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Integrating livestock management with the required devices and sensors is now seen as a critical factor in the agricultural sector’s long-term success. The findings revealed that the agricultural business sector is open to implementing Information and Communication Technology (ICT) solutions, so the aim of this paper is to determine how advantageous it is for Romanian farmers to invest in a project that employs smart cattle farming methods that incorporate Artificial Intelligence (AI), Robotic Process Automation (RPA) and the Internet of Things (IOT). An unstructured interview was used to gather empirical evidence during a focus group meeting. Analyzing the selected primary performance metrics, it was projected that the farm’s profitability would increase by 19 percent, productivity would increase by 21 percent, and the farm’s environmental impact would decrease by 22 percent. Automation and remote work would help minimize the farm’s worker burden while also making control panels, decision-making files, and data analysis more available. In order for the domain to be as prosperous as possible, farmers must be made aware of the benefits of using these emerging technologies for closing the gap between farmers and Information Technology (IT) solution providers, and this can be accomplished through continuous training for both farmers and their technology vendors.
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Research on Innovative Business Plan. Smart Cattle Farming
Using Artificial Intelligent Robotic Process Automation
Diana Elena Micle 1, Florina Deiac 1, *, Alexandru Olar 1, Raul Florentin Drent
,a2, Cristian Florean 3,
,Grigore Coman 4and Felix Horat
,iu Arion 1,5
Citation: Micle, D.E.; Deiac, F.; Olar,
A.; Drent
,a, R.F.; Florean, C.; Coman,
I.G.; Arion, F.H. Research on
Innovative Business Plan. Smart
Cattle Farming Using Artificial
Intelligent Robotic Process
Automation. Agriculture 2021,11, 430.
Academic Editors: Michele Mattetti
and Luigi Alberti
Received: 13 April 2021
Accepted: 7 May 2021
Published: 10 May 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
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This article is an open access article
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Attribution (CC BY) license (https://
1Department of Economic Sciences, University of Agricultural Sciences and Veterinary Medicine of
Cluj-Napoca, 3–5 Manastur Street, 400372 Cluj-Napoca, Romania; (D.E.M.); (A.O.); (F.H.A.)
2Faculty of Engineering, North University Center of Baia Mare, Technical University of Cluj-Napoca,
400114 Cluj-Napoca, Romania;
3Centric IT, 407042 Sannicoară, Romania;
4Department of Industrial Engineering, Faculty of Machine Building, Technical University of Cluj-Napoca,
400114 Cluj-Napoca, Romania;
5Asociatia Clusterul Agro-Food-Ind Napoca (AgroTransilvania Cluster), 407039 Dezmir, Romania
Integrating livestock management with the required devices and sensors is now seen as a
critical factor in the agricultural sector’s long-term success. The findings revealed that the agricultural
business sector is open to implementing Information and Communication Technology (ICT) solutions,
so the aim of this paper is to determine how advantageous it is for Romanian farmers to invest in
a project that employs smart cattle farming methods that incorporate Artificial Intelligence (AI),
Robotic Process Automation (RPA) and the Internet of Things (IOT). An unstructured interview was
used to gather empirical evidence during a focus group meeting. Analyzing the selected primary
performance metrics, it was projected that the farm’s profitability would increase by 19 percent,
productivity would increase by 21 percent, and the farm’s environmental impact would decrease
by 22 percent. Automation and remote work would help minimize the farm’s worker burden while
also making control panels, decision-making files, and data analysis more available. In order for
the domain to be as prosperous as possible, farmers must be made aware of the benefits of using
these emerging technologies for closing the gap between farmers and Information Technology (IT)
solution providers, and this can be accomplished through continuous training for both farmers and
their technology vendors.
Keywords: agriculture 4.0; farm automatization; agricultural digitalization; AI technologies; IOT
1. Introduction
One of the basic branches of agriculture is cattle farming [
] and it occupies a priority
place in the economy of animal production [
] (p. 92). Cows are responsible for a variety of
products that also derive from this from this important branch, such as: milk, meat, skins,
manure, etc. [
] (p. 92). The economic situation of dairy farms in the European Union (EU)
is unstable and strongly differentiated from one-member countries to another [
] (p. 92).
According to Eurostat’s statistics a total of 158.2 million tons of raw milk is produced in
the EU, an increase of 1.4 million tons from year-to-year [
]. Of these, 143.1 million tons
are cow’s milk. Romania occupies the penultimate position in the EU for cow production,
which reflects the inadequate structure of dairy farms. Farmers need to join associations to
better manage agricultural inputs, increase yields and production, reduce costs, and sell
raw milk efficiently [4].
This paper brings a new perspective on how the implementation of IT technologies
and innovations in agriculture (animal husbandry) can improve the overall productivity of
Agriculture 2021,11, 430.
