Elleouet Joane S, Main Russell, Hartley Robin J L, Watt Michael S
Data and Geospatial Intelligence, Scion, Wellington, New Zealand.
Data and Geospatial Intelligence, Scion, Rotorua, New Zealand.
Front Plant Sci. 2025 Jun 17;16:1574720. doi: 10.3389/fpls.2025.1574720. eCollection 2025.
Phenotyping is critical in tree breeding, but traditional methods are often labour-intensive and not easily scalable. Resistance to biotic and abiotic stress is a key focus in tree breeding programmes. While heritable traits derived from spectral remote sensing have been identified in trees, their application to tree phenotyping remains unexplored. This study investigates high-throughput hyperspectral and thermal imaging for assessing Dothistroma needle blight (DNB) resistance in D.Don.
Using UAV-based hyperspectral and thermal imaging during a severe DNB outbreak in a clonal trial in New Zealand, we computed narrow-band hyperspectral indices (NBHIs), canopy temperature indices, radiative transfer inverted plant traits, and solar-induced fluorescence. Visual severity scores and remote sensing indices were modelled using spatially explicit mixed-effect linear models integrating pedigree and genomic data in a single-step genomic evaluation. Multi-trait models and sampling simulations were used to evaluate the potential of remote sensing indices to supplement or replace traditional phenotyping.
Remote sensing indices exhibited narrow-sense heritability values comparable to severity scores (up to 0.37) and high absolute correlation coefficients with severity scores (up to 0.79). Carotenoid and chlorophyll-related NBHIs were the most informative, reflecting physiological impacts of DNB. Combining partial visual scoring with NBHIs maintained high estimated breeding value (EBV) accuracy (0.68) at 50% scoring and moderate accuracy (0.59) at 20% scoring. EBV correlation with full scoring was above 0.8 even at 20% scoring. Using solely the most heritable NBHI achieved 0.71 breeding value accuracy and 0.79 absolute EBV correlation with severity scores, suggesting NBHIs can replace visual scoring with minimal precision loss.
By utilising UAV-based hyperspectral and thermal imaging to capture single-tree phenotypes related to disease in a forestry trial and pairing the data to genomic evaluation, this study establishes that remote sensing data offers an efficient, scalable alternative to traditional phenotyping. Our approach constitutes a major step towards characterising specific physiological responses, facilitating the discovery of the genetic architecture of physiological traits, and significantly enhancing genetic improvement.
表型分析在树木育种中至关重要，但传统方法往往劳动强度大且难以扩展。对生物和非生物胁迫的抗性是树木育种计划的关键重点。虽然已在树木中鉴定出源自光谱遥感的可遗传性状，但其在树木表型分析中的应用仍未得到探索。本研究调查了高通量高光谱和热成像技术，以评估辐射松对散斑壳针孢叶枯病（DNB）的抗性。
在新西兰的一个无性系试验中，利用基于无人机的高光谱和热成像技术，在严重的DNB爆发期间，我们计算了窄带高光谱指数（NBHIs）、冠层温度指数、辐射传输反演植物性状和太阳诱导荧光。使用空间明确的混合效应线性模型对视觉严重度评分和遥感指数进行建模，该模型在单步基因组评估中整合了谱系和基因组数据。使用多性状模型和抽样模拟来评估遥感指数补充或替代传统表型分析的潜力。
遥感指数表现出与严重度评分相当的狭义遗传力值（高达0.37），以及与严重度评分的高绝对相关系数（高达0.79）。与类胡萝卜素和叶绿素相关的NBHIs信息含量最高，反映了DNB的生理影响。将部分视觉评分与NBHIs相结合，在50%评分时保持了较高的估计育种值（EBV）准确性（0.68），在20%评分时保持了中等准确性（0.59）。即使在20%评分时，EBV与全评分的相关性也高于0.8。仅使用最具遗传性的NBHI就实现了0.71的育种值准确性和与严重度评分0.79的绝对EBV相关性，这表明NBHIs可以替代视觉评分，且精度损失最小。
通过利用基于无人机的高光谱和热成像技术在林业试验中捕获与疾病相关的单株表型，并将数据与基因组评估相结合，本研究表明遥感数据为传统表型分析提供了一种高效、可扩展的替代方法。我们的方法朝着表征特定生理反应、促进生理性状遗传结构的发现以及显著提高遗传改良迈出了重要一步。
