1 山东农业大学信息科学与技术学院, 山东 泰安 271018
2 山东农业大学资源与环境学院, 土肥资源高效利用国家工程实验室, 山东 泰安 271018
氮素是影响冬小麦生长的重要元素, 如何根据冬小麦需求适时变量施用氮肥是现代农业精准施肥研究需要解决的关键问题之一。 无人机遥感技术在冬小麦生长情况监测中具有高分辨率、 高时效性、 低成本等优势, 为解决施肥需求监测问题提供了重要数据源。 因此研究无人机多光谱影像数据, 构建其与冬小麦产量与施肥量之间的关系模型对于精准施肥研究十分重要。 选择冬小麦典型生产区山东省桓台县为实验区, 布置4种不同施氮水平的田间实验。 利用无人机搭载Sequoia多光谱传感器, 采集实验区不同氮素施肥水平的冬小麦返青初期多光谱影像, 同时测得冬小麦冠层叶绿素含量(soil and plant analyzer development, SPAD)数据及产量数据。 通过多光谱影像数据计算获得归一化植被指数(normalized difference vegetation index, NDVI)、 叶绿素吸收指数(modified chlorophyll absorption ratio index, MCARI2)等6种形式植被指数, 建立无人机多光谱影像植被指数与小麦冠层SPAD值的线性、 二阶多项式、 对数、 指数和幂函数模型, 优选地面氮素状况最优植被指数模型, 反演冬小麦不同施氮水平的状况, 进而根据不同施氮水平与敏感植被指数和冬小麦产量的关系, 构建了基于植被指数指标的氮肥变量施肥模型, 并将模型应用于同时期小麦多光谱影像。 结果如下: (1)地面实测的SPAD值能较好的反映冬小麦施氮水平及生长状况。 无人机多光谱数据分区统计结果表明不同施氮水平冬小麦冠层反射率有较大差异性。 (2)结构性植被指数与SPAD拟合效果优于其他类型指数。 MCARI2的二阶多项式模型精度最优(R2=0.790, RMSE=0.22), 其能较好的移除冬小麦返青初期土壤背景等因素的影响, 为氮肥敏感植被指数。 (3)基于产量-施氮量模型和产量-敏感植被指数模型, 构建敏感植被指数的氮肥变量施肥模型为Nr=10 707.63×MCARI22-5 992.36×MCARI2+715.27。 通过模型应用生成了实验区冬小麦氮肥变量施肥图, 与实际情况具有较高一致性。 该研究提出了利用无人机多光谱数据进行冬小麦施氮决策的模型及方法, 为冬小麦精准施肥的进一步研究提供了依据。
精准农业 无人机 多光谱传感器 植被指数 氮肥推荐 Precision agriculture UAV Multi-Spectral sensor Vegetation index Nitrogen recommended 光谱学与光谱分析
2019, 39(11): 3599
Author Affiliations
Abstract
1 Key Lab of Advanced Transducers and Intelligent Control System, Ministry of Education and Shanxi Province, Taiyuan University of Technology, Taiyuan, 030024, China
2 Section of Transport Engineering and Logistics, Faculty of 3mE, Delft University of Technology, Mekelweg 2, 2628CD, Netherlands
3 College of Information Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
Infrared and visible light image fusion technology is a hot spot in the research of multi-sensor fusion technology in recent years. Existing infrared and visible light fusion technologies need to register before fusion because of using two cameras. However, the application effect of the registration technology has yet to be improved. Hence, a novel integrative multi-spectral sensor device is proposed for infrared and visible light fusion, and by using the beam splitter prism, the coaxial light incident from the same lens is projected to the infrared charge coupled device (CCD) and visible light CCD, respectively. In this paper, the imaging mechanism of the proposed sensor device is studied with the process of the signals acquisition and fusion. The simulation experiment, which involves the entire process of the optic system, signal acquisition, and signal fusion, is constructed based on imaging effect model. Additionally, the quality evaluation index is adopted to analyze the simulation result. The experimental results demonstrate that the proposed sensor device is effective and feasible.
Integrative multi-spectral sensor device infrared and visible fusion beam splitter prism imaging effect model Photonic Sensors
2018, 8(2): 134
