How digital innovation is transforming agriculture

Published on in

TL;DR The Internet of Things is a digital innovation that is transforming agriculture and improving productivity, addressing challenges in global food security

This note demonstrates how and why the Internet of Things (IoT) embodies digital innovation. It defines IoT in terms of Connected Devices and uses the Layered Modular Architecture to describe how the technology uses modularity and interoperability to enable generativity. Platform thinking is pervasive in IoT and the technology’s generativity enables it to serve as a platform for a range of applications. These platforms are being combined with third-party content, services, and devices to form ecosystems that are transforming modern day agriculture. Using big data and programmatic decision making, IoT enabled smart-farming is having transformative effects on customer experience, operational processes, and business models in agriculture.

It is understood that food production must more than double over the next 35 years and competition for scarce input resources means that 90% of growth in supply must come from higher yields (United Nations, 2009). New IoT-based farming techniques have the potential to boost farm productivity by 70% by 2050 (Sarni, et al., 2016). To achieve this however, farmers must undertake practical change and continuously test-and-learn. Australia is a competitive net-exporter of agricultural production (Department of Foreign Affairs and Trade, 2014) and disruption in the sector will impact local businesses, the economy, and regional food security.


Since the earliest civilisations, humanity has experienced waves of agricultural innovation that have been critical for population growth and prosperity. In the 18th and 19th centuries, innovation in crop-mix and crop-rotation techniques incited the British Agricultural Revolution, which saw England’s population triple over the course of a century (Overton, 2011). The Green Revolution of the 20th century, was driven by technological change that enabled the packaging of modern irrigation, improved seeds, synthetic fertilizers, and pesticides (Hazell, 2009). Today, a second Green Revolution promises to enhance farm productivity, by capitalising on technologies that comprise the Internet of Things (Sarni, et al., 2016).

In this note, I will demonstrate why and how technological change is critical for this enduring human pursuit of agricultural productivity. I will describe how digital innovation is disrupting traditional farming and the transformative effects of this on individuals, businesses, and the agricultural industry more broadly. Finally, I will examine the practical implications of these disruptive effects, as we navigate global challenges in sustaining explosive population growth and adapting to rapid climate change.

Industrial significance

Food production must more than double over the next 35 years and competition for land, water, and energy means that 90% of growth in supply must come from higher yields.

The world’s population has grown by approximately one billion people in the past decade, and despite slowing growth we remain on track to add another billion over the next 15 years (United Nations, 2015). To match this population growth, food production has intensified significantly in the past half-century, dramatically reducing the proportion of the world’s hungry (Godfray, et al., 2010). However, “more than one in seven people today still do not have access to sufficient protein and energy from their diet" (Godfray, et al., 2010, p. 812). Per a medium projection variant, global population is expected to reach 9.7 billion by 2050 and 11.2 billion by the end of the century (United Nations, 2015). To satisfy this growth in demand, food production must increase by 50% to 70% over the next 35 years (United Nations, 2009).

Correlated with population growth is an increase in wealth, which is shifting consumption behaviour towards more processed foods, meat, dairy, and fish (Godfray, et al., 2010). Additionally, the agricultural sector is experiencing intensifying competition for land, water, and energy. For example, almost half of Australia’s total land area was used for agriculture in 2015 (Australian Bureau of Statistics, 2016). Furthermore, in 2004, the global agricultural sector consumed about 70% of the planet’s accessible freshwater (Clay, 2004). Extrapolating this trend sees freshwater usage peak at about 80% in 2016. With current limitations on scarce input resources such as land and water, 90% of growth in food supply must come from higher yields on-farm (United Nations, 2009). Together with a changing climate, these factors are adding pressure on an already strained food system, raising global concerns over global food security (Godfray, et al., 2010).

Australian context

While food security and disruption in agriculture is of global implication, this note will draw on examples from Australian agriculture. Australia is a competitive net-exporter of agricultural production (Department of Foreign Affairs and Trade, 2014). In 2015, the gross value of Australian agriculture increased to $53.6 billion, accounting for approximately 3% of the country’s gross domestic product (Australian Bureau of Statistics, 2016). Australia is home to more than 130,000 farming business, 99% of which are family owned (National Farmers' Federation, 2012). Together these businesses provide 93% of domestic food supply (National Farmers' Federation, 2012). Agricultural exports accounted for 15.5% of all merchandise exports in 2012-13 (Department of Foreign Affairs and Trade, 2014), when each farmer produced enough food to feed 600 people on average (National Farmers' Federation, 2012). Thus, disruption in the sector will have significant implications on local businesses, the Australian economy, and regional food security.

