SBIR Phase I: CAS: Tree Root Quality Inspection System with Noninvasive Evaluation

这个小型企业创新研究(SBIR)第一阶段项目提高了苗木的质量、价值、效益和寿命，使它们能够在人们生活的环境中茁壮成长。树木对人类健康、生态系统服务和气候缓解有许多好处，当它们长期茁壮成长时，就会充分发挥其潜力。目前的方法不能充分解决树根质量问题。通过将根部检查现代化，这项技术不仅将提高行业标准和提升经济竞争力，还将在全球范围内促进环境管理。有了这项技术，树艺师和种植者可能能够识别地上树木的根部缺陷，并采取纠正措施，促进这些重要的树木资产成长为成熟所需的高质量根系。这个SBIR第一阶段项目的重点是开发一种三维、无损的探地雷达(GPR)计算机断层扫描(CT)系统，该系统具有尖端的软件分析功能，用于检查和评估苗木容器栽培的根系质量。这一创新是基于这样一种理解，即探地雷达信号是由活组织和周围土壤之间的巨大差异产生的。这项技术可以检测到严重的根系缺陷，如果在种植树木之前不加以纠正，这些缺陷可能会导致树木过早死亡。数据将在一种创新设备的帮助下收集，该设备使用商业探地雷达系统无缝捕获容器种植的树木的3D根数据，该系统带有无线天线，作为容器周围的第二层，模拟X射线CT扫描仪的精度。一种新的根质量分类模型将为开发根分析软件程序提供信息，以便为机器学习模型建立初始探地雷达数据集，并随后采用主动学习方法。最初的实验将集中在数量足够多的一到两种树上。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Small Business Innovation Research (SBIR) Phase I project improves the quality, value, benefits, and life span of nursery stock trees, so they will grow and thrive in the landscape where people live. Trees, with their myriad benefits for human health, ecosystem services, and climate mitigation achieve their full potential when they thrive long term. Current methods fail to adequately address tree root quality. By modernizing root inspection, this technology will not only improve industry standards and boost economic competitiveness, but also promote environmental stewardship on a global scale. With this technology, arborists and growers may be able to identify above-ground tree root defects and take corrective action to promote good quality root systems that are needed for these important tree assets to grow to maturity. This SBIR Phase I project focuses on the development of a 3-dimensional, non-destructive, ground penetrating radar (GPR) computed tomography (CT) system with cutting-edge software analytics to inspect and assess the quality of container-grown root systems in nursery stock trees. This innovation is based on the understanding that the GPR signals are generated by the large differentials between live tissues and the surrounding soil. The technology detects serious root system defects that could cause early tree mortality if not corrected before the tree is planted. The data will be collected with the help of an innovative apparatus designed to seamlessly capture 3D root data from container-grown trees using a commercial GPR system with a wireless antenna that works as a secondary layer around the container, emulating the precision of an X-ray CT scanner. A novel root quality classification model will inform the development of a root analysis software program to build an initial GPR dataset for the machine learning model and subsequently to adopt an active learning approach. Initial experiments will focus on a sufficiently large number of one or two species of trees.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.