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, we introduce a dataset, so-called DAVIS-Ag, for promoting more extensive research on Domain-inspired Active VISion in Agriculture. To be specific, we leveraged our open-source "AgML" framework and 3D plant simulator of "Helios" to produce 502K RGB images from 30K densely sampled spatial locations in 632 synthetic orchards. Moreover, plant environments of strawberries, tomatoes, and grapes are considered at two different scales (i.e., Single-Plant and Multi-Plant). Useful labels are also provided for each image, including (1) bounding boxes and (2) instance segmentation masks for all identifiable fruits, and also (3) pointers to other images of the viewpoints that are reachable by an execution of action so as to simulate active viewpoint selections at each time step. Using DAVIS-Ag, we visualize motivating examples where fruit visibility can dramatically change depending on the pose of the camera view primarily due to occlusions by other components, such as leaves. Furthermore, we present several baseline models with experiment results for benchmarking in the task of target visibility maximization. Transferability to real strawberry environments is also investigated to demonstrate the feasibility of using the dataset for prototyping real-world solutions. For future research, our dataset is made publicly available online: https://github.com/ctyeong/DAVIS-Ag.