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A new AI model has been developed that can quickly tell how much weeds have grown—and it could help ensure global food security by accelerating the development of next-generation herbicides.

Created as part of a collaborative project between Loughborough University computer scientists and agricultural biotech firm Moa, the AI system aims to eliminate the need for manual assessments of how well herbicides—chemicals used to kill weeds—work.

Traditionally, assessing herbicide effectiveness relies on human experts visually determining weed growth after chemical exposure—a slow and labor-intensive process. The new AI system automates this task, making it faster, more accurate, and highly scalable.

"By integrating AI and advanced monitoring techniques, the agrochemical industry can ensure smarter, more sustainable herbicide development and use, helping to achieve global sustainability goals by protecting ecosystems and natural resources," said Professor Baihua Li, an expert in AI and computer vision and the Loughborough University project lead.

"We believe this collaboration sets the stage for continued innovation in AI-driven agricultural solutions. It shows how modern technologies can be harnessed to drive more efficient and environmentally responsible farming practices."

The urgent need for new herbicides

Farmers have long struggled with weeds as they compete with crops for moisture, nutrients, and sunlight, reducing yields and threatening food production. For over 50 years, herbicides have been the primary solution; these chemicals interfere with plant biology, killing unwanted weeds while allowing crops to thrive.

However, two major challenges are emerging. Weeds are developing resistance, making existing herbicides less effective. At the same time, scientific evidence is starting to reveal that some herbicides pose environmental and health risks, raising concerns about their long-term use.

There is now an urgent need to develop safer and more effective herbicides that can combat resistant weeds while minimizing harm to people and the environment.

, an Oxford University spin-out, is tackling this problem by developing the next generation of weed control solutions. Their scientists use an advanced screening system to identify thousands of potential new "Mode of Action (MoA)" herbicides—chemicals designed to attack weeds in novel ways, including blocking essential enzymes.

The most promising herbicide candidates are tested on a range of common weed species in the company's greenhouses. After treatment with a herbicide candidate, scientists evaluate weed growth and compare it to untreated control plants to assess the chemical's effectiveness.

Moa is testing thousands of promising chemicals and evaluating their effects on the growth of tens of thousands of test weeds. By automating the assessment process, the AI model minimizes errors associated with manual observations and significantly speeds up weed growth measurements. This advancement has the potential to accelerate the development of effective, non-harmful herbicides.

The AI works by analyzing images of treated and non-treated weeds and objectively categorizes herbicide effectiveness into three levels: "Active" (significant impact, minimal to no weed growth), "Moderate" (partial inhibition of growth), and "Inactive" (no visible impact on weed growth).

The developed AI model was trained on more than 6,000 images of weeds and, when tested using the dataset, the model achieved 95% effective in assessing herbicide effectiveness, with findings set to be published soon in a peer-reviewed journal.

Dr. Majedaldein Almahasneh, a Research Associate at Loughborough University, worked on the project with support from the Loughborough academic team, including Professor Li, Dr. Haibin Cai and Professor Qinggang Meng.

When asked what the next steps are for this research, Dr. Almahasneh said, "Our AI model is now being integrated into Moa's glasshouse pipelines, to give scientists a standardized, scalable, reproducible process, reducing manual labor and improving the overall quality of assessment.

"AI cannot find and develop new, safe and effective herbicides all on its own, out of nowhere. It needs data: the higher quality the better. Moa has a huge bank of data—having already screened more than 750,000 chemical compounds.

"We are starting to use the AI to help clean up and interrogate this data: re-analyzing archived experiments and recovering previously overlooked compounds. This will help us—and AI—to make better decisions."

Moa and Loughborough University collaborated to create the AI model as part of a Knowledge Transfer Partnership (KTP)—a UK government-backed initiative that connects businesses with university researchers to drive innovation.

Dr. Nasir Rajabi, Principal Scientist at Moa Technology, said, "While AI on its own cannot find a new generation of modern, safe and effective herbicides to help farmers protect their harvests, tools like this, co-created with the Loughborough KTP team, are playing a vital role in helping us accelerate our discoveries."