Ground and aerial imagery dataset for strawberry breeding trials: Training deep learning models for runner detection and segmentation

Runners play a crucial role in vegetative propagation and provide a
cost-effective method for expanding strawberry production, excessive
growth presents significant challenges. Uncontrolled runner development
diverts energy from the mother plant, leading to smaller, lower-quality
fruit and ultimately reducing overall yield. Accurate and efficient
identification of strawberry runners is essential for optimizing crop
management and propagation. This dataset was prepared to enhance the
automation of strawberry runner identification in field environments by
developing a deep learning-based approach. A diverse dataset of strawberry
images was collected, encompassing a wide range of varieties, different
growth stages, and multiple growing seasons. Images were captured using
three different platforms and cameras at varying altitudes to improve
dataset diversity: 0.5 m above the ground level (AGL) for ground imaging
(GI), 10 m AGL for aerial imaging (AI10), and 5 m AGL for aerial imaging
(AI5). All raw images were preprocessed to segment individual plants. out
and saved. Runners were annotated using polygonal masks to delineate their
boundaries, with a single class designated for runner identification.