TinyML unlocks new possibilities for sustainable development technologies

Changing weather patterns, rising sea levels, wildfires, droughts, depletion of natural resources are just some of the long-term consequences of unsustainable human activities. Indiscriminate exploitation of resources is accelerating global warming, and if preventive countermeasures are not taken immediately, the world may end up at a point of no return.

TinyML is a field that aims to bring machine learning (ML) capabilities to low-power embedded systems. Its applications can be found from households to industries and everywhere in between. The idea of sustainability naturally links well with the tinyML as it encourages us to reduce our carbon footprint by executing ML algorithms on low-power devices.

In our previous article about tinyML, we introduced our readers to the basics of the technology and how it is helping shape the embedded systems of the future. This article focuses on two interesting applications of tinyML for sustainable development.

Groundwater Wells: The Untapped Edge Use Case

Farming is the oldest and arguably the most crucial industry in the world. It employs around one-fourth of the world’s population and feeds billions of people. [1] Agriculture is also the largest use of fresh water in the world. Regrettably, it involves the largest wastage of water as well, making it incredibly inefficient. Most of the water used here is unregulated and unmonitored.

Wastage of water has put 20% of the 39 million groundwater wells at the risk of running dry. [2] Once a well dries up, the only possible option is to dig a deeper one, but that can have a much worse long-term impact on the water table.

Niolabs is a digital transformation and Internet of Things (IoT) solutions organisation that leverages IoT and tinyML in precision farming. Over the past couple of years, the organisation has achieved excellent results with the technology. Precision farming is the future of agriculture involving the collection and utilisation of real-time data to control the various irrigation automation systems. The result is lower consumption of water and energy while saving on operating costs. [3]

For the implementation of the precision agriculture model, connecting all the devices to the network and passing data to the cloud is not always feasible, especially if a large number of data sources are involved. With thousands of plants being monitored for various environmental variables, the amount of data being generated is enormous and creates a security concern too.

Using microcontrollers interfaced with moisture sensors and water control valves, it is possible to implement a simple automatic irrigation system that turns the irrigation system on or off depending on a static value of soil moisture levels. But not all plants require the same amount of moisture. Considering the environmental conditions like temperature, humidity, sunlight, and wind speed that have a direct effect on the water requirements of the plant can also be a challenge on the microcontroller programmed with simple if-else statements.

This is where tinyML comes in. By implementing machine learning algorithms on the embedded controllers, it becomes possible to take into account the various environmental variables and dynamically adjust the water supply depending on the plant species and environmental conditions.

The precision agriculture solution by Niolabs uses edge computing to enable each node to decide the amount of water required individually. The system uses an Xbee service that tracks the moisture levels near each watering zone and another service that allows users to provide their inputs about the watering requirements. 

Both the inputs and several other variables are combined and used as a reference to train the irrigation model, which keeps learning and improving with time—proof enough of the fact that combining general knowledge with ML can do wonders.

The one thing that makes Niolabs’s implementation of a precision agriculture system unique is its ability to combine millions of seemingly unrelated data points into insightful and straightforward metrics, which can be accessed as a summary or detailed view at the fingertips of the users. The same system that maintains the plantation can also help with predictive maintenance. By using condition monitoring techniques, it can tell when its components might fail.

Niolabs aims to come up with a revamped architecture of their application before March 2022. This is when the harvesting season begins, a perfect time to deploy and test the latest solutions. The plan is to create a repeatable tinyML solution that can be transferred to other farms and be scaled for use in a variety of different environments.

Ribbit Network: The world’s largest crowdsourced network of open-source, low-cost, Carbon Dioxide Gas Detection Sensors

If we take a look at the long-term trend of worldwide greenhouse gas emissions, it is easy to note that the figures have shown constant growth in the past three decades [4]. There is a massive uptrend that continues to get steeper with each passing year. To make informed decisions and new policies regarding climate change, stakeholders and government organisations need accurate data.

