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Naware’s Chemical-Free Weed Killer Tech Could Transform Lawn Care

Originally published on: December 26, 2025
▼ Summary

– Naware’s founder, Mark Boysen, initially experimented with a laser to kill weeds but abandoned it due to fire risk, later developing a system that uses steam (vaporized water) instead.
– The company’s technology employs computer vision and AI, trained on Nvidia GPUs, to identify weeds in real-time, overcoming the challenge of distinguishing them in “green-on-green” environments.
– The steam system can be attached to various vehicles like mowers or tractors and is targeting commercial customers such as athletic field and golf course maintenance companies.
– Boysen claims the chemical-free method can save these customers significant money, between $100,000 to $250,000 on chemicals and additional labor costs.
– For Naware to succeed, Boysen identifies three key needs: securing strategic partnerships with large equipment manufacturers, obtaining patents, and completing a successful funding round, which he plans to launch soon.

Finding an effective, environmentally friendly method for weed control is a significant challenge for the landscaping and agricultural industries. Naware, a startup founded by Mark Boysen, is tackling this problem with a novel system that uses steam to eliminate weeds without any chemicals. The inspiration came from a personal place; Boysen’s family in North Dakota lost three members to cancer, an illness they suspected might be linked to chemical contamination in groundwater. This tragedy fueled his search for a safer alternative to traditional herbicides.

His initial experiments were far from conventional. Boysen first attempted to kill weeds using drones equipped with a 200-watt laser, but the fire risk made that approach impractical. After extensive prototyping, which included exploring ideas like cryogenics, he found his answer in vaporized water. The company demonstrated this steam-based technology earlier this year, showcasing a system that can be attached to standard equipment like mowers, tractors, or ATVs.

The development journey was a classic grassroots effort. The team began by ordering a basic garment steamer from Amazon for initial tests. Realizing its limitations, they purchased seven more and embarked on the difficult task of engineering an industrial-grade solution. The core challenge was not just generating enough steam, but making the process effective, repeatable, and scalable for commercial use.

An even more complex hurdle was teaching the system to see. The technology relies on computer vision to identify weeds in real-time as the equipment moves across a lawn or field. Boysen refers to this as the “green-on-green” problem, distinguishing unwanted plants from desirable grass in a monochromatic environment. The company uses AI software powered by Nvidia GPUs to achieve this precise, on-the-fly recognition, a critical component for the system’s practicality.

Naware is currently focusing its commercial efforts on professional landscapers who maintain large areas like athletic fields and golf courses. Boysen claims the technology can offer substantial savings, potentially reducing chemical costs for such clients by anywhere from $100,000 to $250,000. Additional savings come from reducing labor dedicated solely to chemical application. The startup has been conducting paid pilot programs to refine the product and has already attracted interest from major equipment manufacturers.

For Boysen, the path forward hinges on three key elements: forging strategic partnerships with established industry players, securing strong patents for the technology, and raising sufficient capital. While he has bootstrapped the company to this point, he plans to initiate a formal funding round soon. His goal is to secure a round so compelling that it deters potential competitors, allowing Naware to firmly establish its chemical-free solution in the market.

(Source: TechCrunch)

Topics

weed control 95% steam technology 90% startup development 88% tech startup 87% chemical-free agriculture 85% computer vision 82% product prototyping 80% ai training 78% startup funding 77% business partnerships 75%