Understanding Neutral Weed Communities - Crops Interactions and Identification of Artificial Intelligence Algorithms for Species-Specific Weed Management Using Robots

The overarching objective of NEUTRAWEED is to form an international and inter-sectoral network of 16 organizations in four continents (Europe, Africa, Asia, and America) working on a joint research program in the fields of weed ecology and management, artificial intelligence (AI), and robotics.

New sustainable weed management solutions are requested since the over-reliance on chemical control and intensive tillage in over-simplified cropping systems are now questioned because they have many drawbacks.

As a solution, we propose the promotion of Neutral Weed Communities (NWCs), which are weed communities that can coexist with the crops without reducing crop yield or quality compared with weed-free conditions.

In this 4-yer research plan, we propose to 1) identify NWCs in diverse cropping systems and study their interactions with crops and other biotic agents, 2) investigate their effect on ecosystem services provision, 3) develop weeder robot prototypes for weed-specific analysis; 4) identify AI algorithms for weed species-specific discrimination to test on robots to attain NWCs.

A multidisciplinary and intersectoral approach will be used to comprehend the multiple interactions between weeds and crops under four aspects: agroecological, physiological, molecular, and microbiological.

Using weed species images, AI algorithms will be developed and tested on autonomous robots to attain NWCs by selectively removing only highly competitive weed species while leaving only the beneficial ones.

The staff members will develop new skills and competencies, will be exposed to new research environments, and will have their career perspectives broadened.

NEUTRAWEED will advance research by fostering the interconnections among countries, providing training and education at academic and technical levels, and engaging with local, national, and international stakeholders to create shared knowledge on weed-crop interaction and new sustainable AI technological solutions to manage weed.