Modern image recognition is becoming more and more prevalent in agriculture and enables at least partially autonomous machine use in many fields. However, this requires powerful cameras or sensors combined with intelligent algorithms.
With its IC-Weeder AI, Lemken has already launched a hoeing machine that reliably distinguishes between sugar beet plants 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 shareholding. Lemken is therefore seizing the opportunity to develop the technologies of the future in-house, in collaboration with a competent partner.
The intensive cooperation with Track32, a company based at Ede, in The Netherlands, has already proven highly successful in the development of camera-controlled hoeing machines. The managing director of the Lemken Group, Anthony van der Ley, 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,” he said. “For Track32, our cooperation delivers planning security and great potential for growth.”
Track 32’s founder, Joris IJsselmuiden, said that as a company that specialises in software and artificial intelligence, it also develops solutions for arable farming and greenhouse processes.
“With Lemken as an investor and client, we’ll be able to concentrate even more on the further development of our software and will benefit from closer proximity to end customers,” he added.
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.
