An Introduction to Digital Agriculture: Part 1
Source: Start Co. | By Oliver McIntosh
This two-part blog post provides a basic overview of the emerging digital agriculture segment. Part one introduces the existing technology and concept of digital agriculture. Part two summarizes future challenges and frontiers in the segment as the market and technologies progress.
Overview
The digital agriculture sector is developing quickly as each segment of the agricultural supply chain seeks to integrate data-driven insights into its processes. Easier access to existing data-collection technology sees the bar of entry steadily lowering; however, critical aspects of connectivity and software tech require further development. The successful bridging of the connectivity gap for rural areas, and development of reliable network software to link data-collection tech together will be critical steps toward widespread adoption, as well as development of an established market model as to which segments of the agricultural supply chain will make inroads to supply these technologies directly to farmers. The sector is enjoying increasing attention from universities, agribusiness, and other stakeholders, and steadily approaches its potential to bring estimated tens of billions of dollars to the global economy, according to the UN Project Breakthrough, along with substantial improvements in productivity, efficiency, and sustainability in the coming decade.
Using data to innovate
Traditionally, agricultural practices rely on time-honored systems of rough measurement, experience, and intuition. In the face of global population growth and increasingly resource scarcity, however, data-driven insights and technology can be leveraged to streamline and maximize efficiency, output, and resource conservation. Immense amounts of data are already generated on the farm: decreasing hardware costs enable farmers to individually utilize drones for aerial crop views, ground-level sensors for insight into crop health and water conditions, sensors and accelerometers to monitor livestock health and care needs, and a host of other network technology to monitor weather patterns and market price analysis, for example.
Unmanned Aerial Vehicles such as drones can be used to monitor and tend to crops.
Digital agriculture describes a broad network of interoperable and interconnected technology, from data-collection devices to intermediary aggregating software to user interfaces, providing data-driven insights to farmers and other agricultural players and ultimately allowing broad and optimizing information-sharing throughout the industry. Think: the Internet of Things (IoT) within the agricultural sector, with tractors, drones, market analyses and weather analyses all interfacing to a farmer-facing UI, as just one example.
In its fullest potential, digital agriculture could optimize not only the input use and procedures of individual farms, but create a broad closed-loop system to monitor resource use and real-time market supply and demand, returning specific actionable insights to multiple levels of the supply chain to identify points of strength and weakness, efficiently administer resources, and maximize investment return. Even further, successful integration of machine learning and AI processes could enable direct task instruction to connected machinery to further streamline cost and input use. Streamlined inputs and costs could pave the way to unlock new sectors and value areas, allowing spare time and money for new method experimentation by individual farmers, and creating new markets for input providers in tech support and maintenance for the data-driven elements of their products.
Digital agriculture holds significant potential to unlock economic value and conserve natural resources— a tangible benefit not only domestically but for developing economies worldwide struggling to meet high nutrient demand with fewer resources. Additionally, integration of Digital Ag concepts into supply chains on a broad, cross-sector basis holds potential to optimize efficiency and resource transactions between farmers, input suppliers, and distributors through complex situational simulations and predictive data science. As industry players and observers focus more attention on the potentialities of Digital Ag, data science can drive substantial innovation in the agricultural segment.
Photo by Navi Singh on Unsplash