An IoT farming solution that enables you to do more with less and readies you for the future.
IoT farming solutions will help farmers solve the current and forthcoming crises that are on the horizon.
As climate change drives more and more unpredictable weather patterns, and operational costs increase, it has become much harder to leverage generational practices to produce yield and profitability.
If you then include variable elements like crop type, existing irrigation technologies, and geography (to name but a few) into the mix with a changing environment, then you have a melting pot of increasing complexity that you need to make sense of.
Real-time monitoring and smart irrigation
Choosing the right methodology and devices was critical to designing our IoT Farming Automation, Water Monitoring & Irrigation Optimization solution. We started by choosing the right controllers and sensors which can automatically adjust irrigation logic to suit crop, weather and soil conditions, and then building out from there.
For the solution to be successful, it needed to apply machine-to-machine communication, backed by machine learning applications that would allow for more precise scheduling decisions.
Defining what data to collect is critical
We started by identifying the key metrics necessary in order to achieve the desired farming outcomes. In this solution, these included:
- Weather station data
- Soil moist probe data
- Irrigation system data
- Irrigation schedule
- ERP data
- Weather history and forecast
- Light forecast
- Production schedule
- Nutrition data
- Sales data
Initial goals of the solution
The first goal of the project was to connect the sensors and systems and push the data to the Rayven cloud. As the irrigation was taking place in a area with bad cellular connection, we first had to set up together with one of our communications partners to launch a private LoRa network.
Once in place, we then needed to make sure that we could send all the data via a LoRaWAN gateway, which would then send that data in real-time to the Rayven cloud. This would ensure that all of the data required to be collected would be accurate and recorded in a consistent and reliable manner, thus ensuring data quality, integrity and provide the following features:
- Monitoring and control of the irrigation system via a web-based and mobile system
- Provide device management monitoring for the irrigation system and sensors in the field
- Enable custom business logic to be programmed into the solution so it could meet differing objectives
- Alarm and alert notifications via email or SMS messages when unexpected issues occur
- Diagnose reasons for downtime
- Enable data modelling based on internal and external data sources, such as weather, moisture and light
- ERP and scheduling information
- Forecast of water schedule recommendation
- The ability to test the application, making sure all of the above goals are met, based on the below solution architecture
Before go-live, we tested four critical aspects of the solution:
The Rayven IoT platform is built with security as a top priority and our proprietary security architecture ensures that data is secure at all points of the IoT environment.
The solution includes data encryption in transit from device-to-cloud, as well as device authentication; security (Bearer) tokens; SSL, AES and RSA encryption; as well as additional device security checks done via automated polling.
As irrigation is in a remote area with bad cellular connection, we couldn’t use 4G (or 5G) to collect data. Instead, we had to set up a LoRa network with one of our communications partners and use a Industrial IoT LoRaWAN Gateway with a 11.5 KM range to collect data from the sensors and systems in the field, before then sending the data to the cloud with a 4G Cellular LoRaWAN Gateway.
As we were using data from very different systems, all in remote areas with limited connection, it was critical for us to test the connectivity to minimize the issue of data package lost. We also ran testing of data back-fill, to make sure that if there is a communication issue, we can fill the blanks in data accurately so that there is never a data loss issue that can affect data integrity.
Industrial data science
The objective of exploratory data analysis was to observe trends in the soil, weather and irrigation data, which included:
- Real-time forecasting of evaporation rate based on weather data
- Irrigation forecasting based on evaporation rate
- Forecast water usage based on weather data
- Automatically update irrigation schedule based on forecasting
- Irrigation system predictive maintenance
- Water tank level forecasting
We are continuing to collect data and provide ongoing insights to our customer, as well as continuing to work on improving even the forecasting results even further by utilizing machine learning as the data sets grow over time.
Data sources used to deliver the IoT Farming Automation, Water Monitoring & Irrigation Optimization solution
New and existing data was used to create the custom applications and business insights that come from our IoT Farming Automation, Water Monitoring & Irrigation Optimization solution.
Key features of our IoT Farming Automation, Water Monitoring & Irrigation Optimization solution
Unlike sensor-based controls, our IoT Farming Automation, Water Monitoring & Irrigation Optimization solution relies on weather updates transmitted by radio, telephone or web-based applications to deliver more accurate results.
Weather factors, such as temperature, relative humidity, and light intensity; have a profound effect on the growth of greenhouse crops, for optimal control and growth
Alerts and notifications
Environmental factors have a huge effect on the crops growth, and deviation from their optimal thresholds impact quality. This make alerts and notifications sent via email and SMS when there is a breach of any of these optimal thresholds critical.
Expanded real-time insights
The solution delivers real-time advice based on measurements, so that farmers can make informed decisions on activities. The platform can also be integrated with other systems, such as greenhouse control systems, enabling it to trigger automatic actions on HVAC, lighting, sprinkler and spraying networks.
Using machine learning, sensor real-time data, and historical performance; the solution can forecast when and where crops will need irrigating for optimal performance.
Water tank level forecasting
Our solution uses weather updates from local weather stations to update the ‘evapotranspiration rate’ and forecast future water tank levels, so that you know when to top them up before they run dry.
The combination of sensor measurement and data science also enable farmers to predict when maintenance to assets will be needed based on their usage, maintenance schedule, and performance.
The business outcomes driven by our IoT Farming Automation, Water Monitoring & Irrigation Optimization solution.
Being able to better predict your crop yield and total performance means that you can manage your farm with confidence, reducing unpredictability and allowing you to make better decisions with less risk
Lower cost of maintenance
Real-time insights, alerts and notifications, together with forecasting, enables you to get an accurate snapshot of how your business is performing and where your issues and risk lie.
Lower cost of water
Unlike sensor-based controls, these smart irrigation systems rely on weather updates transmitted by radio, telephone or web-based applications.
With better real-time visibility and predictability, you reduce the chances of issues not being identified that may have irrecoverable consequences, like loss of crop.
Real-time visibility and predictability combined with smart algorithms and machine learning, plus your custom business logic, means you can configure your solution to optimize conditions for maximum yield, and track your performance vs. targets.
The combination of increased yield, reduced waste and energy consumption, and lower cost of maintenance come together contribute to your bottom-line.
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