GrapeMOTS: UAV vineyard dataset with MOTS grape bunch annotations recorded from multiple perspectives for enhanced...

Object Detection and Tracking have provided a valuable tool for many tasks, mostly time-consuming and prone-to-error jobs, including fruit counting while in the field, among others. Fruit counting can be a challenging assignment for humans due to the large quantity of fruit available, which turns it into a mentally-taxing operation. Hence, it is relevant to use technology to ease the task of farmers by implementing Object Detection and Tracking algorithms to facilitate fruit counting. However, those algorithms suffer undercounting due to occlusion, which means that the fruit is hidden behind a leaf or a branch, complicating the detection task. Consequently, gathering the datasets from multiple viewing angles is essential to boost the likelihood of recording the images and videos from the most visible point of view. Furthermore, the most critical open-source datasets do not include labels for certain fruits, such as grape bunches. This study aims to unravel the scarcity of public datasets, including labels, to train algorithms for grape bunch Detection and Tracking by considering multiple angles acquired with a UAV to overcome fruit occlusion challenges.