Survey Reveals Disconnect Between Weed Science Research Priorities and Available Skillsets

The Weed Science Society of America (WSSA) recently conducted a survey among U.S. and Canadian weed science leaders, revealing a critical need for expanded technical competencies in the field of weed science. Precision weed management and robotics were identified as essential areas that require expertise in artificial intelligence (AI), machine learning, and UAV technologies. However, there is a noticeable gap in these skillsets among current leaders.

Virginia Tech Weed Science Assistant Professor, Vijay Singh, Ph.D., highlighted the challenges associated with UAV-based herbicide applications, which require specific licenses, training, and data analytics expertise. Singh pointed out that there is a shortage of individuals who possess these attributes, as well as a lack of engineering graduates who are knowledgeable in areas such as AI and machine learning and willing to engage in interdisciplinary research.

The survey findings indicate a shift in research priorities, with herbicides ranking lower than cultural and preventative weed management and precision weed management. While advancements like UAV-based herbicide applications show promise, public funding for weed research has declined. This has raised concerns about the lack of support for critical areas like integrated weed management. Weed scientists stress the need for increased public funding to address these challenges and accelerate the development and application of AI and machine learning technologies in weed management.

As a response to the survey findings, stakeholders are advocating for increased public funding to support weed science research and the development of cutting-edge technologies in the field. By investing in AI, machine learning, and UAV technologies, researchers aim to enhance precision weed management practices and ultimately improve the sustainability and efficiency of weed control strategies.