The Internet of Farming Things is Digital Innovation

Digital innovation is described as the coupling of digital and physical components to create new products (Yoo, et al., 2010). There are three key characteristics that distinguish digital innovation from earlier technological advancements: (1) reprogrammability, (2) the homogenization of data, and (3) the self-referential nature of digital technology (Yoo, et al., 2010). With such a strong emphasis on physical product, the Internet of Things is a clear embodiment of digital innovation.

The Internet of Things (or IoT) is defined as “the network of Connected Devices, which can be accessed through the Internet" (Michael, 2015). A Connected Device in this context is defined as any physical object with two core digital components: network connectivity and computational capability. This entanglement of physical and digital is a key characteristic of convergent and generative digital innovation (Yoo, et al., 2012). The digital components in Connected Devices enable reprogrammability either by directly embedding computational logic on the device, or by leveraging network connectivity to access computation on a remote server. When attached to sensors and actuators, Connected Devices enable homogenization of data by collecting information or emitting actuating signals, which can be processed locally or transmitted digitally. Finally, Connected Devices are self-referential in that they achieve the greatest positive network effects, when they are part of a broader network of devices.

It is not necessary for all Connected Devices to be connected to public networks such as the internet. In mission-critical applications for example, it is preferred that these devices remain within private networks (Michael, 2015). For example, Airbus retains Connected Devices on its aircraft within closed and secure networks (Nizam, 2014). However, even though internet connectivity can be regarded as optional, Connected Devices are most powerful (and self-referential) when they exist as part of the broadest network possible: the public Internet.

The Layered Modular Architecture can be used to illustrate how Connected Devices leverage modularity and interoperability to enable generativity

The characteristics of digital innovation set the foundation for a Layered Modular Architecture consisting of four layers (from top to bottom): content, service, network, and device (Yoo, et al., 2010). Each of these layers represents an independent set of design considerations, since a modular architecture is one that is based on interoperability between layers. This interoperability is made possible by standardised interfaces that prescribe how layers are expected to interact with one another. An example of such interfaces includes Application Programming Interfaces, which have existed since the earliest days of computing (Collins & Sisk, 2015). An Application Programming Interface (API) is a software component that enables digital applications to exchange data or services (digital assets) following a previously agreed upon contract. Remote APIs allow access to these digital assets from an independent server over a communications network, typically the internet. Provided consistent implementation of the contract, modules in a network can be freely exchanged with minimal impact on the network’s collective functionality (ibid.).

Connected Devices with their physical nature, computational logic, and networking capability span across the device and network layers of this architecture. The use of standardised APIs allows for independent design and open exchange of the computation and networking elements. Moreover, the use of Remote APIs over the public internet enables interactions from higher architectural layers, namely services and content.

The principle of “generativity" describes a technology’s ability to enable users to create or produce new content or use-cases, unanticipated by the original designers (Zittrain, 2008). Generativity also implies that technology can “become inherently dynamic and malleable" (Yoo, et al., 2012, p. 1399). The modularity and malleability of Connected Devices, combined with the layered nature of the technology, gives rise to characteristics of generativity within IoT. This makes the technology ideal to serve as a platform for a variety applications such as smart-homes, manufacturing, and agriculture.

The IoT’s generativity enables it to serve as a platform for technological ecosystems in agriculture

An indicator of pervasive digital innovation is the emergence of platforms as a centre for further development (Yoo, et al., 2012). A platform is defined as “a building block, providing an essential function to a technological system — which acts as a foundation upon which other firms can develop complementary products, technologies or services" (Gawer, 2009, p. 2). Combining a platform with its various components forms an ecosystem. The Internet of Things is most powerful when its encompassed devices exist within contextually aware ecosystems, reliant on standardised interfaces for machine-to-machine communication (Michael, 2014).

