The farming and agricultural industry relies on innovative ideas and technological advancements to help increase yields and better allocate resources. The late 19th century and the 20th century brought a number of mechanical innovations, like tractors and harvesters. Today, a driving force behind increased agricultural production at a lower cost is the Internet of Things (IoT), which leaves the door wide open for engineers looking to bring a smart farming solution or IoT agricultural sensor to market.
Internet of Things applications in agriculture include farm vehicle tracking, livestock monitoring, storage monitoring, and much more. For example:
The next several years will see increasing use of these and other smart farming technologies. In fact, IoT device installations in the agriculture world are projected to experience a compound annual growth rate of 20 percent. And according to a January 2016 Machina Research report, the number of connected agricultural devices is expected to grow from 13 million at the end of 2014 to 225 million by 2024.
Below, we’ve outlined three generic IoT agriculture use cases and seven IoT agricultural applications already on the market that are making it possible for farmers and ranchers to gather meaningful data. Additionally, we’ll walk you through five engineering questions you should consider before you finalize your smart agriculture solution.
Thanks to livestock monitoring, ranchers can use wireless IoT applications to gather data regarding the health, well-being, and location of their cattle. This information saves them money in two ways:
There are some specific challenges when instrumenting livestock with sensors. Specifically, it’s quite difficult to outfit cattle with a collar. An alternate option is to use a wireless retrofitted bolus in the cow’s stomach, which can communicate via Bluetooth to an ear tag.
Another potential challenge ranchers face in implementing an IoT solution is selecting a wireless technology with enough battery power to last the lifespan of the animal. A beef cow, for example, lives 15 months or longer—and while some technologies that use a mesh network likely won’t manage that kind of battery life, Symphony Link can easily connect for that length of time without much infrastructure around the ranch to connect all of the devices.
While it doesn’t strictly fall under the heading of “agriculture,” monitoring for endangered rhinos is one of the more interesting animal IoT use cases out there. Knowing where rhinos are located in large game facilities can help conservationists protect them from poachers.
As one may imagine, collaring a rhino isn’t easy—and we’ve found it isn’t often successful. The collars get ripped off during bouts of fighting, and they’ve been known to cause behavioral changes in the rhinos. To overcome these obstacles, we are currently examining the idea of putting Symphony Link devices inside a rhinoceros’s horn.
Monitoring plant and soil conditions is a simple use case—but it can lead to a fantastic return on investment for farmers. We’ve seen several great uses for agriculture IoT in this space:
Because the sensors in all the use cases above are close to the ground, using a mesh network can be difficult. There simply isn’t enough link budget. But star topologies like Symphony Link are an ideal fit, because one access point can talk to a number of sensors 20-100 square kilometers away.
While these generic case studies provide insight into how IoT in agriculture can be useful to the farming community, it’s also important to understand what IoT agriculture projects and applications have already been developed:
Cropx produces hardware and software systems that measure moisture, temperature, and electrical conductivity in the soil. Their system tells farmers when and how much to irrigate.
TempuTech saw a need for increased safety in agricultural storage. Silos and grain elevators can be dangerous places, with conveyor belts that can catch fire and dust buildup that can be explosive. Using sensors to track hazards is of massive value. With GE’s Equipment Insight, TempuTech created a way to connect wireless sensors and help farmers make sense of the data from their silos and grain elevators. Using this platform, manufacturers can establish baseline performance norms and set alert and alarm conditions related to temperature, vibration, humidity, and other conditions.
CLAAS is one of the world’s leading manufacturers of agricultural engineering equipment. Farmers can operate CLAAS equipment on autopilot, receive advice on how to improve crop flow and minimize grain losses, or automatically optimize equipment performance. The company is partnering with 365FarmNet, a program that enables farmers to manage their entire agricultural holding on a computer or mobile device. The system collects data and makes meaningful use of it through field mapping, fertilization planning, nutrient balance, and calendar and planning programs.
PrecisionHawk has created an autonomous UAV that collects high-quality data through a series of sensors that are used for the surveying, mapping, and imaging of agricultural land. It’s essentially a drone that performs in-flight observations and monitoring. Before sending the drone into the air, farmers tell it what field to survey and choose a ground resolution or altitude. Each drone can detect weather conditions using artificial intelligence, so it chooses the best flight path to take based on things like wind speed or air pressure. During the flight, the drone collects visual, thermal, and multispectral imagery; then it lands in the same place it took off. (Now that’s a cool and useful Internet of Things farming tool!)
Radar Family Farms began as a pumpkin farm in the 1990s, and today offers a 10-acre corn maze to visitors every fall. In the early days, the family created the maze by planting all 10 acres of corn and then hiring a company to mow out the shape of the maze. This was a waste of seed—and a waste of money. By using Precision Planting technology, they’re now able to plant in the shape of the map—something they believe no other farm in the U.S. is doing.
