Modern image recognition which requires powerful cameras or sensors combined with intelligent algorithms is becoming more and more prevalent in agriculture, thus enabling at least partially autonomous machine use in many fields
With its IC-Weeder AI, LEMKEN has already launched a hoeing machine which reliably distinguishes between sugar beets and weeds, even under high weed pressure. The relevant software was developed by the Dutch AgTech specialist Track32, in which LEMKEN has now acquired a holding. LEMKEN is therefore seizing the opportunity to develop technologies of the future in-house in collaboration with a competent partner.
The intensive cooperation with Track32, a company based in Ede, Netherlands has already proven highly successful in the development of camera-controlled hoeing machines. Anthony van der Ley, managing director of the LEMKEN Group, is therefore pleased that the current investment establishes a sound basis for continued cooperation in the future. “This will accelerate the development of smart technologies and ensures continuity. For Track32, our cooperation delivers planning security and great potential for growth,” said Anthony.
Track 32’s founder Joris IJsselmuiden explained, “As a company that specialises in software and artificial intelligence, we also develop solutions for arable farming and greenhouse processes. With LEMKEN as an investor and client, we will be able to concentrate even more on the further development of our software and will benefit from closer proximity to end customers.”
For LEMKEN, this investment also offers additional benefits besides great market potential, namely in terms of sustainability: the agricultural machinery specialist is confident that machines equipped with this technology will make a major contribution to regenerative agriculture. Track32’s expertise will allow LEMKEN implements to be used even more precisely and in more versatile ways, so that intelligent machines can be controlled not only by human operators, but also by robots, for example. LEMKEN and Track32 are currently collaborating on a number of joint projects to achieve this objective.