Agriculture 2021,11, 430 2 of 15
the industry in the field. It contributes to the existing specialized literature by proposing a
new method (methodologies/business plan) that can be easily implemented by the busi-
ness owners, with the help of a standardized procedure, customized and adapted to the
specificities of a farm/business. Cattle breeding and exploitation is a traditional activity of
the rural population and gives the character of a sustainable and promising activity [
which must include a grazing plane for the habitat conservation purposes [
]. Beef is
very important because it is a safe resource for trade, ensures labor stability, ensures do-
mestic and export needs, which bring high incomes to producers [
] (pp. 75–96). Milk is
a strategic product with a special impact on children, the elderly, and those with various
diseases [
]. Due to its complex chemical composition, high biological value and high
degree of digestibility, milk represents a product of high importance [
]. Cows produce
more milk than their offspring require, and man has taken advantage of this since the dawn
of time [
] (pp. 75–96). Breeding programs have successfully increased the amount and
quality of milk cows produce. Cow’s milk contains about 87.4% water and about 12.6%
milk solids (total solids) the latter comprising about 3.9% fat, 3.2% protein, 4.6% lactose
(anhydrous), and 0.9% ‘other solids’, i.e., minerals, vitamins, etc. [
] (pp. 75–96). Milk could
be considered a complete food product for people with nutritional needs [
]. Milk is a
crucial agricultural product for the EU, ensuring that the stability of agricultural production
still has the greatest importance as it had when the foundation of European Community
took place [
] (pp. 17–33). The average world milk consumption is estimated at 107 L per
capital, which varies in 380 L in Europe, 280 L in North America, and 50 L in East Asia [
In general terms, usually the more intensive a farm is, the greater the revenue turnover,
yet efficient farms are found in all sizes and categories, with many of the smaller farms
experiencing relatively high technical efficiency [
] (pp. 219–240). The number of farms
for milk production using any type of Automatic Milking System (AMS) is constantly
growing, especially in Eastern Europe [
] (p. 2). In recent years, the number of AMS
installed worldwide has increased dramatically and the worrying reality is the quality of
milk produced and how it compares to conventional milk [
] (p. 1). Some research shows
that the use of any kind of Conventional Milking System (CMS) is more cost-effective
than the AMS [
] (p. 2). The biggest advantages of the AMS are the quality of the milk,
the health and welfare of the animals, the large amount of milk, the reduction of the labor
force and a better social life of the dairy producers [
] (pp. 3–14). For commercial farms,
automatic milking saves time compared to manual milking [16].
Since the 1970s, there has been discussions of food security, a relatively recent concept.
Food security, a component of economic security, requires food producers to have access
to the agricultural resources available for this purpose, with costs that allow them to be
produced sustainably so that they can be delivered to consumers at affordable prices. [
In the food industry, especially in the dairy industry, the concept of hazard analysis and
critical control points (HACCP) guarantees the quality of food due to their entire chain of
supervision, “from farm to fork” [18] (pp. 173–177).
Given that external threats lead to mass contamination, digitalization in the agri-food
industry is becoming a necessity [
]. These digital technologies such as AI, large volumes
of data, and robotics can help farmers and companies to provide safe food, streamline pro-
cesses, and create new products and services. The benefits of using these digital technolo-
gies are known to farmers and may include animal performance, optimizing production
factors, reducing labor, agricultural data flows, improving conditions for farmers and
reducing the negative impact of agriculture on the environment [18].
Smart Farming (SF) refers to the application of ICT in agriculture. The use of these
technologies for farming activities stand for innovation and examples of innovative tech-
nologies are sensors, robots, weather satellites, and so on [
]. These technologies rely
on data and they are dependent of the internet. By collecting and analyzing these data,
the efficiency of production processes increases, so that all actors involved, from farmers,
companies, and institutions, are motivated to work together to use innovative technolo-
gies [
]. SF can be defined as a facilitator of efficient, productive, and profitable agricultural
Agriculture 2021,11, 430 3 of 15
enterprises [
] (pp. 179–187). The development of data applications is a social product of
the interactions between people, institutions, and technology itself. Basically, Big Data (BD)
applications are intrinsically sociotechnical [
]. Currently, farmers considering adopting
smart agriculture are faced with the absence of legal and regulatory frameworks around
the collecting, sharing and use of agricultural data [
]. Farmers and firms are creating,
adapting, and adopting a range of new hardware, software, mobile apps, sensor technolo-
gies, and BD applications, all involved in disrupting the established structures within the
farm machinery and associated data sectors [24].
Similar results were observed during research carried on by the authors to observe the
links and disparities among the agriculture and ICT sectors, with a study case in Romania,
carried on in in frame of TRACK Project (COS-CLUSTPARTN-2017-3-02: “Tracking oppor-
tunities to develop and strengthen data collection and big data in agri-chain to increase
competitivity of SMEs-TRACK”. Project was co-funded by the European Union’s COSME
Programme). The main findings revealed that agricultural business sector is open for adopt-
ing ICT solutions, and they need solutions dedicated to their specificities of the farm or
company. IT managers are facing complications in adopting appropriate ICT solutions for
their business. A slow transition towards agri-food companies is observed. ICT companies
must bridge the gap between innovation and farmers. There are some unique experiences
that began on both sides: supply and demand, and this is the starting point of this article,
from where the idea of this study came from. Consequently, the goal of the paper is to
develop creative solutions to adapt the cow farm software model based on innovative field
technology, which enables farm productivity to be increased while labor and time spent on
the farm to be reduced.
The research question from which the substantiation of this article was based is as
follows: “Is it opportune for Romania farmers to invest in a project that uses smart cattle
farming methods integrating Artificial Intelligence (AI), Robotic Process Automation (RPA)
and Internet of Things (IOT)?”
