How an Engineer Used IoT to Save His Avocado Farm
Joan Thompson posted on August 05, 2016 |
Spirent senior solutions manager uses IoT engineering expertise to make a smart farm.
Photograph of Hass avocado on Californian farm. California grows 90 percent of avocados in the U.S. (Image courtesy of Zac Benedict.)

Photograph of Hass avocado on Californian farm. California grows 90 percent of avocados in the U.S. (Image courtesy of Zac Benedict.)

When Kurt Bantle bought a small avocado farm in California, he found himself in a crisis he couldn’t have anticipated: the beginning of a historic four-year-long drought.

Luckily, Bantle happens to be an engineer and an industry expert with products that use the Internet of Things (IoT). As a senior solutions manager at Spirent, Bantle is tasked with developing an IoT network for his company. To test his IoT network application, Bantle decided to use his backyard avocado farm as a “laboratory.” The result was that he cut his water usage by 75 percent.

Small-scale Californian avocado farmers, like Bantle, are faced with a difficult task: ensuring they adequately irrigate their plot amidst high-cost water bills. Avocados are among the most water-consumptive fruit, requiring 74.1 gallons of irrigated water per pound of crop. It is therefore no surprise that many small-scale avocado farmers are unable to sustain their business.

The typical amount of water to irrigate one acre of avocado trees is 4 acre-feet per year. With water costing $1,200 per acre-foot and Bantle’s plot being 12 acres in area, it was clear that the simple act of watering his farm would be a heavy financial burden.

IoT Irrigation Sprinklers Are Only On When They Need to Be

Bantle’s farm of 900 young avocado trees is separated into 22 distinct sections that are irrigated separately. To assess how well these sections are irrigated, each of them contains two soil moisture sensors that Bantle inserted in the ground.

One sensor is positioned near the top soil while the second is planted deeper, past the plants’ roots. The second sensor is used to ensure that the water is seeping into the ground deep enough to wash the salt away from the roots. Each section also has a thermistor to test the temperature. In each soil section, all three devices are wired to a microcontroller, which is paired with a LoRa unit.

From there, narrow band data communication is sent to a LoRa gateway which has broadband cellular uplink connectivity functionality. Within the gateway is an Oasis re-programmable SIM, which enables remote water provisioning.

Every ten minutes, the soil moisture sensors transmit data to the gateway. The data is then collected in a cloud-based software platform where it can be visualized in a presentation layer. Spirent is preparing to commercialize the platform and model it based on the system that Bantle developed.

When the soil moisture data is assessed and determined to be too low, the cloud sends a command back down to the gateway to start spraying water. This command is then relayed to the irrigator’s LoRa-based valve controllers. The sprinklers turn on automatically and the trees are watered until the soil moisture returns to the required levels.

Battery Issues on an IoT Farm

One challenge Bantle faced while making his IoT farm was the battery consumption of his sensors. Before settling on the LoRa gateway, he employed a ZigBee mesh network. Every packet of data was being sent across every node, so managing the network was needlessly difficult.

This always-on and overworked ZigBee network was also the source of Bantle’s battery issue. This goes to show that IoT engineers need to understand the data they are collecting and ensure the data collection is large enough to avoid aliasing but small enough to avoid overloading the system. This is why Bantle decided on his LoRa network-based 10-minute sampling delay.

Farmers of smaller farms typically use local weather stations or CIMIS to determine how much they should water their crops.

Farmers of smaller farms typically use local weather stations or CIMIS to determine how much they should water their crops.

Bantle’s IoT farm introduces an easier way for farmers to decide how much to water their plot. Conventional methods of choosing irrigation quantities include relying on local weather stations, like the California Irrigation Management Information System (CIMIS). The CIMIS calculates evapotranspiration rates for zones across the state which farmers then use to estimate how much to water their crops.

Progress on Pilot Test for Small-Scale IoT Farms

Bantle’s goal was to reduce water consumption by 50 percent. So far, Bantle’s water usage is actually 75 percent less than what it was before he implemented the sensors. This is partly because he has since ripped out some of the older, mature trees and replaced them with new ones, which consume less water. Bantle will have to wait for these trees to grow before he can see if he has met his 50 percent goal.

Nonetheless, seeing a 75 percent reduction in water consumption with these young trees is still a sign that his system is effective.

Using IoT technology to improve irrigation efficiency has been done before, but it is much more common for large-scale operations. Bantle states that he surveyed the marketplace for IoT systems designed for agriculture but couldn’t find anything implemented on small-scale farms.

This pilot test study has also shown that the technology may be financially sustainable. In fact, Bantle received a full return-on-investment after just six months.

To learn more about LoRa networks read: IoT Trends: Low-Power Wide-Area Networks and Standard Consolidation.

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