Imaging sensors can extend phenotyping capability, but they require a system to handle high-volume data. The overall goal of this study was to develop and evaluate a field-based high throughput phenotyping system accommodating high-resolution imagers. The system consisted of a high-clearance tractor and sensing and electrical systems. The sensing system was based on a distributed structure, integrating environmental sensors, real-time kinematic GPS, and multiple imaging sensors including RGB-D, thermal, and hyperspectral cameras. Custom software was developed with a multilayered architecture for system control and data collection. The system was evaluated by scanning a cotton field with 23 genotypes for quantification of canopy growth and development. A data processing pipeline was developed to extract phenotypes at the canopy level, including height, width, projected leaf area, and volume from RGB-D data and temperature from thermal images. Growth rates of morphological traits were accordingly calculated. The traits had strong correlations (r = 0.54-0.74) with fiber yield and good broad sense heritability (H² = 0.27-0.72), suggesting the potential for conducting quantitative genetic analysis and contributing to yield prediction models. The developed system is a useful tool for a wide range of breeding/genetic, agronomic/physiological, and economic studies.

中文翻译：

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GPhenoVision：具有多模态成像功能的地面移动系统，用于棉花现场高通量表型分析。


成像传感器可以扩展表型分析能力，但它们需要一个系统来处理大量数据。本研究的总体目标是开发和评估可容纳高分辨率成像仪的基于现场的高通量表型系统。该系统由高间隙拖拉机以及传感和电气系统组成。该传感系统基于分布式结构，集成了环境传感器、实时运动GPS以及包括RGB-D、热成像和高光谱相机在内的多个成像传感器。定制软件采用多层架构开发，用于系统控制和数据收集。通过扫描具有 23 种基因型的棉田来评估该系统，以量化冠层生长和发育。开发了数据处理流程来提取冠层水平的表型，包括来自 RGB-D 数据的高度、宽度、投影叶面积和体积以及来自热图像的温度。相应地计算形态性状的生长率。这些性状与纤维产量具有很强的相关性（r = 0.54-0.74），并且具有良好的广义遗传力（H ² = 0.27-0.72），表明进行定量遗传分析并有助于产量预测模型的潜力。所开发的系统是广泛的育种/遗传、农艺/生理和经济研究的有用工具。