For finding the answer to this question, a number of secondary key questions were set
up for our article:
How can innovative technologies on the market be integrated into the operational
activity of farms and how will the farmer benefit from them?
Will the farm profitability be increased, and will there be an increase in production?
Will these technologies help the environment?
The research results will lead to the adoption of disruptive technologies in animals,
RPA, Industrial Internet of Things (IIOT), in order to give efficiency, sustainability, and com-
petitiveness to enterprises, as well as to increase confidence in disruptive technologies in
Industry 4.0.
The first objective is to integrate the Livestock Management module with the required
devices and sensors, cloud computing, RPA, and BD, while paving the road to full au-
tomation and changing the gates, scales, windows, climate systems, watering systems,
feeding systems, and adding milking robots. The Livestock module is all about the well-
being of the cattle, its movement, alimentation, medical treatment, sleeping and milking
periods, and so on. This first objective specifically will generate many use cases.
2. Materials and Methods
2.1. Business Models—Innovative Business Model
Pisano [
] mentioned in 2015 that in the global framework that seems to affect
companies as well as human conduct, new socioeconomic and technical patterns are emerg-
ing. High uncertainty caused by rapid technological innovation and global competition
pushes organizations, in order to gain competitive advantages and grow their market share,
to develop and use various business models [26] (pp. 7–17).
To understand which characteristics of a trend must be integrated in a new business is
not an easy challenge, but it is strategically vital if entrepreneurs are to maintain inspiration
to run new businesses [25].
Agriculture 2021,11, 430 4 of 15
The increasingly intense and volatile environment in which modern businesses must
operate necessitates the use of creative solutions in various areas of business activity [
Because of today’s tough economic conditions for business groups, implementing technolo-
gies only in product and process areas may be inadequate for achieving the enterprise’s set
goals [
]. Dynamic and tumultuous economic environments encourage industries to seek
innovation in production, processes, and organizational areas of business operations. As a
result, the need to make changes may affect the company [
] (pp. 352–364), whereas the
need for change may lead to a new Business Model (BM) [29] (pp. 1023–1030).
2.2. Focus Group
Empirical data was collected in December 2020 at a one-day focus-group meeting.
The focus group is a qualitative data collection method, similar to the interview, that is
intended to use the group dynamics [
] (pp. 491–497). They are unstructured interviews
that allow the participants and the researchers to create knowledge together rather than to
focus on a singular solution to address the research question [
] (pp. 400–417). The group
environment encourages the participants to engage in discussions and express ideas they
had not thought of on an individual basis [
] (pp. 400–417). In recent years focus groups
have become a popular research method in this field [32] (pp. 400–417).
The meeting consisted in two parts. Firstly, a group of 10 farmers was consulted in
order to identify the most important problems they face and to discuss their solutions. In the
second part the business model and the conclusions of the first session were presented to a
group of 12 stakeholders and experts. The aim of the second session was to validate the
model and the results, and also to identify additional solutions, opportunities, limitations,
and barriers regarding the implementation of the model. Thus, the participants were
selected based on their expertise in different key aspects [
] of the proposed model,
as follows:
Cluster (1; Agriculture) and farmers associations (2)—Farmers needs and market
opportunities for the product;
Clusters (3; IT, Environment and Green energy)—the model contribution to a low
carbon and climate resilient economy in agriculture and food sectors;
Business consultant (2)—business model and economic sustainability;
IT providers (2) and Universities (2)—technical solutions;
Due to the SARS-CoV-2 virus pandemic constraints, the research was implemented
online, through a video conference. This approach limits the interactions of the participants,
but is less time consuming and expensive, and it is easier to record. The meeting was
recorded and transcribed. This allowed for a greater familiarity with the data and also
greater transparency [34].
3. Results
The solutions identified are new so far in the literature and are addressed mainly to
companies in economically and technologically developed countries. The novelty brought
by our article through the proposed solution consists in facilitating the diffusion of IoT
at the level of developing countries, for small and medium enterprises (SMEs) in the
agricultural field. The proposed new BM involves changing existing management systems
with new ones, based on information technologies that ensure increased productivity of
companies but also better management of environmental issues, while minimizing the
negative impact of economic activities and maximizing biodiversity conservation.
3.1. Essential Conditions for the Success of the Proposed Business Model
While big sectors will likely invest with sophisticated facilities, data-free intuition
management will no longer reflect the way professional farms work. In the long term,
small farms will constantly adopt fundamental technologies. This should, particularly for
the new generation of digital farmers, also be seen as a source of opportunities [34].
Agriculture 2021,11, 430 5 of 15
SMEs are progressively implementing simple technologies, while major farms are
likely to invest in sophisticated equipment, so data-free intuition-based management
for future business farms will no longer be the norm. The new generation of digitally
knowledgeable young farmers should see this as a source of opportunities [34].
The proposed cattle farm robotic solution comes at a time when technological ad-
vancements have reached a favorable level of maturity and the implementation of such a
mechanism has diminished costs [
]. There have been many good AI and IOT proof of
concepts, most of them reaching the production environments. The available technologies
are either open source or can be used as services [
], including a larger extent of provider
independence; or they are available as standardized technologies from multiple vendors.
