Setting Messages in Wireless Sensor Networks for Smart Livestock Farming

A Reduction Method of Multi-path Setting Messages in Wireless Sensor Networks for Smart Livestock Farming

Smart livestock feeding strategies for harvesting triple gain – the desired outcomes in planet, people and profit dimensions: a developing country perspective

Of the total greenhouse gas emission from the livestock sector, 45% relates to feed production and processing. Enteric methane (35%), land-use change (9%) and manure nitrous oxide and manure methane together (9.5%) are the other sources of greenhouse gas emissions, which to a large extent depend on feed types. Inefficient use of feeds reduces profitability. Increasing future feed demand and food-feed-fuel competition have environmental and social impacts. The growth for demand in livestock products comes with social, economic and environmental challenges. This paper argues that the efficient utilisation of feed resources and application of appropriate feeding strategies are vital for strengthening the three conventional pillars of sustainability (environment, social and economic). Towards this end, it identifies and explores a series of promising innovations and practices in feed production and feeding including balanced and phased feeding; increase in the quality and level of use of forages in diets; reduction in use of grains; harvesting forages when nutrient availability per unit of land is maximum; targeted mineral feeding; reduction in feed losses; use of straw-based densified feed blocks; better recycling of human food wastes and human-inedible food components to feed; new business models for production and use of urea-ammoniated straws, urea-molasses blocks, forages and silages in smallholder farms; and use of underutilised locally available feed crops linked with strengthening of seed development and distribution infrastructure. The development of simple tools and on-site assays for correcting nutritional imbalances also offers interesting opportunities. Collection of data on feed availability at the national level, and generation of sound chemical composition and nutritional value data of feeds, are a prerequisite to innovate. The focus of the discussion will be on low-input livestock systems in developing world. A large number of livestock are found in such systems and small improvements can have high global impact. In addition to the technological aspects, policy and institutional building options required to realise large impact are also discussed.