Agriculture’s Next Breakthrough: New Technologies Are Driving Efficiency, Data Insights
We live in an exciting time in agriculture. Not only is technology advancing rapidly, but the understanding and comprehension of technology and data is becoming easier to decode and decipher. By 2050, the population of Earth will amass 9.7 billion people, each with needs of food and water. Throughout time, revolutions have changed the way we live and produce, and as we continue to breakthrough with technology understanding tied to agronomics and decision making, the next revolution is here and continues to grow.
Digital agriculture has enabled technology to provide insights and information across farms, from anywhere in the world. The ability to collect, transfer, store, and analyze information has never been easier, but with this brings a challenge. As we continue through the precision agriculture revolution and the adoption and use of GPS into now using data and technology to enable precise applications and decision making, the next phase is taking the digital information and turning it into Decision Agriculture, using this plethora of ‘big data’ and information to make faster, more complex and impactful decisions, both proactively and reactively to increase efficiencies and profitability. Insights from digital tools are not set to replace in-field agronomics, or develop technologies that do all the work for us, but instead to make us more efficient at finding, deciphering, and developing decisions forward with more information than we have ever had before.
Decision Agriculture is filled with digital information and technology tools. Here is a close look at some that are already making an impact:
- Artificial Intelligence (AI). AI is becoming more popular with the ideas of automation, monitoring, control, and predictive analytics coming to the forefront of decision making and implementation. Robotics and automation preparing machine operations, in-field tasks, and high efficiencies allow producers to ensure precise applications are occurring while they can be away from these operations or spending time on other projects. Predictive analytics from models provide insight on potential losses from future environmental or pest events.
- Machine Learning. A more defined way of using AI, machine learning allows for training algorithms and machines to learn from the tasks at hand without explicitly developing code and programs to do so. Machine learning and training uses big data to find trends, patterns, and anomalies to develop decision making through the large amounts of data and insight that are being produced today and in the future in agriculture.
- Data Management and Traceability. Blockchain became popular with the rise of cryptocurrencies in the last year, and technologies similar to blockchain with data traceability, stability, and management have opportunities to help feed a population with a rising interest in how their food was produced. Keeping identity preserved crops in check in a storage and transportation system, while providing ledgers of information and data flow help niche markets ensure they have the right product, and keep mix ups and confusion of flow in check while giving the opportunity for premium markets to producers. Instant payments to producers through verified technology data streams help get transactions to the farm quicker, to make decisions for the next season on purchases early while allowing a place for records to be kept year after year that can be used to help feed decision making in the future.
- Internet of Things (IOT). IoT continues to grow with the vast amount of sensor technologies out and being developed that “connect” the farm to the digital world for massive amounts of continued insight. IoT sensors can provide data collection, insight, and connectivity from the field on parameters such as soil moisture, health, fertilizer tank levels, fuel levels, irrigation system monitoring, and many more. These multiple technologies tied to IoT are the big data pool that can continue to be fed into technologies like machine learning as a part of AI for improved decision making quicker than before.
When thinking about how technology is moving rapidly, I align what we see today in agriculture to what we are seeing in the medical fields. Years ago, we would have had trouble seeing anything other than an X-Ray, and can now monitor across multiple pools of data and subsets of information with constant monitoring to help us make a decision quicker than we ever had before for our health. These insights have helped save lives, make health decisions sooner, and give us the proper information to know we are making a good choice for our lives. Without technologies, this would have been difficult to accomplish and move forward for our well being.
While many of these technologies can be intertwined, it is exciting to see what is coming in the future for agriculture when thinking of insight and efficiencies. The key takeaway is to remember that these new tools and technologies don’t replace what is happening in the field, and that efficiencies of workflow and application can be addressed by using these new developments. Data integrity and ownership is a key topic in today’s agricultural developments, and understanding where your data sits is crucial to ensuring the decision you want to make and where you want to go aligns with your and the provider’s vision. As we reach the challenge of growing more food for more people, understanding what is coming and where the most opportune decision can be made requires working with those you trust and can help through the adoption phases of new technology.