A competitive market in terms of service quality, low prices, and accurate cost esti-
mates is assured by the large number of AI and IOT providers.
Collaborations are also a very important factor, for example, within clusters, technol-
ogy adoption is simpler and quicker. In terms of maintaining economic sustainability and
empowering entrepreneurs, disruptive innovations can be a solution. The implementation
of technology for one of a cluster’s members is sped up by proving it for the others as well,
ensuring the never-ending demand of business competitiveness and empowerment.
Organic first actors are bound to have a more accelerated development if they choose
to integrate Industry 4.0 technologies in their routine [
]. This model of farm should stand
as a foundation of the digitalization mission; thus Agri-Food RPA has a serious growth
potential in terms of market share.
IT and data gathering have achieved an unprecedented degree of confidence [
while the afferent markets are in constant growth, in order to withhold the increased
demands for state of the art/disruptive information technologies which are responsible
as a result of smother and more accessible Agri-Food RPA services while keeping the
costs competitive.
3.2. Business Model
3.2.1. How the Organization Creates and Captures Value
Centric IT, the organization responsible for developing the software, creates and
captures revenue by a developed and currently operated Software as a Service (SAAS)
portal which integrates complete business management modules. The goal is to further
develop the capabilities with AI and IOT technology, transforming the expert software
system to an intelligent software system. Farm hardware will be operated by cloud
software robots, AI generated valuably will learn and evolve, learning how the human
mind learns and interfering with prediction on the basis of our interpretation of the data
that is being trained.
3.2.2. The Target Audience
Cattle farmers have been the main focus in the beginning, afterwards extending the
services towards vegetable milk and meat processing businesses.
3.2.3. The Key Differences
The services are reaching the end user through a cloud portal which makes it more
accessible and less costly to use the technologies. The modules function as an integrated en-
vironment, with data shared between (e.g., account and stock management work together).
At the heart of the project are cognitive technology, cloud computing, and remote work.
The company offers a complete package for cloud computing services SAAS that
include management, development, and business operations.
3.2.4. Its Primary Capital and Partnerships
Providing accessibility, availability, and data security are key factors, and there are
established marketing, consulting, production, and support teams. A consortium of
companies has been created, directly or indirectly interested in the field of agri-food, as an
Agriculture 2021,11, 430 6 of 15
addition to our internal team. There is a well-established collaboration with farmers’
groups that were the direct architects and beneficiaries of the first software module created
by the company.
3.2.5. Its Distribution Networks
Given the fact that the platform is cloud based, the delivery channel mostly relies on
the internet, through tools such as social media and online marketing, the participation in
international events and fairs, symposiums, and conferences using the available Digital
Innovation Hubs (DIH) communication channels is a must for the successful propagation
of the project and a reliable BM.
3.2.6. The Method by Which It Generates Revenue
The company’s clients benefit from the software’s capabilities through a monthly or
annual subscription. The price is calculated by the number of modules, the scale, and the
number of users of each module used in the channel of the customer. This solution allows
the beneficiary to upgrade or downgrade the package according to its needs. The AI tech-
nologies will be taxed as part of the monthly usage fee, while the IOT will be invoiced as
an extra fee.
3.2.7. Expenditure Framework
The business model strategy is based on value and in the beginning the current
customers would be the first to utilize this technology. It is foreseen that the initial number
of customers will rise, in time, due to involvement in fairs, both domestically and globally,
as well as the demonstration activities and showcases that will be hosted across Clusters
and DIH.
According to financial forecasts for the next three years, the margin will show a
30 percent growth due to the emerging technology that the business is incorporating into
its goods (modules), but the company’s turnover will also increase by 200 percent, fueled by
the number of agri-food consumers who will pick up enterprise’s inventions alongside
its partners.
3.3. Description of Applied Technology
The AI RPA with IOT for cattle farms in its core is driven by a business process
automation designed to reduce the burden of repeated work. This technology will be
incorporated with a variety of components, such as ICT, computers, hardware, and other
robots, through the IIOT. RPA is sometimes correlated and referred to as software robotics,
and figurative robots (bots) or AI “tour” the facility in order to achieve what this technology
does best.
The IIOT’s flexibility and adaptability makes it suitable for manufacturing, energy stor-
age, integration of sensors, tools and technology relating to electronic industrial applica-
tions. This connectivity makes data gathering, distribution, and analysis simpler. The effi-
ciency and ease of use benefits as an economic advantage. The IIOT consists of developing
a distributed control system (DCS), which makes it possible to optimize and improve
process controls by using cloud computing.
The main concept of this project is to have an AI information store, cognitive apps,
linked inside a livestock farm to each sensor, computer, or robot. Communications must be
allowed from sensors to BD to cognitive applications to computers and robots, for example
(Figure 1).
A lot of hardware (e.g., picture and audio processing, air monitoring) has been in-
tegrated into and with the SAAS-RPA system from the 3rd partner, the Faculty’s Re-
search Centre.
