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

经济实惠的立体相机阵列系统，用于捕捉植被茂密环境的实时 3D 响应

• 批准号: BB/N02334X/1
• 负责人: Alistair McCormick
• 金额: $ 19.32万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FN02334X%2F1
• 关键词: affordable; stereoscopic; camera; array; system

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.

为了养活不断增长的全球人口，主要农作物的生产力将需要增加大约到 2050 年将达到 60%。目前，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，阻碍了这一重大挑战的实现。植物表型分析是一门新兴科学，它将基因组学与植物生态生理学和农学联系起来。植物在整个生命周期中的结构特征（表型）的集合是遗传背景（基因型）与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以及资源利用效率来衡量）。植物表型的一个关键参数是三个维度的形状（植物结构），它反映了植物对环境条件（例如光量和质量以及温度）的适应。我们将汇集植物科学家、图像捕捉和机器视觉专家，开发一种价格实惠且用户友好的系统，以在生物学中最重要的问题之一——提高植物生产力的能力——上取得进展。我们将把强大的光度立体方法与使用优化学习算法的软件结合起来，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。

项目成果

BRDF of human skin in the visible spectrum

可见光谱中人体皮肤的 BRDF

• DOI: http://dx.10.1108/sr-11-2016-0258
• 发表时间: 2017
• 期刊: Sensor Review
• 影响因子: 1.6
• 作者: Sohaib A

A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth.

基于光度立体的 3D 成像系统，使用计算机视觉和深度学习来跟踪植物生长。

• DOI: http://dx.10.1093/gigascience/giz056
• 发表时间: 2019
• 期刊: GigaScience
• 影响因子: 9.2
• 作者: Bernotas G

Leveraging Multiple Datasets for Deep Leaf Counting

利用多个数据集进行深度叶子计数

• DOI: http://dx.10.1109/iccvw.2017.243
• 发表时间: 2017
• 作者: Dobrescu A

A "Do-It-Yourself" phenotyping system: measuring growth and morphology throughout the diel cycle in rosette shaped plants

“DIY”表型系统：测量莲座状植物整个昼夜周期的生长和形态

• DOI: http://dx.10.1186/s13007-017-0247-6
• 发表时间: 2017
• 期刊: Plant Methods
• 影响因子: 5.1
• 作者: Dobrescu A

Citizen crowds and experts: observer variability in image-based plant phenotyping.

公民群体和专家：基于图像的植物表型分析中的观察者变异性。

• DOI: http://dx.10.1186/s13007-018-0278-7
• 发表时间: 2018
• 期刊: Plant methods
• 影响因子: 5.1
• 作者: Giuffrida MV