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Accurately counting maize tassels is important for monitoring the growthstatus of maize plants. This tedious task, however, is still mainly done bymanual efforts. In the context of modern plant phenotyping, automating thistask is required to meet the need of large-scale analysis of genotype andphenotype. In recent years, computer vision technologies have experienced asignificant breakthrough due to the emergence of large-scale datasets andincreased computational resources. Naturally image-based approaches have alsoreceived much attention in plant-related studies. Yet a fact is that mostimage-based systems for plant phenotyping are deployed under controlledlaboratory environment. When transferring the application scenario tounconstrained in-field conditions, intrinsic and extrinsic variations in thewild pose great challenges for accurate counting of maize tassels, which goesbeyond the ability of conventional image processing techniques. This calls forfurther robust computer vision approaches to address in-field variations. Thispaper studies the in-field counting problem of maize tassels. To our knowledge,this is the first time that a plant-related counting problem is consideredusing computer vision technologies under unconstrained field-based environment.

TasselNet: Counting maize tassels in the wild via local counts regression network