This is a keynote presentation presented at a conference on INNOVATIVE TECHNOLOGIES AND DATA APPLICATIONS IN THE AGRIFOOD SECTOR, 26 February 2019 at Boğaziçi Üniversitesi South Campus, Rectorate Conference Hall, Turkey. It describes multi-disciplinary, collaborative, agile approach for digital transformation of the agri-food sector based on the IoF2020 and SmartAgriHubs project. It describes several examples of IoT and Big Data applications from those projects,
The video and voice-over of this presentation can be found at https://youtu.be/wYJVqh6jvSE
Digital Agriculture can be defined as ICT and data ecosystems to support the development and delivery of timely, targeted (localized) information and services to make farming profitable and sustainable (socially, economically and environmentally) while delivering safe, nutritious and affordable food for ALL. Rural connectivity will be a key to providing low cost data and access to information. Digital technology will be key to increasing agriculture productivity by delivering tailored recommendations to farmers based on crop, planting date, variety sown; real time localized observed weather and projected market prices. Mobile phones also enable farmers to integrate into structured markets based on approved grades and standards. The greatest impact of Digital agriculture will have is on democratization of market pricing and compressing transaction costs. Digital agriculture will also leverage social media platforms to build human capacity. One of the best examples originating from India is Digital Green.
Agriculture 4.0- The future of farming technology Dishant James
The World Government Summit recently came out with an agenda to improve agricultural technologies by integrating farming with industry 4.0. The outcome would be a fourth agricultural revolution or Agriculture 4.0
Food 4.0: Data Driven Agri-Food SystemsDeepak Pareek
Presentation delivered as Expert Speaker on "Food 4.0: Technology to make food and agriculture sector SAFE for Consumers EFFICIENT for Stake Holders and PROFITABLE for Growers" at CTO Forum focused on the "Impact of Artificial Intelligence" hosted by Tamil Nadu Technology Development & Promotion Centre at CII South Zone Headquarters on 16th November 2018.
Digital Agriculture can be defined as ICT and data ecosystems to support the development and delivery of timely, targeted (localized) information and services to make farming profitable and sustainable (socially, economically and environmentally) while delivering safe, nutritious and affordable food for ALL. Rural connectivity will be a key to providing low cost data and access to information. Digital technology will be key to increasing agriculture productivity by delivering tailored recommendations to farmers based on crop, planting date, variety sown; real time localized observed weather and projected market prices. Mobile phones also enable farmers to integrate into structured markets based on approved grades and standards. The greatest impact of Digital agriculture will have is on democratization of market pricing and compressing transaction costs. Digital agriculture will also leverage social media platforms to build human capacity. One of the best examples originating from India is Digital Green.
Agriculture 4.0- The future of farming technology Dishant James
The World Government Summit recently came out with an agenda to improve agricultural technologies by integrating farming with industry 4.0. The outcome would be a fourth agricultural revolution or Agriculture 4.0
Food 4.0: Data Driven Agri-Food SystemsDeepak Pareek
Presentation delivered as Expert Speaker on "Food 4.0: Technology to make food and agriculture sector SAFE for Consumers EFFICIENT for Stake Holders and PROFITABLE for Growers" at CTO Forum focused on the "Impact of Artificial Intelligence" hosted by Tamil Nadu Technology Development & Promotion Centre at CII South Zone Headquarters on 16th November 2018.
Artificial Intelligence is an approach to make a computer, a robot, or a product to think about how smart humans think. AI is a study of how the human brain thinks, learns, decides and work when it tries to solve problems. And finally, this study outputs intelligent software systems. The aim of AI is to improve computer functions that are related to human knowledge, for example, reasoning, learning, and problem-solving.
Farmers, growers, and agricultural companies are increasingly adopting digital technologies to transform a traditional industry. In the past, farmers and growers made decisions based on their personal experience, combined with interpreting local conditions.
But digital technologies, from the internet of things to blockchain, are rapidly turning the industry into a high-tech sector. Smart, connected devices can now provide the insight to enable farms to improve every aspect of their operations.
- What is the digital agriculture revolution?
- How digital technologies are transforming the industry – including the impact of the internet of things and blockchain.
- What are the unique challenges that the sector faces in adopting digital technology?
- The future of agriculture
Reshaping the Future of Agriculture through ICT: Agriculture 4.0Rizwan MFM
M.F.M. Rizwan | Assistant Director of Agriculture (Development)
National Agriculture Information & Communication Centre (NAICC) | Department of Agriculture
Thinking about the distant future allows us to go out of the box and to create room for social creativity and empathy. The technology survey, the social developments, the archetypal scenarios and the visions of the future in this study aim to boost the debate on the Dutch agro & food sector, especially in the domains where technological developments may have an impact. Taken together, these instruments form an important inspiration for further study, policy studies, innovation and a public debate.
A session on "Digitalization of Agriculture" at Entrepreneurship Conclave organized by Shailesh J. Mehta School of Management, Indian Institute of Technology Bombay.
Internet of Things ( IOT) in AgricultureAmey Khebade
Application of IOT in Agriculture
Monitoring soil moisture and temperature
Controlled irrigation
Efficient usage of input like water, fertilizers, pesticides, etc
Reduced cost of production
Connected greenhouses and stables
Livestock monitoring
Download PPT for better design and animation
Ict as an important tool in rural development in context to Agriculture, e - ...Nischay Patel
Here is the various ICT tools that are important in rural development in various sector namely., Agriculture, dairy sector, e- governance, extension and veterinary sector
Agri Tech Startups: Redefining Indian AgricultureShailesh Herale
This presentation highlights the concept of startup and current scenario of agritech startups, government support/incubators/ accelerators related to agritech startup, bottlenecks for agritech startups in India and case studies highlighting innovative agritech solutions.
