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Advancements in machine vision that enable detailed inferences to be madefrom images have the potential to transform many sectors including agriculture.Precision agriculture, where data analysis enables interventions to beprecisely targeted, has many possible applications. Precision spraying, forexample, can limit the application of herbicide only to weeds, or limit theapplication of fertiliser only to undernourished crops, instead of spraying theentire field. The approach promises to maximise yields, whilst minimisingresource use and harms to the surrounding environment. To this end, we proposea hierarchical panoptic segmentation method to simultaneously identifyindicators of plant growth and locate weeds within an image. We adaptMask2Former, a state-of-the-art architecture for panoptic segmentation, topredict crop, weed and leaf masks. We achieve a PQ{\dag} of 75.99.Additionally, we explore approaches to make the architecture more compact andtherefore more suitable for time and compute constrained applications. With ourmore compact architecture, inference is up to 60% faster and the reduction inPQ{\dag} is less than 1%.

Hierarchical Mask2Former: Panoptic Segmentation of Crops, Weeds and Leaves