Smart Weeding (LUXEED Robotics & Synthgen) is one of the MIT AI projects funded in 2025. Smart Weeding is developing an AI-driven laser weeding platform that can control weeds in agriculture without the use of herbicides. The project focuses on a more precise, cleaner, and more scalable method of weed control, in which artificial intelligence helps reliably distinguish between crops and weeds.
LUXEED and Synthgen are combining their expertise
With Smart Weeding, two complementary partners are combining their expertise. LUXEED Robotics is developing an agricultural machine that uses cameras, AI, and lasers to detect and eliminate weeds without damaging the surrounding crops. Synthgen brings expertise in synthetic data and automatic labeling for computer vision: by generating and labeling realistic training images, AI models can learn more quickly to handle variations in plants, soil, lighting conditions, and growth stages. Together, they are working on a robust recognition and control system for laser weeding in practical applications.
From image recognition to action
The core of Smart Weeding lies in the transition from image recognition to targeted action: whereas farmers currently often rely on chemical agents, mechanical tillage, or manual weeding, Smart Weeding aims to automatically identify weeds and treat them at the plant level. The system must determine from camera images what is a crop and what is a weed, after which the laser precisely targets the weed’s growth center. Synthetic data can help train the AI model on situations that are difficult, scarce, or costly to capture in real-world field data, such as early crop stages, overlapping leaves, or varying field conditions.
In this way, Smart Weeding helps farmers adopt a future-proof approach to weed management: less reliance on herbicides and manual labor, less disruption to the soil and crops, and greater precision in treating individual plants. In this way, the project can contribute to more sustainable farming, better use of labor and resources, and agricultural practices in which robotics and AI help to better balance yield, soil health, and environmental impact.
The MIT AI Program funds collaborative R&D projects by small and medium-sized enterprises (SMEs) that are using AI to develop a product, process, or service. The call for proposals is part of the AiNed program of the National Growth Fund; AIC4NL coordinates the program, and RVO processes the applications.
