Proofminder, a Budapest-based agtech computer vision startup offering AI-supported leaf-level farm monitoring, has secured its first VC investment of €400k from Hungarian social innovation investor, Impact Ventures, the founders announced. The investment marks the end of a one and a half year long bootstrapping period and opens the opportunity of going global both in partner cooperations and client-related activities.
Founders highlight the funding will be mostly used to further strengthen the team behind Proofminder, deliver on the committed orders of 2023 and introduce new functions to the platform to make it even more widely available and impactful for farmers.
“While agriculture is facing global challenges, the solution can inherently be only local. AI has proved itself to be a transformative technology in many different fields, now it’s our turn to leverage its power and enable growers to make farming decisions on plant and leaf levels. The possible applications of our platform are almost unlimited in agriculture, and we’re already genuinely honored by the constant requests we’re getting from growers worldwide to help count, measure and understand their plants (be it corn, olive grove, avocado or else) and to provide them quick innovative and sustainable solutions”, says Ambrus Vancsó, CEO and co-founder of Proofminder.
The importance of the product Proofminder offers comes at a moment when there is an ever-growing demand for food supplies which imposes a heavy burden on agriculture all over the world. On the other hand, quality plays as much of an important role as quantity in this equation, that’s why the EU’s Farm to Fork strategy leads the way in regulating the reduction of chemicals used by farmers. Adapting to these strict rules requires the latest technological innovations accessible on the market. WIth its AI-powered computer vision platform, Proofminder provides a solution for leaf-level monitoring and enables growers to meet production goals sustainably.
Currently, widespread solutions in precision agriculture are mainly based on satellite images and cannot address challenges that require a closer look at plants, hence high-resolution imagery. By creating a SaaS platform, Proofminder puts an easy-to-use solution right into the hands of farmers. Agricultural professionals can access an almost unlimited number of use cases with a click of a button: plant stand count and distancing, crop monitoring, plant disease or weed detection, insect damage analysis, weather and wildlife damage analysis and many more.
“We still see a huge untapped potential in the use of AI models in Agriculture and Proofminder has built outstanding competence in a key area toward a sustainable future. Compared to the early stage they are currently in, they have developed a really mature solution and did a thorough pivotal stage to gain on-field experience and insights from farmers. Based on their already impressive clientele and growing track record we expect significant growth and social impact from the team” – says Károly Fehérgyarmati-Tóth, Partner at Impact Ventures.
Established in 2021 by three data scientists, Ambrus Vancsó, Norbert Havas and Levente Simon, Proofminder has built a strong international and local clientele with companies like MAS Seeds and Bonafarm Group and has already covered over 1000ha by involving partners like drone operators in the monitoring process. Based on their project mentored by Corteva Agriscience, the company has already been awarded as ‘Most Innovative Agri Startup of 2021’ by the Hungarian Chamber of Agriculture and shortlisted among the TOP3 ‘Agri Innovation of 2021 and 2022 Globally’ by AgriInvestor.com.
After successfully finding the product-market fit, the year 2023 marks the beginning of the scale-up process for Proofminder. Currently the team of 8 plans to multiply the annually covered arable land by 10, covering diverse plant types like for example field crops, orchards and trees. As the company is operating under a revenue model based on processed area, their plans indicate a 10x growth in revenue.