Platform thinking is evident across many IoT efforts that extend beyond consumer markets and into industrial applications, which demonstrates the generativity of the technology. For instance, Thingworx develops an enterprise IoT Platform as a Service that can support a range of applications including agriculture (ThingWorx, 2013). It enables users to create and produce use-cases that were not originally anticipated and then offers a marketplace to sell and distribute user creations. Another example is the platform from GE and Bosch for Industrial IoT and “Industry 4.0" applications (Corner, 2016). The joint-venture emphasises modularity and interoperability with the hope of generative outcomes.

Water is a critical input resource for improved crop-yields (Clay, 2004). Observant is an Australian company that builds a platform for the precision management of water in agriculture (Observant, 2017). The organisation designs and builds IoT hardware that provides third-party sensors and actuators with on-farm power management, internet connectivity, and local computational power. Thanks to standardised interfaces, third-party firms can build complementary sensors and actuators, that use the Observant platform as a foundation. On a farm seeking precision water management, sensors may include: weather stations and flow meters, while actuators would include: pumps and irrigation systems. The system collects and aggregates all the data collected into a cloud platform, which homogenises data enabling big-data analysis and programmatic decision making. Furthermore, the use of open APIs enables third-party firms, such as insurance and credit brokers, to provide customised services and content to users – demonstrating the technology’s capacity to spread across all layers of the Layered Modular Architecture, as well as its ability to combine various components into an effective and contextually aware ecosystem.

Global agriculture companies are also developing similar IoT platforms. For example, John Deere is adding new environmental sensing capabilities to its competing FieldConnect platform (Schwalbe, 2013; Moran, 2014). Monsanto is growing its digital farming platform through the $1b (USD) acquisition of Climate Corporation (Plume, 2016). The company is also expanding its reach into soil and weather sensing to boost the capabilities of its Climate FieldView platform (Plume, 2016). OnFarm Systems is another platform with an emphasis on simplifying data collection and management (OnFarm Systems, 2017). The platform helps growers turn their data into decision-aids for everything from disease management through to irrigation planning.

When it comes to agriculture, the platform strategy discussed extends beyond hardware systems. For example, Telstra, Australia’s largest telecommunications provider, is boosting its National Broadband Network efforts as a core network-layer platform, to better accommodate “Precision Farming" needs; stressing the importance of improved water and fertilizer management (Muscat, 2015).

Using big data and programmatic decision making, IoT enabled smart-farming is having transformative effects on agriculture

Digital transformation involves leveraging technology to fundamentally alter and improve the performance and reach of organisations (Westerman, et al., 2014). It is a topic on the minds of many executives, as they explore ways to use digital technology to improve their company’s customer experience, operational processes, and business models. The breadth of this transformation is clear in agriculture, as “the IoT’s ability to collect and correlate resource data means that the agricultural sector can develop solutions to do more with less—for instance, by enabling automated precision agriculture" (Sarni, et al., 2016, p. 7).

Big-data in agriculture is transforming customer experience, operational processes, and business models

Data is critical to farmers and is something they have collected meticulously for generations, however IoT is transforming how data is collected and used (Muscat, 2015). IoT delivers an enhanced customer experience by enabling farmers to move from manual to automatic data-collection. This increases the frequency, accuracy, and reliability of the data collected, enabling reliable data-driven decision making. “Data-driven or ‘algorithmic’ decision-making is based on collecting and analysing large quantities of data that are then used to make strategic decisions" (Newell & Marabelli, 2015, p. 4). This access to big-data transforms how farmers make a range of operational and business decisions (Sarni, et al., 2016).

For example, breeding selection is being transformed as farmers now use tools such as RamSelect to handpick the most suitable rams for their flock, using data and algorithms to optimise for factors such as wool quality, weaning percentage, carcase size, and parasite resistance (RamSelect, 2017). When it comes to irrigation and chemical use, growers are known to make application decisions uniformly across large plots of land, this behaviour can lead to high variability in crop-yield (Hogarth-Scott, 2016). Using tools such as the Observant or OnFarm platforms, growers can now easily collect and analyse data from different locations across the property, allowing for decision-making that is localised and saving on critical resources. Combining algorithmic decision making with in-field actuators such as irrigation systems, operational processes are being transformed by entirely automating some irrigation activity (Sarni, et al., 2016). Finally, IoT technology continues to transform business models, as organisations such as Monsato leverage their platform to adopt alternative pricing models that include seasonal seed-licensing (Monsanto, 2017).

