The farming industry has seen rapid advancements in productivity over the last century. Over this period, technologically advanced nations have gone from allocating a majority of their workforce to farming to allocating only a small minority. Advanced nations have leveraged machines to remove much of the manual labor, pesticides to combat pests which lower crop yields, and fertilizers as a kind of steroid to boost growth rates, yield, and keep soil fertile over time. While these advancements have been huge boons to the industry, as a society we still face a 70% increase in consumption of food by the year 2050 according to a report from the Food and Agriculture Organization of the United Nations. 1 We can view the past advancements in farming as a kind of brute force method to improve crop yields. The main motivators were based on ideas that were apparent upon inspection and borrowed from other industries. For example, the idea to use machines to replace manual labor in the field does not take much imagination and was in use throughout the economy during the industrial revolution, granted that the construction of these machines is complex.
However, future improvements must come from a more evidence based approach. We will need to harness the invisible (at least to the naked eye) data that drives crop yields such as sunlight, soil conditions, precise water delivery, and localized genetic properties. A great example of such an effort is the suite of products offered by Kaa.2 Kaa provides a software development kit (SDK) to integrate data from connected objects, such as sensors, with back-end infrastructure commonly referred to as servers. The customer base for such applications is huge as it includes virtually all farmers on the planet that aim to use technology to increase yields.
While it is straightforward to purchase and place sensors to monitor soil attributes, moisture, and sunlight, it can be difficult to aggregate this data and then make informed decisions based upon this data. Kaa enables a user to develop a system to perform A/B testing, remote device configuration, real-time device monitoring, and aid in the collection and analyzation of sensor data. This kind of functionality and ease of use is key for developers. As more developers work with such tool kits the prices of software suites will come down. This will increase adoption for farms under family operation which still constitute a substantial number of commercial farms.
Phenonet is a system that “collects, processes, and visualizes sensor data from the field in near real-time” per their website.3 The article header is an image of their system in use. This particular network is advertised to help cut out labor costs associated with testing different crop varieties by automating the yield measurement process. When comparing crop varieties, it is essential to control for variations in the environment surrounding the crops. The Phenonet system aggregates the sensor data which can then be used to generate statistically significant comparisons. These are the same type of comparisons that were essential in the Indigo case when the research teams were trying to compare the efficacy of different seed coatings.
These two examples provide an overview of a fragmented IoT industry that is attempting to revolutionize the farming industry. Sensors must continue to improve in terms of lower cost, lower power, better accuracy, longer lifetime. The software used to interface with the sensors must continue to improve in providing a clear representation of the data that is both actionable and accurate. An emphasis on machine-to-machine actions will further reduce labor demands on the farm.
Overtime, as these integrated systems become more affordable, effective, and available we will see an increase in their use and more players move into the space to try and generate profits from the increase to the farming value chain.
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