Artificial intelligence applications used to monitor plant health and growth, especially for commercially valuable crops, are important for both scientific research and economic value. Despite the availability of numerous datasets, most of them are designed primarily for disease classification. In contrast, datasets annotated at the leaf instance level remain limited. This limitation constrains many studies to classification-based approaches, restricting the development of more advanced tasks such as instance-level segmentation. In this study, leaves from 13 different plant datasets, most of which were originally for disease classification, were segmented at the instance level and introduced as a new dataset named LeavesBank. Within LeavesBank, 32,232 unique images containing real backgrounds, gathered using different devices and across various geographic regions, were combined. Subsequently, an active learning–based annotation pipeline was implemented, through which a total of 221,427 leaf instances were segmented, including 198,489 primary and 22,938 secondary annotations. The effectiveness of the LeavesBank was evaluated using four public datasets, through both zero-shot evaluation and fine-tuning experiments. According to the experimental results, LeavesBank-pretrained models achieved 10%, 5%, and 1% higher mAP50 scores on the Komatsuna, GrowliFlower and SoyCotton datasets, respectively, compared with the most recent foundation model, SAM3. Only on the Potato Leaf dataset did SAM3 outperform the LeavesBank-pretrained models, achieving a 9% higher mAP50 score. Furthermore, in experiments, the proposed approach demonstrated 1%–24% improvements in mAP50 performance according to the literature. The LeavesBank dataset is publicly available at: https://www.kaggle.com/datasets/aslhanyldrm/leavesbank-dataset