Agriculture 2021,11, 430 7 of 15
3.3.1. Description of the Innovative Process Proposed by the Business Model
The breakthrough is that RPA is responsible for both market and manufacturing
processes within agri-food industries beginning with cattle farms in applications. The tech-
nology can help with animal protection, hygiene, and medical treatments such as antibiotics
or other unsafe types of medical treatments. The system can help prevent the milk from a
medicated cow to be mixed with the rest of the untreated cows. This type of system can
help raise the quality of products, prevent contamination, and increase food security in a
manner that man will never be able to without continuous surveillance.
Figure 1. Industrial Internet of Thing connection diagram (realized by authors).
The framework uses SAAS, RPA, IIOT, Hardware, and Software Integration Tools to
integrate/interoperate and will be sold as cloud-based services (Figure 2).
In virtual assisting and information dissemination environments the RPA systems
have achieved good results, but with this research, RPA and IIOT can be experimented in
the production processes.
3.3.2. Interoperability
As mentioned earlier, the technology also uses the internet and has a component in-
frastructure such that connectivity and interoperability with other applications, equipment,
and robotics is central to it.
3.3.3. Replicability
Replication can be conveniently conducted as a SAAS gateway at the heart of the
project. For faster international implementation and expansion, best practices and configu-
ration data can be easily replicated. Integration hardware is still commonly used at the EU
level by EU suppliers and cloud technology.
3.3.4. Reuse
The software center of the project has the design “Farm Software Ecosystem”. In a
typical environment, a knowledge base may often be used to enhance the efficiency of the
cognitive program at a reduced cost.
Agriculture 2021,11, 430 8 of 15
Figure 2.
High level diagram of software architecture (reproduced from [
], with permission from
Elsevier, 2021)
3.4. Sustainability Impact
The business model communicates the declared aim to achieve harmonious and
sustainable growth for the agrarian sector through the goals and actions mentioned. Sus-
tainability is a must and can be assured by:
Good practice in the operations carried out and changes made in the sector purchased
along the way will be a good benefit and will continue through the coming years;
The multiplier and multi-function impact would contribute not only to benefits for
consortiums on the labor market but for future generations who are best positioned
to benefit from the advantages gained, savings made, and knowledge gained.
The establishment of reciprocal ties between members of the consortium, between the
consortium and third companies that would enable access not only for the labor
market, but also, indirectly, for the sharing of good experience. This will allow the
acquisition of the requisite practical skills for possible business developments.
The consortium is structured to ensure that its findings are not a final objective but
rather a starting point for prospective efforts which would generate more advantages:
To provide participants with cross-cutting business expertise and to assist them in
creating new income.
The establishment of an advisory committee and its daily consultation would lead to a
continuous database that will allow the participants and other stakeholders to access
information (from the academic environment, socio-economic, or administrative-
legislative environment).
Diagnostic and influence analysis are elements in which the key strategic directions
for adaptation rely, but also the creation of new ones, and for the establishment
(fundamental and/or applied) directives for research in the consortium, at cluster
level, will be decided.
In order to strengthen creativity, collaboration with the corporate community, infras-
tructures, facilities, and technical expertise acquired during this time will be further used
in university teaching at a proper level (Figure 3). The outcomes of smart cattle farming
using AI and RPA’s efforts can be institutionalized.
The findings are incorporated into the agri-food policy and plan at the state, regional,
and national level. As previously discussed, the fact that market climate developments are
a positive win and will continue in the next few years ensures longevity.
Agriculture 2021,11, 430 9 of 15
Figure 3. Components class diagram (reproduced from [38], with permission from Elsevier, 2021).
3.5. Focus Group Estimations
As an outcome of the implementation of the proposed solution, focus group estimated
a number of Key Performance Indicators (KPIs) that could be evaluated during and after
implementation. Therefore, 15 indicators were proposed and considered by the partici-
pants. Five indicators were supported by 83% (10 out of 12), and thus were selected as
KPIs. To substantiate the decision, the KPI levels were estimated based on the average
results of data collection and results analysis and comparisons after digitalization of farm
activities/flows. The data has been gathered by the digitalization solutions usage and early
adoption by 7 cattle farms with about 200 cattle and about 350 hectares of crops cultivated
for animal food production, which represents a regular family farm in Romania.
Reduction of human working hours at the farm, as an early estimation: in-office work
by 82%, total hours by 14%. Of the whole farmer’s activities more than 20% represents
the office work (e.g., data fill-ins, reports generation, reports analysis, data-based
decision making) and this can be optimized by 82% percent based on company’s
statistics. All participants supported this indicator. The biggest supporters were the
Universities and the Agricultural Cluster. This answers one of the key issues raised
by the 10 farmers in the first round of the focus group.
Reduction of variable/hard-to-predict costs at the farm, as an early estimation: by at
least 32%, as result of digitalization for better management of resources, sensors mon-
itoring and data archiving, pattern recognition of the cognitive technologies offers
great premises in terms of costs reduction and costs estimation selected by 10 out of
12 participants. The providers of IT solutions, Cluster it, and business consultants
were the promoters of the measure.
Increase the profitability of the farm, as an early estimation: 19%, by better resources
management, better estimations, better data-based planning support, better yields out
of disruptive industry 4.0 technologies adoption. It was proposed by all participants.
It was also the main benefit identified by farmers.