Tobacco is a big industry in Italy and requires certain environmental and climatic requirements for optimal growth. In response to this issue, an Italian software company—TeamDev—deployed Libelium’s Waspmote Plug & Sense platform to collect data on weather conditions that may affect tobacco crops. This technique can be used by tobacco farmers to optimize their crops in conditions not typically suitable for tobacco growth.
JMB North America has brought to market an IoT solution that helps ranchers monitor pregnant cows that are preparing to give birth. A battery-powered sensor is expelled from the heifer when its water breaks, which sends a notification to the rancher or herd manager. The sensor allows farmers to be more focused in the time spent with pregnant heifers.
As shown in the examples above, there are a wide variety of IoT agriculture use cases to cover a menagerie of farming and livestock issues. If you’re engineering a smart agriculture solution, there are specific areas of focus to keep in mind while you build out your product.
Farmers, agriculturalists, and industrial food producers alike are looking at IoT solutions to increase efficiencies and yields and reduce loss and theft. In other words, they’re looking to optimize resources and lower costs. Whatever the end user will be monitoring should be front-and-center as you design the application.
For example, a corn farmer may be primarily concerned with water usage. He doesn’t want to use too much water, but he also needs to be sure that enough water is getting where it’s needed. Real-time monitoring, on the other hand, can help a rancher locate a sick cow in the herd before it contaminates the rest of the animals. Doing this will dramatically reduce livestock losses, and reduce costs associated with purchasing antibiotics needed to treat a large group.
The distance that data needs to travel makes a huge impact on what type of technology should be used. If you measure something 10 meters away, you wouldn’t use the same technology you’d use for something 1,500 meters away.
For short distances, you may use radio frequency identification (RFID) or near field communication (NFC), which is common in cell phones. NFC or RFID may be used if you’re tagging a feedbag and need to know how many pounds of soybeans are in each bag.
If you’re sending data to an object 10 meters or closer, Bluetooth or Bluetooth Low Energy (BLE) might be good options. A good example of that would be engineering a Bluetooth ear tag for pigs living in a small area, which would tell an end user the swines’ ages and important information about them.
If your application needs to send data over hundreds or even thousands of meters, you might look at low power, wide-area network (LPWAN) options. A few examples include Symphony Link or other sub-GHz technologies. An application over this type of network might be used to measure soil moisture in fields or to find and track livestock as they graze. This type of application is also ideal for monitoring fish farms that have large, fenced-in areas of aquaculture and are difficult to access.
There is a very close correlation between battery life and range. A sensor that is very far away requires more energy to get information from one point to another. To get around that, IoT product creators often engineer applications to send much less data (or send data less frequently) to save on costs and power.
So, you’ll need to determine where your sensor application will draw power from. Given that most IoT agriculture is typically outside or spread over a large area, you’ll need to consider a low power application. Otherwise, the service and upkeep of many distant sensors will be overwhelming for the end user.
You may think that the more data packets a sensor can send the better, but this isn’t necessarily the case. How many data packets are necessary depends on many different factors, including the end-user application and the local environment.
For example, if a farmer has a moisture sensor in a faraway potato field, he likely doesn’t need to gather information every two seconds. Once or twice a day is probably sufficient, which means the battery life will be far greater.
On the other hand, an application that is used to send GPS coordinates and other information gleaned by a tractor could easily send near-constant data packets back to the gateway. After all, a tractor offers a perfect (and nearly unlimited) power source, so large amounts of data or video streams can be sent without clogging up the network. You can see how this is a lot different than our moisture sensor example, which doesn’t have a constant power source.
Another example is a comparison of tank leveling and irrigation. Many farms have large tanks that house fertilizer, fuel, or livestock feed. Monitoring the levels of these tanks more than once a day is probably unnecessary. On the other hand, when irrigation is on, continual updates can ensure that the right amounts of water are being released and that there isn’t a leak.
All this to say: Before you create an M2M agriculture application, make sure you consider how much data is too much.
Every wireless sensing technology is different, so you’ll want to consider which ones you’re going to use and how you’ll be interfacing with them before you get started. Some sensors—like moisture sensors—are embedded, and require microcontrollers to interface. Creating the sensor and weatherizing it is an engineering challenge that would need to be met.
Positioning the sensor for an optimal communication path is another engineering challenge. If sensors are placed in an orange orchard, the trees may interfere if the antennae aren’t mounted properly. This is obviously not as big of a challenge if an antenna is mounted in a strawberry field.
In conclusion, remember this: The technology you engineer into your IoT agriculture application should not be a hindrance to what the end user is trying to measure. So make sure you understand what your end user needs to measure, then choose a solid technology to build around to get that information out.
Because they are not dependent on third-party WiFi or 3G connections, low power wide-area (LPWA) connectivity options like Symphony Link enjoy greater network reliability and scalability even across a vast farming enterprise. Download the brochure below to learn more about how Symphony Link can help connect your IoT agriculture application. If you have questions, let’s talk.