Farms need to do more with less than ever before to remain competitive. This is especially true when it comes to big ticket items like agricultural machinery. We’re now seeing the implementation of new Internet of Things (IoT) technologies to track, analyze and act on data from machines like tractors, combines, planters and pickers. When paired specifically with ag machinery, these technologies will unlock the true value of unused data sources to drive new efficiencies in an increasingly resource-constrained world.
IoT technologies are much more than smart doorbells or smartphone-controlled lighting. It’s the industrial applications of IoT, rather than the consumer, that stand to make the greatest impact on how business is done, and agriculture will be a big part of that. While we’ve seen internet connected technologies in ag for over a decade, these applications have generally utilized what I like to call IoT 1.0, meaning: sensor + datalogger + cell modem = website with data. Now we’re seeing something new. By taking advantage of lower costs in both sensor and computing hardware and applying layers of analytical software, we’re poised for an explosion of new connected solutions. This explosion will be felt, in terms of adoption and value created, nowhere in ag as strongly as farm machinery.
Benefits of IoT tech for farm machinery
The emergence of IoT technologies for heavy machinery makes sense for a number of reasons. To start, a tractor and attachment are the largest capital goods a farmer will buy. According to the USDA’s Economic Research Service, tractors and equipment accounted for roughly 65 percent of a farmer’s capital expenditures in 2013. As a result, it’s vital to put this capital to work by driving up efficiency and driving down idle time. New IoT solutions are aimed at exactly that. By pulling harvested crop data via machine embedded sensors in real-time, a farm manager who is miles away can see how certain crews are doing relative to others. This also enables them to more effectively manage downstream operations such as transportation, distribution and storage by diverting more resources to high performing crews. Going one step further, we’re seeing applications developed to automate asset dispatch so that trucks are diverted to a soon-to-be-full harvester.
Another reason for the close relationship between IoT and machinery is the sheer amount of data produced. According to Monsanto, sensors deployed on harvesters can collect up to 7GB of data per acre. Tractor manufacturers themselves are already gathering massive amounts of data on everything from engine performance to operator seat positions. OEM’s of attachments like harvesters are also starting to do the same with sensors and software that can measure the weight of harvested product in real-time (Figure 1). Another example is a tiller attachment calculating clay content of soil based on resistance sensors that, coupled with GPS feeds, can compile detailed soil composition maps. While upwards of 80 percent of a machine’s data might go unused today, don’t expect it to go to waste for long. As advanced analytics at the tractor level and in the cloud catch up to the data produced, it’s expected that value will be there sooner rather than later.
This data has the potential to change how business is done. Most of the data captured from these devices can be used to directly affect the farmer’s decisions, but a growing set of data can also drive new revenue streams, new services and entirely new products. One of the most common of these new revenue streams for big machinery OEMs is preventative maintenance. While a farmer might not be aware of an issue during operation, multiple sensors can measure vibration, noise, stress and resistance of vital parts within the machine. This data can predict when parts might fail before they actually do, thus opening up a new service opportunity for machinery vendors. Other downstream sectors can also benefit from data collected by machinery. For example, a packing/cooling facility might be open to paying for access to real-time harvest data so they could be better prepared to receive product.
Lastly, we have recently heard a lot about farm labor shortages affecting all sectors of ag. Between 2002 and 2012 the number of new field and crop workers immigrating to the United States fell by roughly 75 percent, according to the New American Economy Organization. IoT technology for machinery can help solve the labor shortage problem by driving operational efficiencies with mechanized harvesting of crops that once had to be hand-picked. In my agricultural backyard of the Salinas Valley, lettuce producers are starting to deploy new harvesters that slice off heads of lettuce using supersonic jets of water, and strawberry growers are testing robotic picking machines that utilize IoT and edge (field level) computing power to scan for red, ripe berries for harvest by robotic arms. An interesting byproduct of this automation technology has actually been more interest in harvest related jobs. In fact, according to Taylor Farms – one of the nation’s largest lettuce producers – they have recorded a larger number of new workers in their 20s and 30s applying specifically to work on the new automated machine crews due to interest in this technology.
Challenges to overcome with IoT machinery for ag
As with the emergence of all new technology, however, challenges do exist. Agriculture is tremendously specialized, so a particular technology applied to potatoes, for example, might not be applicable to onions. Additionally, technology providers must navigate a variety of sensor types, data interfaces and connectivity options. As a result, bringing these technologies and knowhow together into full IoT solutions is becoming just as valuable as the core technology itself. Another challenge that must be overcome is data access since data from one source is often siloed and difficult to access from outside that ecosystem. For example, a tractor with a proprietary data collection system may not allow other data to be accessed outside of their system. As a result, some parts of a farm might struggle to benefit from the data captured in others, and this should be considered when developing or evaluating IoT solutions.
The good news is that a growing number of technology providers are addressing these challenges. At Infiswift, we’re leveraging our scalable and hardware agnostic IoT platform to help OEMs and other enterprises commercialize and deploy IoT enabled technologies. What’s different is that these solutions take advantage of increasingly commoditized hardware and use any form of data connectivity (Bluetooth, ZigBee, WiFi, cellular, Low-Power WAN, etc.) based on the application requirements. On the software side, infiswift can deploy computing power and intelligence at the edge (field level) and in the cloud (server level) to gather data that is open to any user. This flexible solution is customized for each customer’s specific needs and drives costs down by minimizing cloud and data communications costs.
As we move into a future where everything is connected and the utilization of data becomes increasingly important, we need to be active in ensuring these new technologies are designed and exist to help drive value – whether that’s through increasing efficiency, decreasing failure rates or providing new levels of operational insight. Big machines will likely be where this revolution starts, but won’t be where it ends. That means that you’ll have to bring your imagination because the next round of IoT-driven technologies will only exist if people are able to connect the dots between a potential source of data and a solution that might not yet exist.