Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images

Abstract

Phenotyping provides important support for corn breeding. Unfortunately, the rapid detection of phenotypes has been the major limiting factor in estimating and predicting the outcomes of breeding programs. This study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and deepening UAV techniques for comparing phenotypes and screening new corn varieties. Two geometric traits (plant height, canopy leaf area index (LAI)) and one lodging resistance trait (lodging area) were estimated in this study. It was found that stereoscopic and photogrammetric methods were promising ways to calculate a digital surface model (DSM) for estimating corn plant height from UAV images, with R2 = 0.7833 (p

Code and data availability

The paper describes UAV-based corn phenotyping (plant height via SfM/nDSM, lodging estimation, PROSAIL LAI retrieval) with custom code, but no public dataset, image, or code deposit is stated. The only URLs mentioned are the Pix4Dmapper software site (a commercial tool, not a paper asset), the generic PROSAIL Python Py

No evidence-backed public reproduction asset is currently recorded.