An affordable stereoscopic camera array system for capturing real-time 3D responses to vegetation dense environments

为了养活不断增长的全球人口，到2050年，主要作物的产量需要提高约60%。由于植物科学和农业技术界分析现有植物遗传资源与环境相互作用的能力不足，目前阻碍了这一重大挑战的实现。植物表型是一门将基因组学与植物生态生理学和农学联系起来的新兴科学。植物在整个生命周期（表型）中建筑特征的集合是遗传背景（基因型）和植物发育的物理世界（环境）之间动态相互作用的结果。这些相互作用决定了植物的性能和生产力（以累积生物量和商业产量衡量）以及资源利用效率。植物表型的一个关键参数是三维形状——植物结构——它反映了植物对环境条件的适应性，如光的数量和质量，以及温度。我们将把植物科学家、图像捕捉和机器视觉专家聚集在一起，开发一个负担得起的、用户友好的系统，以推进生物学中最重要的问题之一，我们提高植物生产力的能力。我们将结合一个强大的光度立体方法和软件，使用优化的学习算法，将产生标准化的输出，可以很容易地与其他生物数据集跨尺度集成。

To feed our growing global population, the productivity of staple crops will require an increase in yields of ca. 60% by 2050. Realisation of this significant challenge is currently hampered by insufficient capacity of the plant science and Agri-Tech communities to analyse existing plant genetic resources for their interaction with the environment. Plant phenotyping is an emerging science that links genomics with plant ecophysiology and agronomy. The collection of architectural traits of a plant throughout the life cycle (the phenotype) is a result of dynamic interactions between the genetic background (the genotype) and the physical world in which the plant develops (the environment). These interactions determine plant performance and productivity measured as accumulated biomass and commercial yield, and resource use efficiency. A critical parameter of a plant's phenotype is the shape in three dimensions - the plant architecture - which reflects the adaption of a plant to environmental conditions, such as light quantity and quality, and temperature. We will bring together plant scientists, and image capture and machine vision experts to develop an affordable and user-friendly system for progressing on one of the most important issues in biology, our capacity to enhance the productivity of plants. We will combine a robust photometric stereo approach with software using optimised learning algorithms that will produce standardised outputs that can be readily integrated across scales with other biological data sets.