# AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images

arXiv: https://arxiv.org/abs/2007.09178 · HTML: https://mlsanity.com/paper/2007.09178/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [AGI for Agriculture](https://mlsanity.com/paper/2304.06136/) (2023)
- [Learning to Count without Annotations](https://mlsanity.com/paper/2307.08727/) (2023)
- [Gaussian map predictions for 3D surface feature localisation and counting](https://mlsanity.com/paper/2112.03736/) (2021)
- [Wheat Head Counting by Estimating a Density Map with Convolutional Neural Networks](https://mlsanity.com/paper/2303.10542/) (2023)
- [A Semi-Self-Supervised Approach for Dense-Pattern Video Object Segmentation](https://mlsanity.com/paper/2406.05131/) (2024)

## Abstract (arXiv)

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.