The introduction of more and better data goes beyond benefiting business and improving agricultural productivity. It is also impacting end-consumers by demonstrably improving the nutritional value of certain crops (Sarni, et al., 2016). For example, Fujitsu is using an enhanced sensor system to grow raw lettuce with 80% less potassium, an improvement that is critical for people suffering from kidney disease . Similar advancements are being made in dairy cows to track and reduce the risk of illness, improving consistency in milk-quality. IoT is also transforming the food supply chain by helping suppliers automatically and steadily monitor temperature during transit, ensuring customers receive freshly kept produce (ibid.). Overall, the IoT is introducing a level of digital transformation that is empowering growers, farmers, and food suppliers to fundamentally alter and improve their crop-yields, livestock health, and produce quality.

IoT will have practical implications on farm management, impacting individuals’, and introducing cyber-security risks

While adopting new IoT farming techniques has the potential to boost farm productivity, it does come with considerable practical implications for farm management. The pressure of higher yields, and the transformation made possible by technological advancement, is encouraging a conversion from family-owned farms to large-scale industrial farming (Sarni, et al., 2016). In Australia, Coles, one of the country’s largest supermarket chains, has been making agricultural investments that increase the use of technology and vertically consolidate its supply chain (Liew, 2011; Heffernan, 2015).

The introduction of IoT technology on farms demands higher levels of technical literacy. This is creating a digital divide on farms, where those with the skills required feel empowered and others face a real difficulty staying on top of things (Herbert, 2013). To address these issues of inequality, the local council of Tamworth in New South Wales, has setup a Digital Hub where they have upskilled 4000 people in the past two years (The Regional Australia Institute, 2016).

Harbouring large amounts of data, and continuously transmitting control signals across large properties, exposes businesses to cyber-security risks. Cyber concerns also arise with algorithmic decision making, since automation is likely to create problems where no individual or organisation can fully explain why certain decisions are made (Newell & Marabelli, 2015). These challenges will all need to be addressed as the technology scales across the industry.

It is recommended that farmers continuously test-and-learn

A consistent trend in the technology sector, is the struggle that large companies face in staying at the top of their industry (Bower & Christensen, 1995; Christensen, et al., 2015). This notion of displacement is captured in Disruption Theory, which formally “describes a process whereby a smaller company with fewer resources is able to successfully challenge established incumbent businesses." (Christensen, et al., 2015). With the democratisation of IoT platforms, it is critical that managers continuously scan for new IoT applications that will enable them to better serve their mainstream customers. They can do this by adopting a test-and-learn approach to continuously experiment and adapt (Anderson & Simester, 2011).


Anderson, E. T. & Simester, D., 2011. A Step-by-Step Guide to Smart Business Experiments. Harvard Business Review, March.

Australian Bureau of Statistics, 2016. 7121.0 - Agricultural Commodities, Australia, 2014-15, Canberra: Commonwealth of Australia.

Australian Bureau of Statistics, 2016. 7503.0 - Value of Agricultural Commodities Produced, Australia, 2014-15, Canberra: Commonwealth of Australia.

Bower, J. L. & Christensen, C. M., 1995. Disruptive Technologies: Catching the Wave. Harvard Business Review, January, pp. 43-53.

Christensen, C. M., Raynor, M. E. & McDonald, R., 2015. What Is Disruptive Innovation?. Harvard Business Review, December, 93(12), pp. 44-53.

Clay, J., 2004. World Agriculture and the Environment: A Commodity-By-Commodity Guide To Impacts And Practices. Kindle Edition ed. Washington(D.C.): Island Press.

Collins, G. & Sisk, D., 2015. Tech Trends 2015: API economy, s.l.: Deloitte Univeristy Press.

Corner, S., 2016. Industry giants GE & Bosh team for Industrial IoT/Industry 4.0. [Online] Available at: [Accessed 4 January 2017].

Department of Foreign Affairs and Trade, 2014. Australian agricultural exports. [Online] Available at: [Accessed 22 December 2016].

