Zhao Jiangui, Chen Ning, Zhu Tingyu, Zhao Xuerong, Yuan Ming, Wang Zhiqiang, Wang Guoliang, Li Zhiwei, Du Huiling
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Institute of Millet Research, Shanxi Agricultural University, Changzhi 046000, China.
Plants (Basel). 2023 Aug 16;12(16):2956. doi: 10.3390/plants12162956.
Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to rapidly and accurately obtain the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophylls (Chls) and total carotenoids (Cars) as indicators to study the variations in the leaf positions of Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed using competitive adaptive reweighted sampling (CARS) and the iteratively retained informative variable (IRIV) algorithm to extract the characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R), root mean square error (RMSE) and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43 and 2.38 for Chla; 0.8391, 0.53 and 2.49 for Chlb; 0.7899, 2.24 and 2.18 for Chls; and 0.7577, 0.27 and 2.03 for Cars, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of the pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis and fertilization decisions in plant growth management.
叶片光合色素在评估营养元素和生理状态方面起着至关重要的作用。在设施农业中，快速准确地获取叶片色素含量和分布对于确保精准的水肥管理至关重要。在我们的研究中，我们以叶绿素a（Chla）、叶绿素b（Chlb）、总叶绿素（Chls）和总类胡萝卜素（Cars）为指标，研究了在10种氮浓度处理下，千日红叶片位置的变化。使用可见-近红外高光谱成像（VNIR-HSI）共采集了2610片叶子（435个样本）。在本研究中，提出了一种“粗-细”筛选策略，使用竞争性自适应重加权采样（CARS）和迭代保留信息变量（IRIV）算法来提取特征波长。最后，使用偏最小二乘回归（PLSR）建立了同步定量模型。采用CARS-IRIV-PLSR建立模型以获得更好的预测效果。预测叶绿素a的决定系数（R）、均方根误差（RMSE）和性能比偏差（RPD）分别为0.8240、1.43和2.38；叶绿素b分别为0.8391、0.53和2.49；总叶绿素分别为0.7899、2.24和2.18；总类胡萝卜素分别为0.7577、0.27和2.03。这些模型与伪彩色图像相结合，实现了色素含量和分布的可视化反演。这些发现对于指导植物生长管理中的色素分布、营养诊断和施肥决策具有重要意义。
