Rob Tiffany #Digital Podcast 13

#Web3 Explained

Dr. Gavin Wood coined the term, “web3” back in 2014 because he thought the current Web and Internet was fundamentally broken and he wanted fix it with blockchain technologies like Ethereum. Control over identity, financial transactions, and personal data had become centralized and dominated by a handful of powerful web platforms. You can think of web3 as a “Power to the People” movement.

Daring Greatly in 2021

A small group of innovators spent 2021 in the Arena addressing Agriculture 4.0 issues including a rapidly growing population, the scarcity of natural resources, climate change, and food waste using 5G and Industrial Internet of Things technologies.

Let’s start by looking at the problems:

With Earth’s population nearing 10 billion by 2050, growers must double food production to keep up. In other words, growers must produce more food than has been produced since the beginning of farming.

It turns out there are a few headwinds that make this seemingly impossible task even more daunting.

Climate change takes credit for an unprecedented heat wave during the summer of 2021 that wiped out or severely diminished many varieties of fruits and vegetables. Cherries, raspberries, blueberries, and blackberries shriveled-up or burned, putting family farms at risk. Apples and wine grapes stopped growing leading to a smaller harvest. Internal cellular damage to certain fruits and vegetables will carry over into next year’s growing season leading to additional small harvests. Unrelenting floods drove damaging fungal growth. Warmer winters confuse budding plants, while late spring frost can kill those that started growing too early.

Extreme heat and flooding leads to less food.

Most of the Western United States is experiencing extreme drought due to the lack of rainfall combined with evaporative heat, as the Colorado river struggles to deliver enough water to the 40 million people that depend on it. America’s two biggest reservoirs, Lake Mead and Powell, are now at historically low water levels. Since agriculture uses 70% of all freshwater, we find ourselves in a desperate situation.

Reduced water leads to less food.

The Earth is losing almost 30 million acres of arable land each year due to a phenomenon known as desertification. In fact, We’ve lost 33% of all arable land over the last 40 years. Primary culprits include urbanization and deforestation along with farming and ranching that lead to overcultivation, overcropping, and overgrazing. Soil is eroding and turning into lifeless dirt due to drought and poor farming practices such as tilling which depletes soil nutrients.

Less land leads to less food.

Exceptionally dry forests combined with a variety of ignition sources has led to widespread fires. Sadly, the term, “Fire Season” is now a thing. Smoke threatens people and livestock, while fire threatens agricultural lands.

Burned land leads to less food.

A workforce is required for planting, maintaining, and harvesting fruits and vegetables as well as operating and maintaining farm equipment. The current labor shortage is at a crisis level with growers losing crops and income as fresh produce is left rotting in the field because there aren’t enough workers for harvest.

A reduced workforce leads to less food.

Energy usage accounts for roughly 15% of total farm expenditures and comes from operating farm machinery, trucks, processing warehouses, tractors, irrigation pumps, HVAC, ATVs, crop dryers, packing houses, and cold storage. As costs for electricity, gasoline, propane, and diesel increase, grower operating margins decrease making it harder to stay in business.

High energy costs lead to less food.

Between 30 and 40 percent of food, about 1.3 billion tons, is wasted after harvest every year due to disruptions in the supply chain. This disruption creates gaps between production and distribution leading to the loss of perishable items like eggs, milk, and produce in the face of unprecedented need at food banks. Improper temperatures at different parts of the cold chain lead to reduced quality or complete food loss. Lean, just-in-time supply chains, based on buyer behavior leave no room for error.

Supply chain disruptions lead to less food.

Let’s look at the solution we worked on in 2021:

While many of you know about Industrial IoT, Digital Twins, and Industry 4.0, you may not have heard about Agriculture 4.0. I spent 2021 with partners Courtney Latta and Doug Boling building technology that implemented the same kind of IIoT capabilities used in a Smart Factory to facilitate sustainable Agriculture.

As a refresher, the Internet of Things uses devices, sensors, and connectivity to allow you to remotely know the state, performance, or health of an object in real-time. In our case, we needed apples, hops, soil, air, equipment, and water to talk to us and lets us know how they’re doing. With that data and a little bit of analytics, we could help growers make more informed farming decisions to drive desired outcomes such as:

  • Reduced water usage (save money and environment)
  • Increased crop quality (make money)
  • Reduced energy usage (save money and environment)
  • Reduced fertilizer usage (save money and environment)
  • Increased crop yields (make money)
  • Reduced pesticide usage (save money and environment)
  • Reduced labor costs (save money)
  • Improved food traceability (reduce food waste)
  • Increased crop protection from frost, heat, or disease (make money)
  • Increased worker safety (employee well being)
  • Reduced herbicide usage (save money and environment)
  • Increased automation (save money)
  • Increased equipment uptime (save money and make money)

What did we do during the growing season?

