# Panicle Counting in UAV Images For Estimating Flowering Time in Sorghum

arXiv: https://arxiv.org/abs/2107.07308 · HTML: https://mlsanity.com/paper/2107.07308/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2021.

## Built on by (most cited first, 2 of 2)

- [High-Resolution UAV Image Generation for Sorghum Panicle Detection](https://mlsanity.com/paper/2205.03947/) (2022)
- [Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery](https://mlsanity.com/paper/2305.09810/) (2023)

## Abstract (arXiv)

Flowering time (time to flower after planting) is important for estimating plant development and grain yield for many crops including sorghum. Flowering time of sorghum can be approximated by counting the number of panicles (clusters of grains on a branch) across multiple dates. Traditional manual methods for panicle counting are time-consuming and tedious. In this paper, we propose a method for estimating flowering time and rapidly counting panicles using RGB images acquired by an Unmanned Aerial Vehicle (UAV). We evaluate three different deep neural network structures for panicle counting and location. Experimental results demonstrate that our method is able to accurately detect panicles and estimate sorghum flowering time.