The best-performing genotypes according to yield

Multispectral optical data significantly enhances cereal crop monitoring by enabling precise tracking of growth stages, early detection of germination issues, and assessment of plant health. This study evaluates the potential of integrating UAV multispectral sensor with the handheld Plant-O-Meter device for high-precision crop monitoring. The aim was to determine the optimal UAV imaging timing that aligns with proximal sensor measurements to improve growth stage assessments. Experiments were conducted on 41 cereal genotypes, including ancient and modern varieties, under two nitrogen top-dress dosages across 130 plots. The top ten performing genotypes were analyzed to identify resilient varieties adaptable to climate change and evolving field conditions. Our results demonstrate that vegetation indices during booting and spike emergence stages consistently predict yield potential, offering a robust framework for early-stage yield estimation. Additionally, we provide a comparative analysis of UAV and handheld sensor data, highlighting their respective strengths and limitations. Three vegetation indices, GRDVI, NDVI and SAVI demonstrated a very strong average positive correlation: 0.957, 0.954 and 0.944 across the selected genotypes from different performance levels. The combined dataset supports improved fertilization strategies, optimized seeding cycles, and identification of genotypes with stable agronomic traits. This study underscores the synergistic potential of aerial and proximal sensing technologies for next-generation cereal crop management and precision agriculture.

多光谱光学数据可实现谷类作物生育期的精准追踪、萌发问题的早期识别与植株健康状况评估，从而显著提升作物监测效能。本研究评估了将无人机（UAV）多光谱传感器与手持型Plant-O-Meter设备集成以开展高精度作物监测的可行性，旨在确定与近地传感器测量结果相匹配的最优无人机成像时机，进而优化作物生育期评估效果。实验共设置130个样地，在两种氮肥追施剂量下，对包含古老品种与现代品种在内的41份谷类基因型材料开展试验。研究对表现最优的10份基因型材料展开分析，以筛选出可适应气候变化与田间动态条件的高耐性品种。结果表明，孕穗期与抽穗期的植被指数可稳定预测产量潜力，为早期产量估算提供了可靠的分析框架。此外，本研究还对无人机与手持传感器的采集数据开展对比分析，明确了二者各自的优势与局限性。在不同表现层级的筛选基因型中，GRDVI、NDVI与SAVI这三种植被指数均展现出极强的平均正相关关系，相关系数分别为0.957、0.954与0.944。本整合数据集可为优化施肥策略、优化播种周期以及筛选具有稳定农艺性状的基因型材料提供支撑。本研究凸显了航空遥感与近地传感技术协同应用于下一代谷类作物管理与精准农业的巨大潜力。