By applying IoT to agriculture it is easy to observe and interact with physical world. Synergizing Internet of Things and Cloud Computing can help the farmers to share useful information regarding cultivation on social networks, and also helps in ensuring global food and farming security
Large ICT-projects in Agri-Food in EuropeSjaak Wolfert
This is a presentation about the background, development and state-of-the-art of large ICT-projects in Agri-Food that are going on in Europe: Internet of Food and Farm 2020 (IoF2020) and SmartAgriHubs.
Artificial Intelligence is an approach to make a computer, a robot, or a product to think about how smart humans think. AI is a study of how the human brain thinks, learns, decides and work when it tries to solve problems. And finally, this study outputs intelligent software systems. The aim of AI is to improve computer functions that are related to human knowledge, for example, reasoning, learning, and problem-solving.
Farmers, growers, and agricultural companies are increasingly adopting digital technologies to transform a traditional industry. In the past, farmers and growers made decisions based on their personal experience, combined with interpreting local conditions.
But digital technologies, from the internet of things to blockchain, are rapidly turning the industry into a high-tech sector. Smart, connected devices can now provide the insight to enable farms to improve every aspect of their operations.
- What is the digital agriculture revolution?
- How digital technologies are transforming the industry – including the impact of the internet of things and blockchain.
- What are the unique challenges that the sector faces in adopting digital technology?
- The future of agriculture
Reshaping the Future of Agriculture through ICT: Agriculture 4.0Rizwan MFM
M.F.M. Rizwan | Assistant Director of Agriculture (Development)
National Agriculture Information & Communication Centre (NAICC) | Department of Agriculture
Thinking about the distant future allows us to go out of the box and to create room for social creativity and empathy. The technology survey, the social developments, the archetypal scenarios and the visions of the future in this study aim to boost the debate on the Dutch agro & food sector, especially in the domains where technological developments may have an impact. Taken together, these instruments form an important inspiration for further study, policy studies, innovation and a public debate.
A session on "Digitalization of Agriculture" at Entrepreneurship Conclave organized by Shailesh J. Mehta School of Management, Indian Institute of Technology Bombay.
Internet of Things ( IOT) in AgricultureAmey Khebade
Application of IOT in Agriculture
Monitoring soil moisture and temperature
Controlled irrigation
Efficient usage of input like water, fertilizers, pesticides, etc
Reduced cost of production
Connected greenhouses and stables
Livestock monitoring
Download PPT for better design and animation
Ict as an important tool in rural development in context to Agriculture, e - ...Nischay Patel
Here is the various ICT tools that are important in rural development in various sector namely., Agriculture, dairy sector, e- governance, extension and veterinary sector
Agri Tech Startups: Redefining Indian AgricultureShailesh Herale
This presentation highlights the concept of startup and current scenario of agritech startups, government support/incubators/ accelerators related to agritech startup, bottlenecks for agritech startups in India and case studies highlighting innovative agritech solutions.
By applying IoT to agriculture it is easy to observe and interact with physical world. Synergizing Internet of Things and Cloud Computing can help the farmers to share useful information regarding cultivation on social networks, and also helps in ensuring global food and farming security
Large ICT-projects in Agri-Food in EuropeSjaak Wolfert
This is a presentation about the background, development and state-of-the-art of large ICT-projects in Agri-Food that are going on in Europe: Internet of Food and Farm 2020 (IoF2020) and SmartAgriHubs.
APPLICATION OF BIG DATA IN ENHANCING EFFECTIVE DECISION MAKING IN AGRICULTURA...Sjaak Wolfert
The agriculture production system increasingly becomes data-driven and data-enabled based on the cyber-physical management cycle. This paper describes several IoT-applications of the EU-funded IoF2020 project in which data and data-sharing plays a crucial role. It provides an integrative framework aiming at cross-fertilisation, co-creation and co-ownership of results. Technical integration, business support and ecosystem development are key mechanisms to realize this.
Digital Innovation Hubs – Digital Transformation of Agriculture at a Regional...Sjaak Wolfert
• Build local digital innovation hubs offering innovation services and access
to finance
• Organize regional challenges for initiating new Innovation Experiments
• Conduct multi-actor Innovation Experiments for a digital transformation
• Creating a pan-European network of Digital Innovation Hubs
and Competence Centres
Bridging the skills gap IoT Tech Expo Berlin 1 Jun 2017Sjaak Wolfert
Bridging the skills gap: How industrial/academic partnerships can benefit your IoT business.
Several of the large IoT players have recently partnered with universities and colleges worldwide to ensure that the next generation of recruits have the high level technical skills and understanding of the IoT ecosystem need to add value to their businesses. How should you take advantage of such collaboration opportunities, and how can they translate into increasing innovation and getting that competitive edge within your organisation? The EU-funded IoT Large Scale Pilot 'The Internet of Food and Farm 2020 (IoF2020)' will be used as a showcase. New opportunities for collaboration will be mentioned.
Project Description - Collection, sustainable cultivation, value addition and marketing linkages of selected medicinal and aromatic species (MAP) using emerging technology interventions.
IoF2020 project overview for BDE/eRosa/GODANSjaak Wolfert
Presentation of the IoF2020 project at the 2nd Joint workshop of Big Data Europe, eRosa & GODAN on European Policy Perspectives on Data-intensive Agriculture & Food.
Digital innovation for sustainable food systemsSjaak Wolfert
This presentation will show that digital solutions help addressing multiple sustainability issues, particularly illuminating how producers and consumers can use digitalisation to support a transition towards healthier diets.
