Data Science Powers Innovation

The Disruptive Nature of Data Analytics

Disruptive innovation in an industry – where a company with fewer resources takes on established businesses – has the potential to change the landscape and bring new opportunities forward.

Data science presents context and insights to drive disruptive innovation. In the medical industry, understanding data around percentages of medical conditions treated in medical clinics led to the disruptive rise of retail medical clinics to address low-end market needs. Similar opportunities for low-end disruption and new market disruption exist in spades in agriculture, too!

Data science is a space where various scientific, technological, and socioeconomic factors interconnect. These factors can include data and data source availability, high performance analytical processing, open-source analytics, applications that are business and market-ready, and societal global challenge focus. Data science can also be described as the space where analytics, statistical modeling, data mining, artificial intelligence, and monitoring systems intersect. 

Data science isn’t a new concept. It’s been around since the 1960s when a mathematician predicted an empirical science would be born from modern-day electronic computing. Once the personal computer hit the market and households around the world, data science began to take shape, though data was mostly just collected without conversion into insights. Rapid growth in the data science space occurred in the early 2000s with the rise of AI, deep learning, and machine learning applications. 

This 50-year evolution has led to changes in almost every industry worldwide. From personalized healthcare recommendations to route-optimization models that minimize traffic and optimize shipping routes to digital ad targeting, data has created opportunities to see things differently. This has also resulted in innovation in agriculture. 

Innovation in Agriculture

Innovation in agriculture through data science opens the door for new products, services, technology, and even business models. Innovation can take shape in two different ways: identifying an improved solution to address an existing problem, or redefining a known existing problem to make it more meaningful, and then finding a method to solve it. Both types of innovation are powerful, though usually start from different places. 

Innovation around a known problem usually mandates strong expertise and a focused view of an area. Reframing an existing problem requires a holistic view of related spaces to the known challenge. Opportunities for both innovation types exist in agriculture. 

Agriculture has always been an innovative industry. From solving the problems of sticky, midwestern soil with the self-scouring plow to a unique barbed wire that finally found a way to keep cattle contained, agriculturalists have been known to think outside the box to solve well-known problems in new ways. 

In recent years, agriculture innovation has been fueled by data science. For example, GPS system integration into farm machinery has made tillage, planting, spraying, and harvesting more accurate. This technology has taken over the industry, saving inputs by reducing pass-to-pass overlap and allowing effective, environmentally-friendly production practices that require precision – like strip-till or side dressing – to take hold in mainstream row-crop agriculture. 

When farmers have data-based prescriptions for seeding population or nutrient applications and GPS systems that place machinery in just the right place, it’s possible to adjust input rates at a sub-field level. This reduces inputs overall and increases their effectiveness. 

Satellite imagery is another example of innovation in agriculture. Field characteristics can be understood from satellite images, and fields can be broken into unique management zones for more precise management. Seeding population and nutrient application strategies can be set for sub-field areas defined by satellite image views of the bare soil. This increased precision through data science helps farmers to understand their land and adjust management to only use what inputs are truly needed – a win-win for the business and the environment.  

Farmers and agronomists have even taken to the skies with innovation in agriculture. Drones have become another tool for monitoring crop health and disease pressure across many acres. Data collected from drones can be used to make decisions on fungicide or nutrient applications for growing crops to maximize yield and profit. This new view and new data help farmers to make decisions with important economic and environmental sustainability impact. 

These innovations are just the start of using data science in agriculture for increased productivity and sustainability. Artificial intelligence, machine learning, and deep learning are all part of the technology suite that’s driving agriculture forward. 

This aligns with Food and Agriculture Organization’s efforts to increase farming sustainability. Their 2030 Agenda for Sustainable Development calls for enhanced cooperation and knowledge sharing to improve access to technology and innovation. Ultimately, this supports the objective of ending poverty and building economic growth while responding to climate change. 

Specifically for agriculture, protecting the environment and natural resources is key. The FAO is focused on innovation with a systems approach and addressing smallholder farmer needs. Scaling innovation in agriculture requires coordination across the industry, sectors, and partners, including both public and private sector stakeholders. 

Agmatix Fuels Data Science and Agriculture Innovation

Agmatix, a global agro informatics company, is creating a world where high-quality and standardized agronomic data is freely available. This supports ag professionals globally to overcome obstacles in improving sustainable food production and quality in alignment with the FAO Agenda for Sustainable Development. 

Agmatix’s work is data-driven and cutting-edge. We have a constant state of mind to improve, grow, and learn. We strive to change the world for the better with the best solutions for agriculture. That starts with open-source, high-quality, standardized data.

Agmatix’s data science and agriculture innovations include Axiom technology, Agricultural Trial Management, and the Digital Crop Advisor solutions. These data-driven tools unlock impactful insights to empower decisions on the farm. 

Axiom is a disruptive technology that standardizes and harmonizes data from various sources. The GUARDS (Growing Universal Agronomic Data Standard) protocol is based on the FAIR data principles with a specific focus on data interoperability and reusability. This includes standardized definitions with a unique bottom-up approach, leveraging ML capabilities, and unit conversion. Automatic agricultural data ingestion allows Axiom to digest data at scale, even from multiple sources, with a low cycle time and high integrity. 

Agricultural Trial Management is designed to improve productivity and efficiency while increasing the quality of field trial data. End-to-end capabilities give visibility and control throughout the entire field trial. The mobile application enables seamless in field data collection and management for instant data validation and analysis. Collaboration across all stakeholders – from trial operators and researchers to farmers – is easy, especially with field trial data that’s easily accessed, shared, and understood. Quick, data-driven decisions are possible when field trials and field trial data is well-managed. 

Digital Crop Advisor is an agriculture data science application that optimizes crop production and creates nutrient prescription plans in an easy-to-use dashboard. Big data insights help quantify and compare sustainability KPIs such as carbon footprint and nitrogen leaching. It’s even possible to compare tradeoffs between productivity and environmental impact to make educated decisions about crop nutrition. This innovative tool supports agronomy teams in addressing sustainable agriculture at any scale. 

Agmatix’s data science-based solutions support agronomists, researchers, companies, and farmers in making a difference in agriculture and in the world. Supported by open source, high-quality data, Agmatix is determined to support sustainability. 

You may be interested in:
Why Collaboration is the Key to Innovation
Continued Innovation at Agmatix
The impact of big data analytics in transforming the ag industry

Moving Forward 

Data science will prove to be a key part of innovation related to the future of agriculture. When it comes to food production and sustainability, data science will fuel new approaches to balancing productivity and environmental friendliness. Some of that innovation is taking place now – and Agmatix is one example of how data science applications in agriculture illuminate a better future. 

Agmatix works to leverage past and current data to help you make the best decisions  – whether that’s tackling a known problem in a new way or redefining a problem to make it more meaningful when solved. Opportunity will be unlocked through data science in agriculture. Take the next step in your innovation with Agmatix.