Gawer, A., 2009. Platforms, Markets and Innovation. Cheltenham(Gloucestershire): Edward Elgar.

Godfray, H. C. J. et al., 2010. Food Security: The Challenge of Feeding 9 Billion People. Science, 12 February, 327(5967), pp. 812-818.

Hazell, P. B., 2009. The Asian Green Revolution. IFPRI Discussion Paper ed. s.l.:Intl Food Policy Res Inst.

Heffernan, M., 2015. Coles eyes fruit farm investments. [Online] Available at: [Accessed 4 January 2017].

Herbert, L., 2013. Farmers hamstrung by digital divide. [Online] Available at: [Accessed 8 January 2017].

Hogarth-Scott, P., 2016. The Internet of Things and smart agriculture. [Online] Available at: [Accessed 4 January 2017].

Liew, R., 2011. Coles buys in Kalgoorlie. [Online] Available at: [Accessed 4 January 2017].

Monsanto, 2017. Licensing: The Facts on Monsanto's Approach To Licensing. [Online] Available at: [Accessed 4 January 2017].

Moran, C., 2014. John Deere launches ‘information-enabled agriculture’. [Online] Available at: [Accessed 4 January 2017].

Muscat, L., 2015. Smart Farming: The future of agriculture and NBN. [Online] Available at: [Accessed 4 January 2017].

National Farmers' Federation, 2012. NFF Farm Facts: 2012, Brisbane: s.n.

Newell, S. & Marabelli, M., 2015. Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of ‘datification’. Journal of Strategic Information Systems, 5 February, Volume 24, pp. 3-14.

Nizam, C. K., 2014. Connecting the Unconnected. Toulouse, Airbus.

Observant, 2017. Observant Products. [Online] Available at: [Accessed 4 January 2017].

OnFarm Systems, 2017. OnFarm for Growers. [Online] Available at: [Accessed 4 January 2017].

Overton, M., 2011. British History in depth: Agricultural Revolution in England 1500 - 1850. [Online] Available at: [Accessed 4 January 2017].

Plume, K., 2016. Monsanto's Climate Corp to expand digital farming platform. [Online] Available at: [Accessed 4 January 2017].

RamSelect, 2017. RamSelect is a tool for helping you select the best rams for your flock.. [Online] Available at: [Accessed 4 January 2017].

Michael, H., 2014. Architecting the rise of Connected Devices. Sydney, Deloitte Digital.

Michael, H., 2015. Defining the Internet of Things – Internet optional. [Online] Available at: [Accessed 2 January 2017].

Sarni, W., Mariani, J. & Kaji, J., 2016. From dirt to data: The second green revolution and the Internet of Things. Deloitte Review, 25 January, Issue 18.

Schwalbe, K., 2013. John Deere Adds Array of Environmental Sensors to Field Connect. [Online] Available at [Accessed 4 January 2017].

The Regional Australia Institute, 2016. Digital Futures: A case study of the Northern Inland region of NSW, Barton: Regional Australia Institute.

ThingWorx, 2013. ThingWorx Launches the First Marketplace for the Internet of Things. [Online] Available at: [Accessed 4 January 2017].

United Nations, 2009. High-Level Expert Forum: Global agriculture towards 2050, Rome: s.n.

United Nations, 2009. Panel Discussion on Agriculture Development and Food Security. New York, s.n.

United Nations, 2015. World Population Prospects: The 2015 Revision, Key Findings and Advance Tables, New York: United Nations.

Westerman, G., Bonnet, D. & McAfee, A., 2014. The Nine Elements of Digital Transformation. MIT Sloan Management Review, 7 January.

Yoo, Y., Henfridsson, O. & Lyytinen, K., 2010. The New Organizing Logic of Digital Innovation: An Agenda for Information Systems Research. Information Systems Research, December, 21(4), p. 724–735.

Yoo, Y., Jr., R. J. B., Lyytinen, K. & Majchrzak, A., 2012. Organizing for Innovation in the Digitized World. Organization Science, September–October, 23(5), p. 1398–1408.

Zittrain, J. L., 2008. The Future of the Internet - and how to stop it. Paperback Edition ed. New Haven & London: Yale University Press.