Abstract
Abstract. Hyperspectral and three-dimensional measurement can obtain the intrinsic physicochemical properties and external geometrical characteristics of objects, respectively. 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. And narrow field of view (FOV) extends the working hours in farms. Therefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information.
Highlights
The constantly increasing global population presents a tremendous challenge for agricultural production (Mulla, 2013)
Active sensors based on time-of-flight (TOF) or laser triangulation and passive sensors based on stereo vision or structure from motion (SFM) are common ways to acquire depth information
We mainly aim to develop an integrated prototype that combines stereo vision based on triangulation for depth information acquisition and grating dispersion for spectral data acquisition
Summary
The constantly increasing global population presents a tremendous challenge for agricultural production (Mulla, 2013). Active sensors based on time-of-flight (TOF) or laser triangulation and passive sensors based on stereo vision or structure from motion (SFM) are common ways to acquire depth information. Point-based sensors (lidar, ultrasonic transducer) employ a narrow FOV that usually results in the loss of the highest point of crops (Jiang et al, 2016) Depth cameras such as RGB-cameras offer a lowcost way to acquire 3D information (Bellasio et al, 2012), but due to the poor performance on sunny days, a shaded environment is required (Jiang et al, 2016). We mainly aim to develop an integrated prototype that combines stereo vision based on triangulation for depth information acquisition and grating dispersion for spectral data acquisition. Given that the system obtains data frame by frame, it can be applied for the simultaneous acquisition of the high throughput phenotyping of plants
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