Agriculture 2021,11, 430 10 of 15
Reduce the environmental impact, as an early estimation: 22%, by better estimation
together with better data-based decision-making generated smaller environmental
footprints. Supported by 11 out of 12, especially by Environmental Clusters and
Universities. They believe that: “Given the challenges we currently face; an innovative
business model is bound to include an environmental dimension”. In another quote
one of the participants stated that “This dimension aligns the vision of the model with
the EU environmental principles found and supported in the Common Agricultural
Policy.” However, the Farmers’ Association was slightly retained, because in their
opinion it can be “a limiting factor”.
Increase in production, as an early estimation: 21%, for instance, in the cattle farm
businesses the weather has a great impact on daily milk production for dairy farms but
also in terms of daily weight gains for meat farms, so sensor based/weather forecast
based decision-making digital systems offer a great support for production increase.
The idea was supported by everyone, especially by farmers and business consultants.
According to a study done by Nesta in 2015, precision agriculture and investing in
technological advancements in a farm can increase income possibilities by almost 20% [
This increase in income can be achieved by cutting down costs with water or fertilizers,
for example. Nesta mentions that “new agricultural technologies can drastically reduce the
costs of fertilizers or increase yields by even 5% [39]”.
Sales are projected to increase slightly in the first year as a result of the teams’ efforts to
market the product, according to estimates. A small rise in turnover is anticipated in the first
year as a result of efforts to promote the product in particular. The number of new customers
is expected to rise significantly during the second and third year of implementation, as a
result of the promotion carried out in the first year (Figure 4). The margin growth is due
to the increase in revenue, and the cross-sales tactics. Customers get to know the brand
and the company goods better and order new linked modules or other offerings on the
same portal.
Figure 4. Financial projection over 3 years (authors’ estimation).
4. Discussion
This paper aims to develop modern agriculture platforms and to increase the produc-
tivity of farms by rapidly integrating emerging digital innovations and innovative business
models into agriculture.
Business model innovation (BMI) is one of the critical tasks that must be carried out
on a continuous basis in every enterprise in order to maintain competitiveness in today’s
digital economy. Although interest in BM has grown substantially since the late 1990s,
there are few known factors in BMI procedures in SMEs, which represent the vast majority
of companies on the European market [40] (p. 344).
BMI is described as BMs’ activity-based view leading to modifications at the BM
of a company which are novel for the world or simply novel to the companies being
Agriculture 2021,11, 430 11 of 15
analyzed [
] (pp. 216–226). The result of BMI is to reconfigure the existing BM activities
of an enterprise new to the competing market [
] (pp. 183–198). It is a process which
changes the core elements of a company and its business logic deliberately [43].
The digital and technological advancements realized through Industry 4.0 transforma-
tion are enhancing the quality of the product, working safety, forecasting faults and energy
savings [
]. It is expected that in the next 5 years the production will be more efficient and
trustworthy with up to 20% due to the Industry 4.0 approach [45,46].
Many manufacturers are still involved in accelerating the adoption and integration of
safe, reliable AI [
]. AI-based manufacturing in particular has the potential to enhance
KPIs of the industry processes.
Gargiulo et al. [
] discussed in 2020 about the low adoption rate of AMS regardless of
the increased possibility of productivity and profitability rates. Although his study results
contest the research hypothesis, he concluded that CMS had similar results with the AMS
in terms of human resource costs, income level and profitability. The initial investment and
maintenance costs entail greater cost of AMS [
]. Bijl et al. discovered that the over-cost
for AMS in the Netherlands were significantly lower [
] (pp. 239–248). New Zealand’s
expected results regarding AMS were much lower than the results of CMS, all this due to
its fast depreciation rate, the high costs of maintenance and extensive reparation costs [
(pp. 239–248). In Ireland, the medium-sized CMS farms, with an intermediate level of
automation, showed that the revenues for AMS are much lower [51] (pp. 7700–7713).
There are also studies that have shown that automated systems can be more profitable
than the conventional systems. In the United States it has been proven that AMS was most
profitable for herd sizes between 120 and 240 cattle [52] (pp. 7739–7749).
The AMS technology is growing in importance in this field, and the estimates of the
focus group participants take into account this trend. They argue that the proposed business
model does not refer only to milking, but to the entire production flow. From animal welfare
to the safety and quality of the product obtained. Thus, in addition to increased profitability,
it also reduces the occurrence of certain risks and costs present in conventional systems.
In Romania, the future of agriculture is Emerging Media, and efforts to neglect it reign
in the growth of this field. Farmers around the world use the IOT, robotics, AI, and compre-
hensive data to dramatically enhance the farming processes. For agro-industrial nations,
scientific advancement in the new field study is seen as a solution. Agricultural technology
focused on IT goods, automation, and robotics that are commonly used can catalyze the rise
in efficiency and profitability of agricultural operations. Digital technology, according to
research in the field, is a practical way to address existing challenges [53].
The digitization of agriculture increases the working conditions of farmers, decreases the
detrimental environmental effects of agriculture, and also makes agricultural holdings
much more profitable.
In the near future, digital farming becomes the conventional one, and manufactur-
ers realize that they are able to obtain productive returns by investing in technologies,
including saving seedlings, fertilizers, herbicides, pesticides and fuels [53].
The word “digital economy” is not described in any particular way. The economy is
seen as a system for producing, exporting, trading, and exchanging products or services.
