SMART WHAT?
In its simplest form, smart agricultural and farming systems is a concept in which inputs—from plants, soil, animals, sensors, machines, people, video streams, maps and more—is digitized and placed onto networks. These inputs are integrated into systems that connect farmers, machines, processes, and knowledge to enable better decision making for agricultural systems and food production.
Whatever we chose to call it — “Smart Farming” or “Precision Agriculture” or some new spin on “Industrie 4.0”— we are referring to the practice of utilizing digital and software technologies to manage the spatial and temporal variability of growing crops to improve the efficiency and sustainability of food production. For precision agriculture to reach its promise, certain challenges will need to be met, including:
Standardization Across Equipment Platforms
Equipment OEMs are at the forefront of smart farming systems helping to drive the adoption of many new innovations. However, standardizing equipment standards and interfaces across diverse OEMs will be a minimum requirement if farmers are to realize the value of these new innovations.
Connectivity Challenges
In developed countries, the majority of food production is shifting to mid to large-scale farming operations. In the US in particular, less than 8% of farms account for over 60% of total production. These larger farms require extensive network infrastructure to provide connectivity across operations.
For both small and large-scale farms, connectivity challenges have continued to be persistent. Rural internet access leaves much to be desired. This is particularly true in developing economies, but it’s also true in developed regions like North America. There are many places on Earth where dropping a hard drive in the mail is more efficient than uploading data on a weak 3G signal. The lack of dependable connectivity (4G or greater) will continue to hamper adoption of new digital systems.
Data Management at Scale
Data and analytics are the core value creation mechanisms within smart farming systems. Today. the data being collected from farming implements is exploding which, in turn, is setting the stage for analytics, AI and machine learning solutions.
Farmers increasingly will need to understand which data points they value and in what time frame, daily, monthly or seasonally. Trying to manage the rapidly expanding volume of data as it accumulates is a monumental task, never mind the challenge of processing the data in real time. Even a small farm has hundreds of thousands of different data points that potentially can be collected. Today, it’s still far easier for farmers to enable data collection from modern farm machinery than it will be to aggregate, analyze and gain insights from the data.
Manufacturers like John Deere and Case collect and store data in proprietary formats that must be converted into common formats where the data can be shared and analyzed. Evolving standards like ESRI Shapefiles, and more recently GeoJSON and GeoPackages, provide open standards for how this data can be shared and analyzed, but in the conservative culture of most farm equipment OEMs, competitive advantage is usually perceived, to one degree or another, to lie in ownership and control. It goes without saying that such a culture does not blend well with the notions of openness, transparency and trust.
Farmers expect evolving software tools to be functional, ubiquitous, and easy-to-use. Within this construct, however, the first two expectations run counter to the third. In order to achieve all three, a new approach is required. Achieving completely fluid information and fully interoperating devices, data, people and systems—requires an equally simple, flexible, and universal abstraction that will make information itself truly portable in both physical and information space, and among any conceivable farm implement or technology.
AI and Machine Learning Inform Autonomous Adaptive Solutions
High performance networks, edge computing, data analytics and machine learning are setting the stage for complex adaptive or autonomous systems in agriculture. Similar to self-driving cars, the ability to process large scale data to model processes and derive algorithms that successfully shortcut calculating information will revolutionize agriculture. For example, the ability to combine image processing (including telemetry and LIDAR) with commercial drone technology where data is far more efficiently collected from the air than the ground (including elevation, slope, weed prevalence, pest prevalence, etc.), will drastically improve the efficiency of optimizing crops. A farmer will be able to canvas a farm quickly and at a substantially lower fuel cost than driving a tractor across the farm to do the same tasks.
Scalability of Solutions
Agriculture can exist at almost any scale, from one-man operations to powerful corporate farming enterprises, but many of the challenges farmers face no matter what the scale will be similar. For precision agriculture to succeed, application solutions will need to be scalable. Farmers will need to utilize the same digital tools for both large and small farms.