AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images
This addresses the time-consuming and error-prone annotation process in plant phenotyping, offering an incremental improvement over supervised methods.
The paper tackles the problem of counting plant organs in field images without requiring annotations by proposing a fully unsupervised technique, achieving competitive performance on sorghum and wheat datasets without dataset-specific tuning.
Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the literature using state-of-the-art supervised deep learning techniques. However, the annotation of organs in field images is time-consuming and prone to errors. In this paper, we propose a fully unsupervised technique for counting dense objects such as plant organs. We use a convolutional network-based unsupervised segmentation method followed by two post-hoc optimization steps. The proposed technique is shown to provide competitive counting performance on a range of organ counting tasks in sorghum (S. bicolor) and wheat (T. aestivum) with no dataset-dependent tuning or modifications.