In recent years, much has been said and written about the use of big data in agriculture. Applications have included everything from better management of cattle to streamlining the supply chain.
However, the possibilities of the marriage between technology and farming remain largely projections for the future. While other industries have been quick to adapt tech innovation, it’s happening slower in agriculture. That’s led to the continued loss of billions of dollars due to inefficient methods that result in about a third of all food produced each year going to waste, according to a report from McKinsey & Company.
When it comes to technology on the farm, there remains a huge amount of untapped potential.
While the use of technology in agriculture has been adapted slowly, most observers maintain it is still coming. Certainly, venture capital companies continue to make huge investments in this area. Tech investments in agriculture have increased 80% annually since 2012, Forbes reported.
The market for software used as “precision farming tools” also has grown. This includes programs for soil assessment, weather forecasting, yield monitoring and projecting potential pest infestations.
Experts also project that the use of artificial intelligence (AI) and machine learning will grow rapidly in the global food market. Dublin, Ireland-based Research and Markets projects that the “AI in agriculture” market will jump by about 22% annually between now and 2025.
The growth, according to the Irish company, will be driven by many factors. They include:
- The food demands of an increasing population
- The increased need to adopt information management systems in agricultural operations
- The availability of innovative technology that will increase crop productivity
- More initiatives and incentives from governments worldwide to improve agricultural operations
Steps Needed to Adopt Agriculture Technology
The United States government has made adoption of innovative technology a focus of their efforts. They have gathered information from the agriculture industry, academia and government agencies to formulate a plan to integrate innovation into modern farming.
As part of this, the National Institute of Food and Agriculture (NIFA) launched a “Food and Agriculture Cyberinformatics and Tools Initiative” in late 2016. That program has identified four major steps needed for faster adoption of technology and agricultural analytics.
They start with developing a culture where there is support and rewards for those who build off each other’s datasets, standardized techniques and experimental designs so that more collaboration happens between researchers and agricultural operations.
The next step calls for creating partnerships between the government and private companies in which data and ideas are shared. And finally, creating a “robust infrastructure” of datasets that are open to the public for analysis, interpretation and application to individual agriculture operations.
Additionally, a workforce needs to be trained that has the skills to collect and analyze large amounts of data that support more efficient farming operations.
The Future Possibilities
As innovation and ideas come from a variety of sources in both private industry and academia, the potential use of technology in agriculture continues to grow.
Some of the more interesting ideas for “smart farms” include:
- Sensors that can provide detailed information on the condition of both crops and the soil
- GPS units on farm equipment that can help determine the best use of heavy machinery
- The use of data analytics in the supply chain to move products faster from farm to table, reducing the amount of waste and spoiled food
- The use of drones to survey fields and supply data on crop conditions and potential problems
- The analysis of historical data to determine the best type of crops to be planted
- The use of data and analytics to help farmers hedge against potential losses and smooth out cash flow, which is always a concern in any agriculture operation
- The use of AI to create autonomous navigation systems that could lead to machinery such as self-driving tractors, something John Deere has worked on for years
The supply chain issue is where agricultural analytics might have the biggest impact. The overall goal is to make the global food supply chain more efficient, which should drive down costs for both the industry as well as consumers.
Data also could lead to better service for customers. Using data from their customer base, food operations could create predictive models that take data from past buying trends and other indicators to create and ship products before they are in demand.
This data also could open up niche markets for different agriculture operations. A huge demand for certain types of food in specific locations or among a specific population demographic could create new markets for both larger corporations and smaller farm operations.
The possibilities seem both endless and beneficial to farmers and consumers alike. While getting there is taking a little longer than optimistic projections expected, all of these changes are expected to happen eventually and lead to a smarter, more efficient agricultural industry.