ICRISAT Global Planning Meeting 2019:Research Program - Innovation Systems fo...ICRISAT
The Global Planning Meeting 2019 focused on an innovation systems approach harnesses the conditions needed to create demand for technologies and creates the knowledge that may be used to bring about such changes…innovations most often emerge from a systems of actors collaborating, communicating and learning, methodologies and tools to create innovations, understand entry points/tradeoffs and leverage actors towards profitable resilient and sustainable agri-food systems at scale and work together to contribute to ICRISAT’s mission.
This presentation was held in a panel discussion on 'Digital transformation in agri-food sector through policymaking and innovation' at the Digital Agriculture Forum Webinar, jointly organized by FAO and Zhejiang University.
It describes how the EU project SmartAgriHubs is working on connecting the dots to mitigate the current fragemented development of smart, digital solutions for agriculture. The key for this approach is creating a network of Digital Innovation Hubs closely connected to a network of Competence Centres. The DIHs are creating new Innovation Experiments at a local, regional level, supported by the network. The Innovation Portal can facilitate matchmaking to do so. The approach can be further extended to other continents and regions such as Africa.
AI bots in the agriculture field can harvest crops at a higher volume and faster pace than human laborers. By leveraging computer vision helps to monitor the weed and spray them. Thus, Artificial Intelligence is helping farmers find more efficient ways to protect their crops from weeds.
Agro IR 4.0-smart and next generation agro-farming-Fab labs to make anythingAbulHasnatSolaiman
Agriculture 4.0 is a term for the next big trends facing the industry, including a greater focus on precision agriculture, the internet of things (IoT) and the use of big data to drive greater business efficiencies in the face of rising populations and climate change. Makerspaces or Fab labs around the world can contribute in big margin to make prototypes reducing cost and makerspaces will be actions towards IR 4.0 in Bangladesh
PROBLEM:
Smart farming is a new concept in the field of agriculture with its complex mechanisms, fresh-coined terms, usage statistics and analytics, and its implementations differ from country to country. There is a shortage of structured information on this, especially, analytical research on comparison the countries’ past and current performance and future-expected gains on the field.
OBJECTIVES:
This paper’s mission is to familiarize the students with the mechanisms, terms, statistics, analytical research data and to do the comparison of the different scenarios of Smart Farming’s implementation in Germany and Uzbekistan.
APPROACHES:
Introducing interconnected technology fields that smart farming strongly related to:
- Farm Management Information Systems
- Precision Agriculture
- Agricultural automation and robotics
Comparing the current and future expected state of the SMART FARMING technology in Uzbekistan and Germany.
The Internet of Things for Food - An integrated socio-economic and technologi...Sjaak Wolfert
The domain of agri-food is increasingly being digitized through the introduction of all kind of smart devices and software: the Internet of Things (IoT). I distinguish 4 application areas in which IoT and the digital transformation is expected to bring big changes and where data will play an increasingly larger role:
1. Digital data is becoming more important for decision-making for actors at any level of the agri-food supply chain: from farmers, through logistic providers to consumers.
2. The same data is essential for food integrity, providing assurance to consumers and other stakeholders about safety, authenticity and quality of food.
3. Public decision-making for societal challenges such as food security, climate change, healthy food and nutrition could also tap into these data instead of using separate censuses and statistics which are usually lagging behind.
4. Finally, this digitization is driven by fast developments in science and technology (S&T), such as Artificial Intelligence, Internet of Things, Blockchain, etc. At the same time, advancements in data science also heavily rely on the data that is being generated by the application of data-driven research; simply put: no big data analytics without big data.
Now it could be expected that this is purely a technological development. However, I will show how the organisational development is equally important, coining an integrated, multi-disciplinary approach. The heart of this approach is formed by use cases in which digital solutions are designed, tested & implemented and evaluated in a real-life environment, following a cyclic, iterative development path. It is supported by (i) data science and information management, (ii) business modelling, governance & ethics and (iii) ecosystem development. The approach will be demonstrated by an existing example from the wine sector.
The outline of this presentation consists of three parts. First, I will describe the trends and developments concerning the digital transformation of the agri-food sector. This will conclude with the definition of the innovation challenge for digital innovation in this sector. Then I will introduce an integrated innovation approach to address this challenge. Finally, I will use a real-life example from pig production to illustrate how this approach works in practice. Finally, I will end up with some conclusions.
I showed how the digital transformation of the agri-food sector is taking place and that there is a clear potential for sustainable food systems. Through the digital transformation a lot of data is produced which can be used for multiple purposes. You have learned that digital innovation is not only about technology, but that technical and organizational issues should be addressed, at the same time. Therefore, digital innovation should take place in a real-life context by use case projects, following a multi-disciplinary, collaborative, agile approach.
If you are interested in more details about the Pig Farm Management, contact Jarissa Maselyne from ILVO. I encourage you to join the SmartAgriHubs community by registering in the Innovation Portal and you can also come to the final event in Lisbon within a few weeks. Hundreds of stakeholders and more than 20 partner projects are coming together to share experiences on Digital Innovation in the Agri-Food sector. You are also welcome to subscribe to our on-line course that is enabled by Wageningen Academy. And finally, if you want to know more about how we deal with data in Wageningen, contact the Wageningen Data Competence Center.
This is a presentation I held in the workshop in which we discussed and offered how the SmartAgriHubs network can support proposals for the current Horizon Europe call 'HORIZON-CL6-2022-FARM2FORK-02-04-two-stage: Smart solutions for the use of digital technologies for small- and medium-sized, farms and farm structures'.
