Mapping nitrogen-use responsiveness through image-based phenotyping in maize1

Abstract

ABSTRACT High-throughput phenotyping has emerged as a strategic tool in maize breeding, enabling the rapid and accurate assessment of agronomic traits. This study aimed to select vegetation indices derived from RGB imagery for the identification of high-yielding maize genotypes under contrasting nitrogen fertilization conditions at the plot level. A total of 35 maize genotypes were evaluated in a randomized complete block design with a split-plot arrangement and three replications, subjected to two nitrogen levels (20 and 140 kg ha-1). Four unmanned aerial vehicle flights were conducted at two altitudes (60 and 80 m), and genetic and spatial analyses were performed using mixed models (REML/BLUP). The flight conducted at 61 days after planting (V7/V8 stage) at 60 m of altitude exhibited the highest repeatability and accuracy. The blue green pigment index (BGI) demonstrated high sensitivity in discriminating nitrogen levels and in the indirect selection of high-yielding genotypes. The genotypes G42, G4, G34, G31, G20, G16, and G1 were identified as superior for grain yield. Vegetation indices based on the blue spectral band, such as BGI, are effective for the early selection of genotypes under nitrogen stress conditions prior to flowering.

Plant phenotyping relevance

UAV RGB画像から植生指数を抽出し、窒素応答や収量性の推定に用いる画像ベース表現型解析が研究の中心で、飛行条件・反復性・精度も評価している。

Code and data availability

The paper's UAV RGB imagery, vegetation-index phenotyping data, and REML/BLUP analysis are not publicly deposited. The Data Availability Statement explicitly restricts access: 'Research data are only made available by authors upon request.' No public repository, code deposit, or author URL is provided in any supplied,

No evidence-backed public reproduction asset is currently recorded.