Open Source Orchard

1. LoRaWAN Gateway Deployment 

To determine the optimal implementation of a LoRaWAN gateway (or multiple) in a commercial orchard setting, our initial assumption was to continue with our approach from the summer of 2022 where we set up a WisGate Edge Pro gateway on a tripod next to our shop, and simply move the entire unit on top of our roof to ensure greater farm-wide coverage. However, we had issues scheduling anyone who would be able to (safely) get on the 30' high roof to fasten the tripod down and we quickly realized if there was ever a need to provide maintenance to the gateway, it would be virtually impossible. We then began to reevaluate the overall agrosecurity of the system, and started by addressing questions such as "If something were to happen to a gateway, how easy would it be to replace that gateway, continue sensor data collection, and get everything back up and running?" Our new shop office was recently completed in the summer of 2024 and in the process, we installed a WisGate Edge Pro (V2) to the inside of the attic and routed a 12’ cable to an 8 dBi antenna mounted on the roof, with the base of the antenna 6’ (2 times the length of the antenna) above the top of the metal roof. The gateway inside has an Ethernet cable routed through the celling to an office room to a PoE injector to provide power, and another Ethernet cable runs from the injector to a Wi-Fi router. We began range testing by connecting four soil moisture sensors to Chirpstack and moved them incrementally further from the gateway location, collecting data on the SNR (Signal Noise Ratio), RSSI (Received Signal Strength Indication), and time intervals of sensor communication. The gateway was able to cover the entire farm, however there was significant signal loss beyond a locust grove that divides a section of our farm. The sensor just in front of the locust grove recorded an average RSSI of -110.915 dBm and average SNR of -2.795 dB for the month of July while the sensor just beyond the locust grove recorded an average RSSI of -113.07 dBm and average SNR of -12.649 dB, showing a notable degradation. This suggests that deploying an additional cellular-connected LoRaWAN gateway on a tripod beyond the locust grove may be necessary to strengthen reliable coverage, and can bolster resiliency of the system through working in a cluster. In the event one gateway is knocked out, sensor signals can still be picked up by other gateways within the network, allowing for data collection to continue even in worse-case scenarios. In addition, this means the second gateway on a tripod can be taken in during winter months when data collection isn't as necessary as during the growing season. For the server to manage the data pipeline, we've switched from a Raspberry Pi-based physical computer to cloud-based computing (virtual machine) via a managed service provider. This concept came from the question "How could a farm respond to cybersecurity threats and ensure operations can continue to run smoothly especially if automated devices are in use?" Using cloud computing instead of physical computing will mean maintenance of a separate computer is not necessary and farms will not be responsible for establishing intense cybersecurity measures on their own (the Raspberry Pi could be hacked if left unmaintained). If cybersecurity issues do occur, the managed service provider will act as a deterrent and work on the farm's behalf. The full IoT data pipeline, including Chirpstack (the web-interface to manage the gateway and all devices connected to it), will reside in the cloud computing space for an affordable cost.

