Panicle Counting in UAV Images For Estimating Flowering Time in Sorghum

TL;DR

This paper presents a UAV-based deep learning approach for rapid panicle counting in sorghum to estimate flowering time, offering a faster alternative to manual methods with demonstrated accuracy.

Contribution

It introduces a novel UAV imaging method combined with deep neural networks for automated panicle detection and flowering time estimation in sorghum.

Findings

Accurately detects panicles in UAV images

Estimates flowering time effectively

Outperforms manual counting in speed

Abstract

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.