IoT and 5G in Agriculture: opportunities and challengesSjaak Wolfert
This is a keynote presentation at a workshop that was organized by Marconi Labs Coltano on 'Wireless technologies in agriculture. The presentation describes the general trend of digitalization in agriculture and food production. This is further illustrated by the IoF202 use case 'Big Wine Optimization' that demonstrates use of the Internet of Things (IoT) in agri-food. From this use case an overall, integrated approach to work on digital innovation is deducted which emphasizes both technological as well as organizational aspects. It also briefly introduces the potential use of 5G illustrated by a use case of the weed detection robot using a pre-5G network in The Netherlands. Recommendations to leapfrog development are provided for three distinctive scenarios ranging from 'no smart farming yet applied' to '5G implementation available'. The presentation ends with conclusions, that are also provided in Italian.
AI for intelligent services in Food SystemsSjaak Wolfert
This presentation was presented at the IEEE 5G Worldforum in a session 'Dialogues between 5G/B5G and Vertical Domains: AI for Intelligent Services. Several use cases in Food Systems that use 5G are presented of which the 'weed detection robot' in more detail. Enabling factors and recommendations for the use of 5G to create intelligent services using AI are discussed.
Navigating the twilight zone - pathways towards digital transformation of foo...Sjaak Wolfert
The Twilight Zone as a metaphor refers to the situation where innovations have proven to be promising but have to be up-scaled to a higher level of adoption. Therefore we have to bridge the difference between technology readiness and user readiness. “We don’t need more apps. We need a system”. This short quote of a farmer reflects the high degree of fragmentation and discontinuity in innovation processes we observe.
More focus on the user readiness of technologies and support by multi-actor ecosystems is required, in which innovations are embedded in a system approach and tested in living labs. These are important pathways towards a sustainable digital innovation ecosystem and to navigate through the Twilight Zone.
During the 2021 Mansholt Lecture, l presented the options and challenges for stakeholders in the transition towards a sustainable digital innovation ecosystem. Europe needs to consider creating an integral European policy on this issue.
oversea
This is the presentation on understanding the SmartAgriHubs project that I gave at the kick-off event in Prague, Czech Republic on the 5th of March 2019. It starts with the background of the Digital Transformation that is going on in the Agri-Food sector. Then the objective is decribed followed by the 5 basic concepts that are the basis of this project: Digital Innovation Hubs, Innovation Expriments, Competence Centers, Innovation Portal and the Innovation Services Maturity Model. Next, the project approach and work package structure are explained. The presentation is concluded by the most important KPIs and numbers of the project.
The SmartAgriHubs project enables a broad digital transformation of the European farming and food sector. With a €20 million budget co-funded by the European Union, the project aims to build an extensive pan-European network of Digital Innovation Hubs (DIHs). The project starts today on November 1st, 2018. This presentation describes the project's objective and method that is used to reach these objectives.
New technologies such as the Internet of Things and Cloud Computing are expected to leverage the current
trend of Smart Farming, introducing more sensors, robots and artificial intelligence, encompassed by the
phenomenon of Big Data.
This presentation will give a quick insight into the state-of-the-art of Big Data applications in Smart Farming
and identify the related challenges that have to be addressed. It shows that the scope of Big Data
applications in Smart Farming goes beyond the farm; it is influencing the entire food supply chain. Big data
are being used to provide predictive insights in farming operations, drive real-time operational decisions, and
redesign business processes for game-changing business models.
It is expected that Big Data will cause major shifts in roles and power relations among different players in
current food supply chain networks. The landscape of stakeholders exhibits an interesting game between
powerful tech companies, venture capitalists and often small startups and new entrants. At the same time
there are several public institutions that publish open data, under the condition that the privacy of persons
must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1)
closed, proprietary systems or 2) open, collaborative systems.
The development of data and application infrastructures (platforms and standards) and their institutional
embedment will play a crucial role in the battle between these scenarios. A major challenge is therefore to
cope with governance issues and define suitable business models for data sharing in different supply chain
scenarios.
New technologies such as the Internet of Things and Cloud Computing are expected to leverage the current
trend of Smart Farming, introducing more sensors, robots and artificial intelligence, encompassed by the
phenomenon of Big Data.
This presentation will give a quick insight into the state-of-the-art of Big Data applications in Smart Farming
and identify the related challenges that have to be addressed. It shows that the scope of Big Data
applications in Smart Farming goes beyond the farm; it is influencing the entire food supply chain. Big data
are being used to provide predictive insights in farming operations, drive real-time operational decisions, and
redesign business processes for game-changing business models.
It is expected that Big Data will cause major shifts in roles and power relations among different players in
current food supply chain networks. The landscape of stakeholders exhibits an interesting game between
powerful tech companies, venture capitalists and often small startups and new entrants. At the same time
there are several public institutions that publish open data, under the condition that the privacy of persons
must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1)
closed, proprietary systems or 2) open, collaborative systems.
The development of data and application infrastructures (platforms and standards) and their institutional
embedment will play a crucial role in the battle between these scenarios. A major challenge is therefore to
cope with governance issues and define suitable business models for data sharing in different supply chain
scenarios.
Guidelines for governance of data sharing in agri foodSjaak Wolfert
Big Data is becoming a new asset in the agri-food sector including enterprise data from operational systems, sensor data, farm equipment data, etc. Recently, Big Data applications are being implemented to improve farm and chain performance in agri-food networks. Still, many companies are refraining from sharing data because of fear of governance issues such as data insecurity, or lack of privacy or liability, among others. To overcome such barriers for developments with Big Data, this paper aims at: 1) analysing governance issues in agri-food networks, and 2) introducing a set of guidelines for data-sharing. Based on a literature review, a framework for analysing agri-food networks was developed, with internal governance factors (efficiency, effectiveness, inclusiveness, legitimacy & accountability, credibility and transparency) and external governance factors (political, economic, social, technological, legal and environmental factors). The framework contributes to development of a set of draft guidelines. Accordingly, for each factor, the guidelines address issues, best practices and lessons learned from other projects and initiatives. The approach developed in this paper creates a baseline for possible future developments of Big data in terms of 1) upscaling of the guidelines at a global level, 2) refining and fine-tuning of the guidelines for context specific agri-food networks, and 3) contributing to solving governance challenges in data sharing. In the future, the relevance of Big Data in the agri-food domain is expected to increase, and so are the contributions of this approach.