It is easy to assume that the data that is used presently is sufficient to make critical decisions, but that is not the case. The reference estimations are mostly theoretical calculations or predictions based on indirect economic measures. For example, vehicular emissions are roughly calculated based on the overall kilometers traveled and average emission per vehicle. 

The measurements that are direct, such as the ones taken by climate tech satellites, prove to be exorbitantly expensive. A single Orbiting Carbon Observatory 2 (OCO2) satellite that makes space-based observations of atmospheric carbon dioxide cost NASA over $467 mn. [5]

Ribbit network aims to fill this gap by combining the economics with actual observations to reduce the uncertainties in data collection. Ribbit network takes over the challenge of building the map of emissions and concentration of carbon in the air with the help of tiny and affordable IoT sensors. They call them the ‘Frog’ sensors. The sensors get their name from frogs as these amphibians are hypersensitive to temperature and are the first ones to get affected due to climate change.

Our primary interest in TinyML is based on decreasing power consumption. Running as much computing as we can on our IoT devices lets us minimize the carbon footprint that comes with big data center services. - Keenan Johnson

The Frog sensors use a Non-Destructive Infra-Red (NDIR) sensor working on the principle of infrared (IR) spectroscopy. A typical NDIR sensor consists of an IR source, sample chamber and an IR detector. For measuring the presence of CO2, the air is allowed to flow inside the sample chamber. Depending on the concentration of CO2, some IR waves of a particular wavelength emitted by the source get absorbed. The difference between the emitted light and the light that makes it through the chamber gives conclusive evidence of the presence and quantitative estimation of the greenhouse gas in the air.

The sensor is interfaced with a Raspberry Pi CM4, which takes care of everything right from powering the sensor to collecting and transmitting the data.

The design of the frog sensors is entirely open-source which means, anyone can build their own version of the device by following the official tutorial from GitHub. 

Twenty-five alpha versions of frog sensors have been deployed across five countries. Beta versions will be deployed in a much larger number before the final rollout. The team is currently striving hard to solve problems related to determining sensor health, calibrations, the inclusion of wind data, and a lot more. With custom engineering solutions, they wish to reduce the cost of the sensor and power it with solar cells.

Conclusion

It is not one, two, or three solutions that are going to solve the problem of climate change, but hundreds and thousands of innovative ideas collectively working together that can bring the revolution. That is what tinyML and its community members stand for. 

Grassroots innovations like the ones we showcased in this article can go long miles in meeting the global sustainable development goals, making the world a better place to live for future generations. Over the next couple of years, we expect tinyML to be leveraged far beyond than presently is.

About tinyML Foundation

With its workshops, webinars, expert talks, research conferences, competitions, and a plethora of other events, tinyML Foundation enables students, researchers, and industry personnel to come together and share their experiences about the technology. It is the incredibly collaborative nature of the community that allows it to advance rapidly.

The introductory article familiarised the readers with the concept of tinyML.

The first article explained some possibilities of tinyML applications in sustainable technologies.

The second article is about tinyML applications aiding healthcare and medical research.

The third and final article shall cover the tinyML applications in pedagogy and education.

References

[1] The World Bank Data, Employment in agriculture (% of total employment) (modelled ILO estimate), 2021, [Online], Available from: https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS

[2] Harrison Tasoff, Gauging Groundwater, 23 April 2021, [Online], Available from:  https://www.news.ucsb.edu/2021/020248/gauging-groundwater

[3] Niolabs, Nio powers the only autonomous vineyard in the world, [Online], Available from: https://niolabs.com/case-studies/agriculture

[4] United States Environment Protection Agency, Global Greenhouse Gas Emissions Data, [Online], Available from: https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data

[5] NASA, Orbiting Carbon Observatory-2 Launch Press Kit, July 2014, [Online], Available from: https://www.jpl.nasa.gov/news/press_kits/oco2-launch-press-kit.pdf