A new economic framework may be called this as evolving technologies are used to develop,
deliver, exchange, and consume processes. The major role of the global economy is on the
consumer: the digital market is a place where goods and services are traded through an IT
and software network. At the same time, the digital medium for assessing the operation
patterns of market players is software, the hardware, and some operating regimes [54].
The project will help reduce the environmental footprint, using the most advanced
technologies, the goal is to protect the environment and moderate the energy waste. The pol-
lution should be close to non-existent in the first year of use. A specialized procedure
for recycling, reuse, and transformation will be adopted, starting from the recycling of
ordinary waste, streamlining processes, to the use of industrial composts which in addition
to these are ecological and efficient method of recovery of organic waste of all kinds, as it
Agriculture 2021,11, 430 12 of 15
is beneficial financially. Reducing emissions of air pollutants from many major sources,
such as transport, industry, and electricity generation, will have a positive impact on the
environment. Reducing air pollution helps combat climate change [55].
At the moment, both agricultural farms and ICT companies prosper for collaboration,
but the profit is not distributed equally among the participants. The ICT are the ones that
profit the most. Income from partnership will be shared by agri-food firms. Proposals in-
clude giving people access to BD and eliminating the need for labor. A core component
of the new paradigm may be an online, interconnected value chain. There is plenty of
space for additional measures in the agricultural sector’s method of adjusting supply and
technical demand.
This business model, like others, is not immune to possible difficulties or challenges
in the implementation process. The main complications that could disrupt the attempt to
put into motion are:
1. There is a vast number of suppliers and lot of proprietary technologies;
Another issue can be represented by inadequate data sharing between ICT elements;
3. The drawbacks of software and hardware licenses;
4. Data access, data ownership and access control are another possible drawback;
5. The right to intellectual property;
6. Low acceptance and high consumption costs are in demand;
Short deployment times and a rapid learning curve or adoption of technologicalsolutions;
Last but not least, the demand from the end consumer to slowly but gradually uptake
the technology.
5. Conclusions
The most important benefits of the AMS are the increased milk consistency, ani-
mal health and welfare, the large amount of milk produced, and the reduction of labor
needed. Farming dairy cows is a heavy physical labor that causes, in the long term, a series
of conditions in the most experienced practicians of the job issues that can be solved with
automation and remote work. Farmers need to be made aware of the advantages of using
these emerging innovations. Issues related to the health of the animal can be found much
faster with the help of RPA, resulting in a better medical assistance.
It is worth noting that, regardless of political changes, Romania’s industrial transfor-
mation in the agri-food sector is important and should be embraced and implemented as a
strategic priority in a multi-year strategy. Furthermore, digitalization in agriculture and the
food industry should be seen as part of a larger digitalization phase. Romanian Govern-
ment observed this necessity, so it specifically assumes, for the 2021–2027 strategy, that one
of the major needs for achievement the Specific Objective 2 of the strategy (Strengthen mar-
ket orientation and increase competitiveness), so-called Need 05. Increasing investment
in precision agriculture and digitization is important, as they are essential tools for the
sustainability of the agricultural sector and for increasing competitiveness [56].
Also, it observed that a close collaboration with transversal stakeholders is neces-
sary. Correlating this need with the promising results of the research carried on by a
heterogenous team, Romanian Government should focus on financing such consortium
by allocating funds explicitly for agri-food sector needs in the selected European DIH in
Digital Europe Program (E-DIH) from Romania to concentrate on the sector in each of the
country’s development regions, as part of the Digital Europe Program’s phase. This is a
supplementary step for establishing a central digital hub (platform) with the mission of
integrating, organizing, and managing a collection of digital infrastructures (platforms,
tools) at the regional and national levels, as well as a technology transfer center at the
national level, representing and promoting technology transfer policies. Both are proposed
in the National Strategic Plan (PNS) 2021–2027, which will manage the creation of common
spaces between technology transfer centers and clusters (specific to the agri-food field).
Agriculture 2021,11, 430 13 of 15
Author Contributions:
Conceptualization, D.E.M., C.F. and F.H.A.; methodology, A.O. and R.F.D.;
software, C.F.; validation, C.F. and F.H.A.; formalanalysis, F.D., A.O. and I.G.C.; investigation, D.E.M.,
C.F. and F.H.A.; resources, F.H.A. and C.F.; data curation, F.D., A.O. and I.G.C.; writing—original draft
preparation, D.E.M.; writing—review and editing, F.H.A.; visualization, C.F. and R.F.D.; supervision,
F.H.A.; project administration, C.F.; funding acquisition, F.H.A. and C.F. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
A.F.H. acknowledge the support by AgroTransilvania Cluster, through the
project TRACK Project (COS-CLUSTPARTN-2017-3-02: “Tracking opportunities to develop and
strengthen data collection and big data in agri-chain to increase competitivity of SMEs-TRACK”.
Project was co-funded by the European Union’s COSME Programme) and through the project “Agro-
Transilvania Cluster—Cluster specialized in bioeconomy” (Competitiveness Operational Program
Axis 1. Program co-financed by the European Regional Development Fund “Investing for your
future”, Section B—Competition code POC-A1-A1.1.1-B-2015—Project type—Innovation clusters.
