Vision statement: Promote the development and use of digital imagery for advancing weed research and precision weed control applications

Technological advancements in digital imaging and artificial intelligence/machine learning (AI/ML) have enabled the capability of accurate recognition of individual weed plants and plant parts as they occur within agricultural contexts. This capability facilitates opportunities for efficient weed management through precision targeting of individual plants with an expanded range of control technologies. Further, these technologies can advance our understanding of weed ecology, through the potential for highly detailed and efficient examination of agricultural environments and weed-crop interactions. The development of weed recognition technologies will have an immense impact on the field of weed science, with the potential to completely transform weed research and the ability to control weeds. 

Weed recognition capability using a machine-learning-based approach occurs in three key steps commencing with image collection, followed by image classification/annotation, and then algorithm training on the image dataset. Weed recognition algorithm development is, therefore, founded as well as completely reliant on the availability of suitable weed image datasets. The image dataset requirements for algorithm training increase with the complexity of the scenarios in which weeds occur. Simple scenarios may require just a few thousand images, while more complex scenarios (e.g. multiple grass seedlings in a mix) may require millions of suitably annotated images. A further challenge is that each weed in each scenario requires an individualized recognition algorithm and, therefore, a suitable image dataset that is relevant to such a scenario. Thus, despite the exciting weed research and control opportunities that come with digital technologies, the need for suitable image datasets and recognition algorithms is currently a major limiting factor. Additionally, providing adequate training and outreach is critical for promoting the broad application of these technologies.