EarthOptics, the US based carbon mapping technology firm, has closed its Series A investment round, raising an additional US$10.3 million in funding. The new investment will enable further growth and expansion of its patent-pending soil mapping and machine learning technologies across more farms.
EarthOptics aims to give farmers the most accurate view of their soil’s health, compaction and carbon content by transforming soil measurement and analysis methods. The company claims its technology will provide farmers with a greater return on investment as well as promote more active participation in the carbon market whilst increasing farmers’ abilities to generate a climate-positive impact.
“Scientists estimate that farm soils could store over 60 billion tons of additional carbon,” explains EarthOptics CEO Lars Dyrud. “EarthOptics’ machine-learning technologies will be at the centre of helping farmers gain control over returning carbon to the soil by accurately mapping both soil carbon and agricultural practices like tillage. We need to know what’s working and where it’s working for sustainability efforts to be successful at scale. Using the combination of GroundOwl™ and C-Mapper™, we can dramatically reduce the number of costly lab-tested soil samples necessary for verifying carbon credits.”
Ground-penetrating radar and electromagnetic induction
EarthOptics’ GroundOwl sensor suite is a mounted soil sampling tool which deploys ground-penetrating radar and electromagnetic induction sensors with no soil contact or speed limitation. Using machine learning in its mapping applications TillMapper™ and C-Mapper, EarthOptics converts the gathered data into a series of maps that provide farmers with highly detailed information for more accurate, efficient decision-making. These innovations serve as the foundation for the SoilCloud™ EarthOptics is building, which the company says will lead to fewer and fewer samples needing to be collected over time.
TillMapper’s three-dimensional view of soil compaction helps farmers decide if, when, where and how deep to apply tillage. The tillage prescription can be exported as a variable-depth tillage or a binary till/no-till. By tilling only where needed, farmers realise cost-savings in fuel, equipment and labour, as well as limit the amount of carbon released from the soil through tillage. C-Mapper gives producers critical verification at a fraction of the cost of traditional soil core methods so they can market carbon credits via agriculture’s newest commodity market.
Leaps by Bayer leads Series A
Lead investor in the Series A funding round, Leaps by Bayer (the impact investment arm of Bayer) was joined by new investors S2G Ventures and previous investors FHB Ventures, Middleland Capital’s VTC Ventures and Route 66 Ventures.
Jürgen Eckhardt, Head of Leaps by Bayer, said “Leaps by Bayer invests in climate-smart solutions that are bringing a fresh perspective and breakthrough technologies to agriculture and allows farmers to contribute to solving global problems.”
Cristina Rohr, principal of investments at S2G Ventures recognised the cost-reducing opportunity that EarthOptics’ technology brings to the marketplace, saying. “We are excited to partner with EarthOptics as it builds the first-of-its-kind soil-mapping technology that provides the accuracy of traditional soil samples at a lower cost and effort,” she continued “With EarthOptics’ technology, farmers can measure and verify carbon sequestration, and optimize water and fertiliser usage. We look forward to partnering with this exceptional team as they scale commercially and give more farmers insights to achieve their sustainability goals.”
In March, at the World Agri-Tech Summit 2021, EarthOptics was crowned winner of the AGCO Innovation Challenge from more than 100 international entries, specifically for New Sensing, Measurement And Edge Computing Technologies To Monetize Soil Insight And Carbon Sequestration. EarthOptics is also exploring soil moisture mapping and soil nutrient fertility mapping, which would provide farmers with a single source for multiple levels of field data.