In agricultural environments, viewpoint planning can be a critical
functionality for a robot with visual sensors to obtain informative
observations of objects of interest (e.g., fruits) from complex structures of
plant with random occlusions. Although recent studies on active vision have
shown some potential for agricultural tasks, each model has been designed and
validated on a unique environment that would not easily be replicated for
benchmarking novel methods being developed later. In this paper, hence, we
introduce a dataset for more extensive research on Domain-inspired Active
VISion in Agriculture (DAVIS-Ag). To be specific, we utilized our open-source
"AgML" framework and the 3D plant simulator of "Helios" to produce 502K RGB
images from 30K dense spatial locations in 632 realistically synthesized
orchards of strawberries, tomatoes, and grapes. In addition, useful labels are
provided for each image, including (1) bounding boxes and (2) pixel-wise
instance segmentations for all identifiable fruits, and also (3) pointers to
other images that are reachable by an execution of action so as to simulate the
active selection of viewpoint at each time step. Using DAVIS-Ag, we show the
motivating examples in which performance of fruit detection for the same plant
can significantly vary depending on the position and orientation of camera view
primarily due to occlusions by other components such as leaves. Furthermore, we
develop several baseline models to showcase the "usage" of data with one of
agricultural active vision tasks--fruit search optimization--providing
evaluation results against which future studies could benchmark their
methodologies. For encouraging relevant research, our dataset is released
online to be freely available at: https://github.com/ctyeong/DAVIS-AgComment: 8 pages, 5 figures, 4 table