Fostering Business and Software Ecosystems for large-scale Uptake of IoT in F...Sjaak Wolfert
The Internet of Things (IoT) is expected to be a real game changer that will drastically improve productivity and sustainability in food and farming. However, current IoT applications in this domain are still fragmentary and mainly used by a small group of early adopters. The Internet of Food and Farm 2020 Large-Scale Pilot (IoF2020) addresses the organizational and technological challenges to overcome this situation by fostering a large-scale uptake of IoT in the European food and farming domain. The heart of the project is formed by a balanced set of multi-actor trials that reflect the diversity of the food and farming domain. Each trial is composed of well-delineated use cases developing IoT solutions for the most relevant challenges of the concerned subsector. The project conducts 5 trials with a total of 19 use cases in arable, dairy, fruits, vegetables and meat production. IoF2020 embraces a lean multi-actor approach that combines the development of Minimal Viable Products (MVPs) in short iterations with the active involvement of various stakeholders. The architectural approach supports interoperability of multiple use case systems and reuse of IoT components across them. Use cases are also supported in developing business and solving governance issues. The IoF2020 ecosystem and collaboration space is established to boost the uptake of IoT in Food and Farming and pave the way for new innovations.
Keynote IoT in Agriculture opening academic year CIHEAM ZaragozaSjaak Wolfert
Keynote presentation for the opening of the academic year at CIHEAM institute for Mediterranean agricultural research in Zaragoza. It is about how IoT and Big Data are transforming Agriculture in Europe and what the main challenges are: governance, business models and open infrastructures. This is illustrated from several use cases in the Internet of Food and Farm 2020 (IoF2020) project.
Entrepreneurs active in the agricultural sector spend more and more of their time registering and publishing all kinds of data, as the government, certification bodies, banks, clients, the retail sector and consumers all want to have more insight into how safe and sustainable their food is.
The majority of this agriculture-related data is still paper-based, spread over different systems and difficult to exchange between the people who want to access it. This is why digitising agricultural business data is an important item on the agenda. With FarmDigital, we can respond to these developments.
FarmDigital is an action research programme which is currently working towards a situation in which data only needs to be entered once and can be shared easily. It aims to achieve this goal by standardising data and developing and implementing an independent, digital platform for people to use.
Smart Farming is a development that emphasizes the use of information and communication technology in the
cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing
are expected to leverage this development and introduce more robots and artificial intelligence in farming.
This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be
captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of
Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following
a structured approach, a conceptual framework for analysiswas developed that can also be used for future
studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond
primary production; it is influencing the entire food supply chain. Big data are being used to provide predictive
insights in farming operations, drive real-time operational decisions, and redesign business processes for
game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in
roles and power relationsamong different players in current food supply chain networks. The landscape of stakeholders
exhibits an interesting gamebetween powerful tech companies, venture capitalists and often small startups
and new entrants. At the same time there are several public institutions that publish open data, under the
condition that the privacy of persons must be guaranteed. The future of Smart Farming may unravel in a continuum
of two extreme scenarios: 1) closed, proprietary systems in which the farmer is part of a highly integrated
food supply chain or 2) open, collaborative systems inwhich the farmer and every other stakeholder in the chain
network is flexible in choosing business partners as well for the technology as for the food production side. The
further development of data and application infrastructures (platforms and standards) and their institutional
embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective,
the authors propose to give research priority to organizational issues concerning governance issues and suitable
business models for data sharing in different supply chain scenarios.
IoF2020 project overview for S3 platform Big Data and TraceabilitySjaak Wolfert
Presentation at a technical meeting of the S3 AgriFood platform on Big Data and Traceability hosted by the regional government of Andalusia. Special attention was paid to those use cases that are dealing with this theme.
DATA-FAIR - value creation by data sharing in agri-food businessSjaak Wolfert
The digitization of society makes data more important. This is enhanced by the Internet of Things (IoT) in which a variety of devices, sensors and the like are connected via the Internet together and deliver real-time data.
Many companies see data as the way to generate new business, also in the agri-food sector. However, the added value is only created when multiple data sources are combined, aggregated and analyzed. The sharing of data between companies in the chain is therefore a critical success factor.
Although several initiatives to share data have been initiated, this development seems to be stagnating. Now the agribusinesses are mainly developing their own proprietary platforms, resulting in a maze of platforms which makes it technically difficult to exchange data between different systems and devices. This causes rather more administrative burdens than they reduce.
Exchange of data between platforms and applications is currently inhibited because of discussions about ownership of data, privacy, fear of concentration of power in the chain and the lack of clear business models where added value of data sharing is distributed among the various stakeholders. Now it seems that the benefit to the primary producers - the farmers - is minimal and the agri-business seems to benefit most. This creates resistance to sharing data that inhibits innovations that ultimately serve the interests of the farmer and the competitiveness of the whole agri-food sector.
DATA-FAIR breaks this deadlock and will accelerate innovation by hosting several large-scale trials with companies and Wageningen Research in which applications are developed where data between various platforms is shared and value is created.
In these trials, the farmer plays a central role as a main supplier and manager of data. He or she determines who may use which data and under what conditions. Digital permissions will play an important role enabled by a central register (e.g. AgriTrust). Also, special attention is paid to the development of attractive and transparent business models and good organizational embedding (governance) so that the results will continue to exist after the project.
DATA-FAIR doesn’t create a new platform itself, but uses existing building blocks and will help improving these if necessary. Here one can think of open application interfaces (APIs) and standards to link platforms and databases. DATA-FAIR builds on experiences with existing data hubs such as EDI-Circle and AgriPlace.
