Successful use of hyperspectral imaging technology to progress precision agriculture is highly dependent on calibration on species of interest. To date, high-throughput hyperspectral imaging to predict plant growth and nutrient content has largely been limited to single-species cultivations. Therefore, this study aimed to calibrate a range of agronomic traits in mixed cultivations to hyperspectral image data. It successfully demonstrated that hyperspectral imaging can predict the plant traits biomass (g), foliar nitrogen (N) concentration (mg g⁻¹) and N yield (mg), in grass and legume monocultures and polycultures in response to differential N and phosphorus (P) fertilization in a controlled greenhouse experiment. Visible light and near infrared (VNIR) and short wavelength infrared (SWIR) input resulted in only minor image misclassification (0.02%) for the green plants from the background regardless of species. The trained partial least square regression (PLSR) models VNIR-HH (hyper-hue) and SWIR had the lowest misclassification errors of 3.16% and 9.56% and were used for the grass–legume classification. For grass, there was good agreement between the mixed-effect models derived from the laboratory, and the PLSR models from hyperspectral measurements, except for the effect of N × P on N yield. Legume model agreement was not as precise as grass, likely because fertilizer-driven treatment effects on the measured traits were not as clear. Key wavelengths contributing to the strength of the PLSR models for predicting N content and biomass were identified from this study. Effective calibration of growth and nutrient uptake traits against hyperspectral data in mixed cultivations under controlled conditions is an important contribution towards improving remote sensing technologies for broader application in polyculture field cropping.

成功使用高光谱成像技术来推进精准农业高度依赖于对感兴趣物种的校准。迄今为止，用于预测植物生长和养分含量的高通量高光谱成像在很大程度上仅限于单一物种的栽培。因此，本研究旨在将混合栽培中的一系列农艺性状校准为高光谱图像数据。它成功地证明了高光谱成像可以预测植物性状生物量（g）、叶面氮（N）浓度（mg g ^-1) 和 N 产量 (mg)，在受控温室实验中响应差异 N 和磷 (P) 施肥的草和豆类单一栽培和混合栽培。可见光和近红外 (VNIR) 和短波红外 (SWIR) 输入仅导致背景中绿色植物的图像错误分类 (0.02%)，无论物种如何。经过训练的偏最小二乘回归 (PLSR) 模型 VNIR-HH（hyper-hue）和 SWIR 的误分类误差最低，分别为 3.16% 和 9.56%，用于草-豆类分类。对于草，除了 N × P 对 N 产量的影响外，实验室得出的混合效应模型与高光谱测量的 PLSR 模型之间有很好的一致性。豆类模型协议不如草精确，可能是因为肥料驱动的处理对测量性状的影响并不那么清楚。从这项研究中确定了有助于预测 N 含量和生物量的 PLSR 模型强度的关键波长。在受控条件下，根据混合栽培中的高光谱数据有效校准生长和养分吸收性状，是改进遥感技术以在混耕大田种植中更广泛应用的重要贡献。