2. Irrigation Management 

Three “Drop Count” sensor systems were constructed during the summer of 2023 and the design involved a Watermark Soil Moisture Sensor (acting as a soil potential sensor) with a 15’ cable, Watermark Soil Moisture Sensor Voltage Adapter in a plastic utility box, Vegetronix Soil Moisture Sensor with a 2-meter cable, Watermark Soil Temperature Sensor with a 15’ cable, Dragino Waterproof Long Range Wireless LoRa Sensor Node, and a wooden stake to prop the system vertically. The systems were successfully assembled but proved to be tedious to assemble and far too bulky, especially since a 2x4 was needed to hold everything upright. Last winter, we initially considered a leaner design that included a Tektellic KIWI Agriculture Sensor, 2 Watermark Soil Moisture Sensors, and 2 soil temperature sensors. One pair of soil moisture sensors and soil temperature sensors would have been placed about 6" deep and the second pair about 18"-24" deep, the latter of which was suggested by a Cornell Cooperative Extension agent. The idea was to not only monitor surface level soil moisture, but also soil moisture at the depth of roots as well. However, this system still would’ve required a degree of custom work and the use of 2 schedule 13.5 PVC pipes to ensure cables aren't exposed and mice can't chew on them, which might be more difficult to deploy. We also found out the Watermark Soil Moisture Sensors have a lifespan of only 2 years, and we’d prefer a lifespan of around 5. Instead, we’ve decided for now to stick with Tektellic Clover Agricultural Sensors given our experience with them collaborating with colleagues at Purdue, which are surface level sensors that collect data on soil moisture, soil temperature, ambient humidity, and ambient temperature or light intensity with battery lifespans of 5-10 years. We’ve found the sensors extremely easy to configure, deploy, and while the soil data is only an average of the first 6”, it still has proven useful for irrigation-type decisions, especially for the low cost. At Purdue, we’ve also used GroPoint Profile sensors, which collect soil moisture and soil temperature data at multiple depths. We connected Dragino Waterproof Long Range Wireless LoRa Sensor Nodes to their tops to report data and deployed the units with stakes. GroPoint sensors are a bit more expensive, however the company allows for custom depths and distances between sensors to be specified, so it could be possible to request units that only have the strictly necessary depths for the application, instead of, say, a 4’ unit with sensors every 6”. While the Tektellic Clover Agricultural Sensors have been primarily used for range testing to date, evaluation is still being done on the implementation of the soil moisture sensor system, such as positioning relative to the apple varieties in irrigation zones, and so far, soil and topography maps have been generated using the USDA NRCS Web Soil Survey online tool to analyze consistency. We plan to use 1-2 sensor systems per zone so that we can deploy 8-16 on the 8 irrigation zones on the south portion of our farm and use additional sensors to test on zones further away to see how all established devices of varying ranges are able to communicate with our gateway(s) for potential expansion. We’ve also had conversations with companies and Cornell researchers about direct tree sensing, giving us some food for thought.

3. Irrigation Automation 

To automate turning irrigation zones on/off, our plan is to install Strega LoRaWAN Smart Valves with 10' cables for each submain to control remotely from anywhere. We successfully acquired 10 smart valves in the summer of 2023 but faced great difficulty in doing so. Because 10 smart valves was a small order for Strega, they had us order the valves through their Chinese partner and the LoRaWAN transmitters through them. There were no issues when paying the Chinese company for the valves, but currency conversion issues came up when making bank transactions to Strega, a Belgian-based company. Then, the smart valves were packaged and shipped in a single box to us, but got stuck in customs in Memphis and an additional payment was required to finally get the package to us in NY. After receiving the smart valves, we were notified we'd need to make a firmware upgrade. Fortunately, the added wait gave us time to think more about the configuration of our submain boxes, which have been wooden to date and are starting to fall apart. As part of an expansion project on another section of our farm, concrete submain boxes are being implemented as a more structurally sound alternative, and now we're planning on replacing our existing submain boxes with the same material. This will also allow for a more stable smart valve deployment and mitigate against flooding and damage concerns. When implementing, the valve portion of the smart valve will replace the current manual valve at a pipe diameter size of DN50 or 2” and is high pressure rated at PN20. The wire to the actuator (the control and transmission portion) will be run in 10’ schedule 40 PVC pipe up the nearest row post and secured with the end of the pipe to the post. This will ensure data transmission/communication between the gateway and smart valve can be kept above ground and interference won’t occur with the zone box lid. In the summer of 2024, we successfully installed a new concrete zone box and smart valve in the irrigation zone closest to our gateway. A slight modification was made for the setup where an additional row post was added and the transmitter was fastened to the post facing inside the row to mitigate the risk of a tractor accidentally hitting it. The smart valve was turned on/off during our irrigation season through the use of a TagoIO dashboard for simplicity in testing since TagoIO (an IoT dashboard company) had an existing integration with Strega smart valves. Once the free data limit was reached, the smart valve’s physical backup mechanism was tested successfully for the tail end of the irrigation season to continue watering the zone. Similar to the soil moisture sensor systems, we will expand to the other 7 zones on the south end of the farm and then build more out based on results. Evaluations will be made during the “irrigation season” (June/July/August when irrigation is needed most) on how well the system works, which will involve the use of the soil moisture sensors to do so, especially as more acreage is covered.