Governance of Data Sharing in Agri-Food - towards common guidelinesSjaak Wolfert
Big Data is becoming a new asset in the agri-food sector including enterprise data from operational systems, sensor data, farm equipment data, etc. Recently, Big Data applications are being implemented, aiming at improving farm and chain performance. Many companies are refraining from sharing data because of the fear of governance issues such as data security, privacy and liability. Moreover, they are often in a deadlock or afraid to take the first step even though they expect to develop new business with data. To accelerate the development of Big Data applications, this paper analyses governance issues and introduces a set of guidelines for governance of data sharing in agri-food networks. A framework for analysis was derived from literature and used to identify lessons learned from recent projects or initiatives. From these results, a set of draft guidelines was developed. The framework and guidelines were evaluated in a workshop. The framework consists of factors that are related to governance on data sharing in networks. Internal factors are: efficiency, effectiveness, inclusiveness, legitimacy & accountability, credibility and transparency. External factors are: political, economic, social, technological, legal and environmental factors. For each of these factors, guidelines are provided in terms of: issues to be addressed, best practices and lessons learned from other projects and initiatives. It is concluded that the framework is complete in covering all relevant issues on governance in data sharing but the guidelines must be considered as a first set, which can be further improved and extended in the future. A wiki-type-of-website could help to upscale the guidelines at a global level. The guidelines could also be further refined accounting for different maturity levels of agri-food networks. The guidelines in this paper are considered to be a valuable step into the direction of solving governance issues in data sharing, which is expected to accelerate Big Data applications in the agri-food domain.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
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Knowledge engineering: from people to machines and back
IoT and Big Data in Agri-Food Business
1. IoT and Big Data Applications in Agri-Food
Dr. Sjaak Wolfert
Strategic Senior Scientist at Wageningen University & Research
Boğaziçi University, Istanbul, Turkey, 26 Feb. 2019
3. To explore the potential of nature
to improve the quality of life
Wageningen University & Research
Academic research & education, and applied research
5,800 employees (5,100 fte)
>10,000 students (>125 countries)
> 30 locations in NL, also global satellites
Turnover about € 650 million
#1 Agricultural Sciences
14x
4. 4
Interview with
Johan Bouma in
Resource 4 Oct. 2018
p. 18-19
1. Multidisciplinarity
2. Collaborative process
3. Agile development
5. ECOSYSTEM & COLLABORATION SPACE
ProjectCoordination&
Management
Take-home message: multidisciplinary, collaborative,
agile approach for Digital Transformation
Trials/Use Cases: Knowledge & App development
Lean multi-actor approach
3. EVALUATION
1. CO-DESIGN
2. IMPLEMENTATION
P1
P2
LARGE
SCALE
P3
Data Science &
Information management
Business Modelling,
Governance & Ethics
Ecosystem Development
9. The Battlefield of Data for Farming and Food
Farming
Data
Food
Data
See: Wolfert et al., Agricultural Systems 153 (2017) 69–80
Processors
Ag
Business Tech
Companies
Tech
Start-up
Tech
Start-up
Ag Tech
Retail
Venture
Capitalists
Data
Start-up
Data
Start-up
11. Governance
● Trust, data privacy, security...
Business models
● fair share, new opportunities
Infrastructure
● open versus closed
Ecosystems
● establishing critical mass
...which are often intertwined!
Current issues and challenges
12. European Public-Private Partnership ICT-projects
2011-2013: SmartAgriFood - a FIWARE-based conceptual architecture
and prototype applications (5 M€)
2013-2015: FIspace – B2B business collaboration platform for agri-food
& logistics (+ apps) (13.5 M€)
2014-2016: Accelerators: SmartAgriFood2, FInish, FRACTALS (~17 M€)
- 125 apps/start-ups based on FIWARE/FIspace
Sep. 2016: FIWARE Foundation established with 3 verticals:
Smart Cities, Industry and Agri-Food
2017-2020: IoF2020 – The Internet of Food and Farm (30 M€) - IoT
large-scale pilot for smart farming and food security
2018-2022: SmartAgriHubs – Connecting the dots to unleash the
innovation potential for digital transformation of the
European Agri-Food sector (20 M€)
13.
14. Objective:
Large-scale uptake of IoT in the European
farming and food sector
• Business case of IoT
• Integrate and reuse available IoT
technologies
• User acceptability of IoT
• Sustainability of IoT solutions
14
Internet of Food and Farm 2020
Innovation Action: 2017 - 2020
30 M€ funding by DG-CNCT/AGRI
15.
16. THE INTERNET OF ARABLE FARMING
1.1 Within-field Management Zoning (potato)
1.2 Precision Crop Management (wheat)
1.3 Soya Protein Management (soya)
1.4 Farm Machine Interoperability
- Data-Driven Potato Production
- Solar-powered Field Sensors
- Within-field management zoning (Baltics)
- Traceability for Food and Feed
- Potato Data processing exchange
17. THE INTERNET OF DAIRY FARMING
17
2.1 Grazing Cow Monitor
2.2 Happy Cow
2.3 Silent Herdsman
2.4 Remote Milk Quality
- Precision Mineral supplementation
- Lameness detection through machine learning
18. 3.1 Fresh Table Grapes Chain
3.2 Big Wine Optimization
3.3 Automated Olive Chain
3.4 Intelligent Fruit Logistics
THE INTERNET OF FRUIT
18
- Smart Orchard spray application
- Beverage integrity tracking
19. 4.1 City Farming for Leafy Vegetables
4.2 Chain-integrated Greenhouse Production
4.3 Added Value Weeding Data
4.4 Enhanced Quality Certification System
THE INTERNET OF VEGETABLES
19
- Digital Ecosystem Utilisation
20. 5.1 Pig Farm Management
5.2 Poultry Chain Management
5.3 Meat Transparency and Traceability
THE INTERNET OF MEAT
20
• Feed Supply Chain management
• Interoperable Pig health tracking
• Decision-making optimisation
Beef supply chain
21. Soil map based variable rate applications and machine automation in potato production
UC1.1. WITHIN-FIELD
MANAGEMENT ZONING
Coordinators: Peter Paree (ZLTO) & Corné Kempenaar (WUR)
22. SOIL MAP SERVICE
VARIABLE RATE
APPLICATION MAP
AUTOMATION & MACHINE
COMMUNICATION
Product Impressions
23. Major Challenge Here is what we aim to improve (KPIs)
Yield by better
plant distribution
Variable planting distance map –
Validation in 2017 and 2018. Nov. 2018
portal where maps can be ordered.