P_35_476, co-funded by European Regional Development Fund).
Conflicts of Interest: The authors declare no conflict of interest.
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The economic situation of EU dairy farms is unstable and differs strongly between member countries. Most studies addressing the EU’s agricultural sector focus on selected countries or selected groups of operators. Conversely, this paper seeks to determine the economic situation of dairy farms in EU countries grouped into types according to their production potential. The analysis relied on data collected and processed in the FADN (Farm Accountancy Data Network). The farms were classified based on a hierarchical clustering routine. The agglomerative technique was used. Distances between clusters were calculated using the Ward’s method. In turn, the distance between countries was calculated as the Euclidean distance. This study enabled the identification of five different types of dairy farms in the EU. On an EU-wide basis, medium and large-sized highly specialized intensive farms play a key role in milk production. Despite their profitability being somehow restricted, they report high levels of labor productivity thanks to an advantageous production potential. In turn, an insufficient potential is among the main restrictions faced by other dairy farms in the EU; their advantageous financial indicators often fail to provide satisfactory levels of income which could drive investment and consumption opportunities.
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The information that crops offer is turned into profitable decisions only when efficiently managed. Current advances in data management are making Smart Farming grow exponentially as data have become the key element in modern agriculture to help producers with critical decision-making. Valuable advantages appear with objective information acquired through sensors with the aim of maximizing productivity and sustainability. This kind of data-based managed farms rely on data that can increase efficiency by avoiding the misuse of resources and the pollution of the environment. Data-driven agriculture, with the help of robotic solutions incorporating artificial intelligent techniques, sets the grounds for the sustainable agriculture of the future. This paper reviews the current status of advanced farm management systems by revisiting each crucial step, from data acquisition in crop fields to variable rate applications, so that growers can make optimized decisions to save money while protecting the environment and transforming how food will be produced to sustainably match the forthcoming population growth.
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The impulse towards a larger introduction of Information and Communication Technology (ICT) in the agricultural field is currently experiencing its momentum, as digitisation has large potentialities to provide benefits for both producers and consumers; on the other hand, pushing technological solutions into a rural context encounters several challenges. In this work, we provide a survey of the most recent research activities, in the form of both research projects and scientific literature, with the objective of showing the already achieved results, the current investigations, and the still open challenges, both technical and non technical. We mainly focus on the EU territory, identifying threats and concerns, and then looking at existing and upcoming solutions to overcome those barriers.
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The absence of legal and regulatory frameworks around the collection, sharing and use of agricultural data contributes to the range of challenges currently being faced by farmers considering adoption of smart farming technologies. Many laws potentially influence the ownership, control of and access to data, in this paper we examine the attitudes of farmers to the collection, control, sharing and use of their farm data. Australian agriculture and the attitudes of Australian farmers to the adoption and uptake of smart farming technologies is used to highlight the tensions., however the issues and challenges raised are common to many agricultural industries throughout the world. We combine insights from a survey of Australian farmers with a legal analysis of the way in which agricultural data are collected, controlled, shared and used. We argue that the lack of transparency and clarity around issues such as data ownership, portability, privacy, trust and liability in the commercial relationships governing smart farming are contributing to farmers' reluctance to engage in the widespread sharing of their farm data that smart farming facilitates. At the heart of the concerns is the lack of trust between the farmers as data contributors, and those third parties who collect, aggregate and share their data. The aim of this paper is to examine the issues giving rise to this lack of trust. We conclude with recommendations on how to address these concerns and facilitate the improved adoption of smart farming technologies, focusing on the need for the social architecture of the agricultural data relationships to change. To achieve this change, open dialogue, education and awareness raising and good data governance are essential to help build trust in the adoption of smart farming systems.
Driven by information technology, big data provides new development opportunities for city construction. People use multiple scientific advancements such as the Internet of Things (IoT) for data acquisition and Artificial Intelligence (AI) for big data analytics to enhance the integration and sharing of data and optimize the basic standards of smart cities. Past few years, the concept behind the Internet of Things has been a major research topic in the development of smart cities, education, industry, and commerce. Services and applications of IoT are the major factors for creating a sustainable urban life that is employed by smart cities. The stakeholders of smart cities become more aware, efficient, and interactive using Information and Communication Technology (ICT) in IoT. The applications of smart cities based on IoT have been increased in number which leads to production and increase in the amount of data and its processing. Moreover, the city stakeholders and governments take prior actions/precautions for processing the collected data from the IoT devices and predicting the future consequences for securing a sustainable environment. Artificial Intelligence is one of the key research techniques which several researchers have analysed and proved to be the best in improving the performance of detecting fire hazard in smart cities. In this research, a Deep Belief Network (DBN) with Recurrent LSTM Neural Network (R-LSTM-NN) is proposed for prediction of big data that are collected from smart cities based on IoT. Moreover, the proposed model mainly concentrates in predicting the fire hazard values that gathered from smart cities using IoT devices. The simulation results show that the proposed technique proves to be better when compared with other existing techniques in terms of accuracy, precision, recall, and F-1 score. The proposed model detects the fire outbreak with a 98.4% of accuracy that having 0.14% of minimal error rate. Furthermore, the proposed model can be used for various prediction problems that are faced by smart cities.