4. Weather Data Collection 

To study weather differences near and away from Lake Ontario, we’re planning on doing a weather data study with a KestrelMet 6000 Weather Station. From talks with fellow growers, this weather station was recommended to us as the best option compatible with the NEWA system. Since the gateway is connected via Wi-Fi or cellular and there are certain constraints about where the weather station can be mounted/placed, we will have further discussions with Cornell extension and IPM researchers to flush out specifics. From there, we will work with 2-3 other farms with already-established weather stations in the NEWA system to compare weather patterns over a period of time. Data collected will be used to correlate with the soil moisture sensor systems and make irrigation decisions utilizing the smart valves. Concerns earlier in the summer of 2023 arose when it appeared Davis weather stations didn't have a supportive LoRaWAN integration, and individual components for the only LoRaWAN-specific option we could find were ordered from a company based in China. However, once we acquired everything, we found out that we had to source the tripod mounting brackets ourselves, as the company in China only worked with local distributors for those parts. There weren’t any distributors in the US or anywhere else as far as we could tell, so that left us with the option of modifying scrap metal ourselves or trying to find an alternative. We considered the Davis Vantage Pro2 Weather Station as well since it’s widely used in the agricultural industry, however we felt that the NEWA integration provided us with an additional added value, leading us to favor weather stations compatible with this.

5. Asset Tracking 

With our LoRaWAN infrastructure, we plan on testing multiple Digital Matter Oyster LoRaWAN GPS Trackers on select pieces of equipment and observing how well multiple oysters maintain connection with our gateway while moving around high-density orchard trees, and if one drives out of range of the gateway, how well the LoRaWAN system can pick up on broadcasted messages once in range again. Case studies will be built out especially during equipment-heavy seasons, such as spray season and harvest, where we’ll apply oysters onto tractors and harvest platforms to show the advantages of seeing progress from anywhere. Additionally, we’ll have the ability to view paths taken by equipment historically, so we can refer back to progress made before on specific days. We have acquired 3-4 Oyster3 trackers from a US supplier and will ensure proper configuration before testing.

6. Fuel Tank Monitoring 

The specific fuel tank level sensors to be used are still being determined, but two options that have been evaluated are Dragino’s LDDS20 LoRaWAN Liquid Level Sensor and IOT Factory’s Fuel-Water LoRaWAN Sensor (Ultrasonic). The issue we faced with the Dragino sensor was it requires a slightly flat surface at the bottom of a fuel tank, and in our case our tanks are perfect cylinders. For the IOT Factory sensor (and in general), we require LoRa products that can operate at the US frequency level (US915), but unfortunately IOT Factory doesn’t offer the US frequency for this specific product. Once we source a sufficient fuel tank sensor, we will likely insert it from one of the screwable top pieces and from there it will be able to record data on the amount of fuel in the tank. We discussed options with a former extension agent from North Dakota State University who has published extension reports on LoRaWAN implementation on farms, however the recommended products couldn’t be used for our scenario, so we plan to follow-up on that conversation to look into alternative options. We plan to have further discussions and continue searching for compatible fuel tank level sensors that meet our needs. Fuel tank monitoring can help with refill scheduling which can be especially important during busy seasons.

7. Dashboard Integration 

By utilizing the free, open source dashboard Grafana, we will synthesize all data points collected to one location for ease of use for farmers, meaning all soil moisture sensor systems will be displayable as graphs live and over time, fuel tanks will show their levels, and the locations of vehicles will be shown on a map, for example, all in the same place. Development work will be done to make the dashboard as efficient as possible, and we will receive input directly from the managers of KC Bailey Orchards, Inc. on improvements to consider. In the summer of 2024, an iteration of the Purdue OATS Center’s custom data pipeline, Avena, was implemented halfway for initial testing of a cloud-based data management system. Accessible from anywhere, the data pipeline components streamline data flow and minimize cost for a cloud-based system. Chirpstack was primarily used so far within Avena to connect the gateway, the first smart valve, and four soil moisture sensors and then monitor the quality of data communications. We will continue building out the rest of the data pipeline components to be able to visualize data collected.

Because of the nature of our project, experimentation and testing of each objective will be frequent and throughout the development process, reflective of a true R&D phase. To make the system more resilient, we started with our focus on the closest block of orchard to our gateway before expanding and testing the system over longer periods to observe data collection and infrastructural stability, while learning how to adapt our systems to the weather and environment of the Northeast. Economic impact analyses will also be conducted to show benefits of each technology to farmers throughout (see Other Relevant Research Information).