Variable rate herbicide use map -
Validation in 2016 and 2017. May 2018
portal where maps can be ordered.
Quality by better
plant distribution
Reduction
pesticide use
Core Product Features
Variable Rate
Application Map Service
Customer & Provider
Uses soil maps and agronomic knowledge to create
crop management task map based on variability in
soil characteristics like organic matter and/or clay
content, water storage capacity, tramlines, shade,
etc..
Smart application of resources: seeds,
pesticides, fertilizers +4%
+5%
-23%
Better distribution of plants leads to +5% kilos and +5% better
quality (more potatoes in desired size). Taking soil characteristics
for weed growth into account: -23% less herbicide and +2% more
yield.
Enriching canopy index with soil characteristics lead to -10% less
additional N fertilizer (2nd phase).
These values derive from comparison of a standard farm’s performance
prior to the installation of our system and after.
Reduction
fertilizer use
-10%
Product Factsheet
Existing variable rate maps are often based on tweaking
expert judgement and lack a certain level of precision in
tasking / lack of validation.
Farmers and advisors
Price per unit, added value
LoonwerkGPS,
soil analysis labs,
FMIS providers VRA additional N spraying
June 2018 on Growth + Soil Maps.
High spatio-temporal monitoring dashboard
24. IoT tools for sustainable wine production, wine quality management and shipping monitoring
UC3.2. BIG WINE
OPTIMIZATION
Coordinators: Mario Diaz Nava, ST Microelectronics
26. IoT Product Impressions
sensors in
the vineyard
display devices,
agronomic parameters
and weather forecast
Temperature/RH
logger
with data
transmission
NIR spectrometer
% alc., sugar,
etc.
28. Keep your herd healthy with an artificial intelligence monitoring system
UC2.2.
HAPPY COW
Coordinators: Niels Molenaar, Connecterra
29. Estrus insights Health insights Value chain integration
Product Impressions
How IDA looks like in practice
30. Translating dairy cow behaviour to management
information that helps a farmer to improve farm
efficiency and animal health.
Major Challenge
Here is what we aim to improve (KPIs)
Calving interval
305 day milk
production
Number of
days treated
with antibiotics
Customer & Provider
Ida a ‘farmers assistant’ based
on artificial intelligence; it helps
the farmer to keep the herd
healthy.
Dairy farmers
-3%
+1%
-0.5%7,5
€/cow/month
Detect oestrus with 80% accuracy
Detection of health problems.
Predict the start of calving
Rank cows based on their feed efficiency
Track management problems based on herd
behaviour.
Insights on dairy herds for partners like
veterinarians and dairy processors with.
Core Product Features
Tracks cow behaviour and learns from the
observed patterns to advise the farmer.
Product Factsheet
IDA: the Intelligent Dairy farmers Assistant
31. IOF2020 ECOSYSTEM & COLLABORATION SPACE
WP1ProjectCoordination&
Management
GENERIC APPROACH & STRUCTURE
WP2 Trials/Use cases: Knowledge & App development
Lean multi-actor approach
3. EVALUATION
1. CO-DESIGN
2. IMPLEMENTATION
P1
P2
LARGE
SCALE
P3
WP3 IoT Integration WP4 Business Support
WP5 Ecosystem Development
32. TECHNICAL / ARCHITECTURAL APPROACH
Use case
architecture
Use case
IoT system
developed
Use case IoT
system
implemented
Use case IoT
system
deployed
USE CASE REQUIREMENTS
IoT reference
architecture
instance of
IoT catalogue
Reusable IoT
components
reuse
IoT Lab
Reference
configurations
& instances
reuse
Collaboration
Space
shared
services
& data
ProjectlevelUsecaselevel
sustain
reuse
34. Business support
Different business
models will be
tested to identify
the most successful
and sustaining ones
BUSINESS MODELS
Buying and selling a
product is te best
service.
MARKET
STUDY
Develop standard
procedures and
guidelines to handle
sensitive
information and to
protect IP
PRIVACY
GUIDELINES
Calculate costs
savings and effects
on revenue
development &
financing plans for
farmers
KPI & IMPACT
35. ECOSYSTEM & COLLABORATION SPACE
ProjectCoordination&
Management
Multidisciplinary, collaborative, agile approach for
Digital Transformation
Trials/Use Cases: Knowledge & App development
Lean multi-actor approach
3. EVALUATION
1. CO-DESIGN
2. IMPLEMENTATION
P1
P2
LARGE
SCALE
P3
Data Science &
Information management
Business Modelling,
Governance & Ethics
Ecosystem Development
36. 36
SmartAgriHubs
:
Where IoF2020 stops and
the Digital Agri-Food
Innovation continues...