We combined battery-powered devices and sensors measuring soil moisture, temperature, air quality, humidity, location, and others with 5G cellular connectivity and our portable Software as a Service (SaaS) platform that can run in the Cloud or at the Edge. Our goal was to validate the MVP of our product. During pilots and trials on large farms in Eastern Washington, we deployed devices and sensors throughout acres of hops and apples. At regular intervals, those devices wirelessly transmitted their telemetry data to our platform. Sometimes we analyzed the captured data with our analytics, and other times our platform routed the data to the grower’s analytics systems to derive insights.

What was the outcome?

In the end, Courtney, Doug, and I found our product/market fit and validated our product with happy customers who willingly signed letters of intent. Heck, we even won the “IoT Innovation of the Year for Agriculture” award from Compass Intelligence. I got to wear a cowboy hat and boots and spent the summer of 2021 amongst trees, crops, and soil. Obviously, we barely scratched the surface in the value the technology we built can provide. We also have no illusions that the precision agriculture we delivered can solve all the problems faced by growers and society at large. Battling climate change, drought, fires, desertification of soil, floods, and food waste is an “all hands on deck” calling for everyone. While we are only part of the solution, the satisfaction and fulfillment we felt this year is immeasurable, and we’re happy to play our part.

The Future of Agriculture is the Future of Humanity

Telemetry Properties of a Digital Twin Model

Telemetry represents the data flowing from a physical twin to a #digitaltwin along with the associated properties that define the data points.

Data Points

Telemetry properties are the dynamic properties of a digital twin model containing values that can change often. For every data point sent from a sensor, tag, or other data source representing a physical twin, a corresponding telemetry property must be defined for the digital twin model. It starts with a human-readable, friendly name that aligns with the data point that makes sense for people and analytics. Something like temperature or humidity for example. In the event that the data points or tags use something unintelligible like T1 or H2, you must also define an unfriendly name that will be translated to the friendly equivalent.

Next up, you must assign a data type and unit of measure to the telemetry property. The data type could be a string, a whole number like an integer, a Boolean (true/false), or floating point number. The unit of measure could be acceleration or pounds per square inch (PSI) of air pressure in a car tire. Assigning data types and units of measure enable conditional logic operations to be performed.

All the telemetry property elements that comprise a digital twin model are inherited by appropriate digital twin instances and tell the software agents in your platform what to expect from incoming data. This facilitates pattern matching.

Data Format

Last but not least, the format that contains all the data points transmitted from the physical twin must be defined. Whether the data is streamed across as JSON, XML, Binary, CSV, Avro, Protobuf, or MessagePack, the platform ingesting the data must know how to parse it.


For every telemetry property you define, there’s a good chance you know in advance what a good or proper data value should be. For instance, when you define the RightFrontTire telemetry property of your car with an integer data type and PSI unit of measure, you might know that 35 is the recommended pressure for your tire. You can therefore define key performance indicators (KPIs) ranges for each of the properties. Green is good. Yellow is a warning. Red is dangerous. A range from 34 to 36 might be green whereas a range from 31 to 33 or 37 to 39 might be yellow. Anything higher or lower than those ranges could be red. The software agents in your platform will look at the incoming data points and compare those values to the KPIs defined for the corresponding telemetry properties and fire the appropriate event for green, yellow, or red to deliver an insight or take an action. The use of KPIs tied to each telemetry property is optional and represents the simplest form of analytics. Those of you in manufacturing will note this is similar to defining thresholds and limits for machine operations.

Prescriptive Analytics

If you choose to define KPIs for your digital twin model properties, you also have the option to define what should be done for respective green, yellow, and red events. This is called prescriptive analytics and clarifies one or more actions to take. Using the tire pressure example, no action is taken for a green event, whereas a yellow event would tell the driver of the car to add or remove a small amount of air from the tire. A dangerous red event would tell the driver to stop their car immediately and change the tire. Since you can define a list of prescriptive actions to take for each KPI event, an additional action to take for the red event might instruct the driver to call a tow truck if the car doesn’t have a spare tire.

More to Come

Follow along with me as I take you on a deep dive of all the elements that come together to make a digital twin. Click links below to catch up with other articles in the series: