Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil.
Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-260, São Paulo, Brazil.
Sensors (Basel). 2024 Oct 9;24(19):6490. doi: 10.3390/s24196490.
The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.
非成像高光谱传感器的应用极大地促进了不同植物物种叶片光学特性的研究。本研究采用叶绿素荧光（ChlF）和紫外可见近红外短波红外（UV-VIS-NIR-SWIR）波段的高光谱非成像传感器来评估叶片生物物理参数。为了进行分析，采用主成分分析（PCA）和偏最小二乘回归（PLSR）来预测绿色和紫色叶片的 8 个结构和超微结构（生物物理）特征。主要结果表明，特定的高光谱植被指数（HVIs）显著提高了偏最小二乘回归（PLSR）模型的精度，能够可靠且无损地评估植物生物物理属性。PCA 揭示了独特的光谱特征，第一主成分解释了传感器数据中超过 90%的变化。对于叶肉厚度、上表皮和下表皮下皮层厚度、总叶厚等变量，预测精度较高，尽管在预测叶肉和类囊体膜中粒层的厚度等参数方面仍存在挑战。整合叶绿素荧光和高光谱技术，以及分光辐射计和荧光传感器，在推进植物生理研究以及改善光学光谱在环境监测和评估中的应用方面具有重要意义。这些方法为促进未来广泛的植物物种的农业实践的可持续性提供了一种良好的策略，支持细胞生物学和材料分析。
