Zhao Huijie, Xu Lunbao, Shi Shaoguang, Jiang Hongzhi, Chen Da
School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2018 Apr 2;18(4):1068. doi: 10.3390/s18041068.
Hyperspectral and three-dimensional measurements can obtain the intrinsic physicochemical properties and external geometrical characteristics of objects, respectively. The combination of these two kinds of data can provide new insights into objects, which has gained attention in the fields of agricultural management, plant phenotyping, cultural heritage conservation, and food production. Currently, a variety of sensors are integrated into a system to collect spectral and morphological information in agriculture. However, previous experiments were usually performed with several commercial devices on a single platform. Inadequate registration and synchronization among instruments often resulted in mismatch between spectral and 3D information of the same target. In addition, using slit-based spectrometers and point-based 3D sensors extends the working hours in farms due to the narrow field of view (FOV). Therefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information. Furthermore, fiber-reformatting imaging spectrometry (FRIS) is adopted to acquire the hyperspectral images. Test experiments are conducted for the verification of the system accuracy, and vegetation measurements are carried out to demonstrate its feasibility. The proposed system is an improvement in multiple data acquisition and has the potential to improve plant phenotyping.
高光谱测量和三维测量可以分别获取物体的内在物理化学性质和外部几何特征。这两类数据的结合能够为物体提供新的见解,在农业管理、植物表型分析、文化遗产保护和食品生产等领域受到了关注。目前,在农业中,多种传感器被集成到一个系统中以收集光谱和形态信息。然而,以往的实验通常是在单个平台上使用几种商用设备进行的。仪器之间登记和同步不足常常导致同一目标的光谱信息和三维信息不匹配。此外,由于视场(FOV)狭窄,使用基于狭缝的光谱仪和基于点的三维传感器会延长在农场的工作时间。因此,我们提出了一种结合立体视觉和光栅色散以同时获取高光谱和三维信息的高通量原型。此外,采用光纤重排成像光谱法(FRIS)来获取高光谱图像。进行测试实验以验证系统精度,并开展植被测量以证明其可行性。所提出的系统在多数据采集方面有所改进,并且具有改善植物表型分析的潜力。