Establish EU-wide network
of Digital Innovation Hubs
for Digital Transformation
of Agriculture
• Build network covering all EU
regions including technology,
business, sector expertise
+ relevant players
• Critical mass of multi-actor
Innovation Experiments
• Financial support 3rd parties
by open calls - various
public/private funds
• Ensure long-term sustainability
incl. business plans
+ attracting investors
• Promote DIH’s full innovation
accelerating potential
37. 37
Digital Innovation Hub
Incubators
Government
Cooperatives
Farmer communities
Investors
Others
Advisories
Research organisations
Start-ups
Education & training institutes
Large companies
Industry associationsCompetence Center
Other Competence
Centers
Orchestrator
Other
DIHs
Innovation
Experiments
38. 38
DIH innovation services
Ecosystem
• Community building
• Strategy development
• Ecosystem learning
• Project development
• Lobbying
Technology
• Strategic RDI
• Contract research
• Technical support on scale-up
• Provision of technology
infrastructure
• Testing and validation
Business
• Incubator/accelerator
support
• Access to finance
• Skills and education
39. 39
5 basic concepts of SmartAgriHubs to build a EU-wide ecosystem
Innovation
Portal
Innovation
Experiments
Digital Innovation
Hubs
Innovation service
maturity model for
DIHsCompetence
Centres
Layered network
of CCs & DIHs in
Regional Clusters
40. 40
SmartAgriHubs in numbers (20M€)
Ecosystem
108 Partners
Involved covering all EU
68 partners are SMEs
54% of budget allocated to SMEs
Digital
Innovation
hubs
140 DIHs in the existing Network
covering all 28 Member States
Regional Approach
9 Regional Clusters
Attract 260 New DIHs
Flagship
innovation
experiments
28 FIEs
22 Countries involved
13 Cross-border collaboration FIEs
(47%)
Impact
30M additional funding
Mobilized from other sources(public,
regional, national and private)
80 new digital solutions
Introduced into the market
2M Farms involved in digitisation
Open Calls
6M Euros distributed through
Open Calls
75% Open Call budget to SMEs
70 New Innovation Experiments
Arable 8
(28,6)
Fruit 4
(14,2%)Vegetables 5
(17,8%)
Livestock 10
(35,7%)
Aquaculture 1
(3,5%)
5
sectors
41. Agri-Food chains become more technology/data-driven
● Probably causes major shifts in roles and power relations
among different players in agri-food chain networks
Digital Innovation requires a multi-disciplinary, collaborative, agile
approach
● Governance and Business Models are key issues
● There is a need for a facilitating open network infrastructure
Conclusions
41
42. Thank you for your
attention!
More information:
sjaak.wolfert@wur.nl
nl.linkedin.com/in/sjaakwolfert/
Twitter: @sjaakwolfert
http://www.slideshare.net/SjaakWolfert
42
Editor's Notes
This has become our general project approach in many projects...
These are some flagship projects that I just want to mention before I move to FarmDigital
This slide provides an overview of the project aim and objectives.
Through these projects we have developed a success formula in approaching the challenge of ICT and Information Management in Agri-Food :
Trials and use cases form the core, in which we jointly develop as research and business organisations, knowledge and application through a lean multi-actor approach
This means that we quickly develop minimum viable products with involvement of all relevant stakeholders and upscale these through several cycles of development
In parallel we create synergy by
Technical integration: open architectures, standard that can be used as generic building blocks in the trials and use cases
Governance and business modelling: solve issues that arise from the trials and use cases regarding ownership, privacy, trust, etc. and support the businesses in developing sustainable business plans for the apps, services and organization structures that are being developed
Ecosystem Development – support the trials and use cases in embedding their solutions in global ecosystems and upgrading them to a large scale
Project coordination and management is trivial, but we have shown that Wageningen University and Research is very capable to fulfil this role in large public-private projects
This integrated approach will guarantee long-term, sustainable results from these projects.
IoF2020 believes that it is important for a large scale take‐up to maximize synergies across multiple use case systems.
As a consequence, much attention is paid to ensuring the interoperability of multiple use case systems and the reuse of IoT components across them. The figure shows the architectural approach to achieve this during design, development, implementation and deployment.
To enable reuse of components, IoF2020 will provide a catalogue of reusable system components, which can be integrated in the IoT systems of multiple use cases of the project. It will include as much as possible existing components from previous and running projects and (open source) initiatives, including FIWARE, FIspace, etc.
This has become our general project approach in many projects...
In this example WUR as a whole acts as a DIH delivering several services that orchestrate various players outside the DIH.
Ultimately, this should result in newly, funded Innovation Experiments, where several of these players are collaborating on digital innovations.
WDCC delivers various competences within the WUR-DIH to (i) facilitate the orchestration process, (ii) setup an innovation experiment and (iii) execute innovation experiments
An example:
A bright start-up has a splendid data-driven product that is expected to help farmers to improve crop disease management. However, it is still a prototype that needs to be upgraded to a real, marketable product in an innovation experiment. They knock on the door of the WUR-DIH for help.
WUR-DIH uses its network of farmers and cooperatives to get end-users interested to experiment and validate the product. There’s a also a need for an appropriate data infrastructure that is robust and compliant with the state-of-the-art security standards. WDCC brings in their knowledge and network of (large) ICT companies to advise on the right infrastructure and helps to choose (i).
WUR-DIH also does matchmaking in their network of public and private funders to find the financial resources to carry out the innovation experiment.
They help the start-up and the established multi-actor network to write a high-quality project plan. This plan requires a good data management plan. WDCC is delivering a services to write a good data management plan (ii).
Finally the innovation experiment is conducted and WDCC helps to analyse the data by connecting the right data scientists from WUR to the innovation experiment (iii)
Potentially, WDCC can also have this function for other DIHs in the whole SmartAgriHubs network of DIHs, as other competence centers can also be involved in the WUR-DIH.