Detecting the presence of invasive plant species: More quickly, cheaply and safely using AI and machine vision

入侵的非土著物种(INN)是被引入其原生区域以外地区的生物体，在那里它们威胁到生态系统。它们被认为是对全球生物多样性的五大威胁之一(IPBES，2019年)，并具有重大的经济影响，交通和公用事业等不同行业的公司花费了大量时间和资源来识别和消除它们。目前识别客栈存在的方法依赖于生态调查，这既耗时又昂贵，特别是在公路和铁路基础设施中。Keen AI、英国生态与水文学中心(CEH)和Time-Lapse Systems正在结合他们在人工智能(AI)、Inns和图像采集方面的专业知识，以提供一种更快、更高效的方法来进行此类调查。Keen AI在为国家电网等公司提供人工智能解决方案方面具有专业知识，有助于简化他们的视觉状况评估流程。CEH在入侵物种的研究方面有着长期的记录，并与物业转让部门一起开创了日本根结的图像识别服务。延时系统是为专业应用捕捉图像的专家。我们互补的经验、技能和资源为开发一种新型的人工智能平台来检测入侵物种的存在提供了机会。目前为客栈调查区域的解决方案包括派遣生态学家进行人工调查，这既耗时又昂贵，或者手动审查从安装在无人机或飞机上的高清晰度数码相机拍摄的照片。使用人工智能技术，我们的建议将减少进行此类生态调查所需的时间，为客户节省成本和时间，并提供特定位置的信息来支持决策和管理行动。我们的项目愿景是评估开发人工智能平台的可行性，以检测线性基础设施中入侵植物物种的存在。这项创新将提供一种快速、高质量的植被调查方法，这将为我们的客户节省成本和时间，并使我们更好地了解市场对此类人工智能创新的需求。该项目将有四个关键目标：1.收集足够质量的植被图像；2.训练人工智能算法以识别图像数据集中的Inn；3.处理大量图像以在地理空间上定位Inn；以及4.对AI模型的性能进行评估。

Invasive Non-Native Species (INNS) are organisms introduced into areas outside their native region where they then threaten ecosystems. They are regarded as one of the top five threats to biodiversity worldwide (IPBES, 2019), as well as having significant economic impacts, with companies in various sectors such as transport and utilities spending considerable time and resources to identify and remove them. Current methods for identifying the presence of INNS rely on ecological surveys, which are time consuming and costly, especially within road and rail infrastructure. Keen AI, the UK Centre for Ecology and Hydrology (CEH) and Time-Lapse Systems are combining their expertise in Artificial Intelligence (AI), INNS and image collection to provide a faster and more efficient method of conducting surveys of this kind.Keen AI has expertise in providing AI solutions to companies such as National Grid, helping to streamline their visual condition assessment process. CEH have a long track record of research on invasive species and are pioneering image recognition services for Japanese Knotweed with the conveyancing sector. Time-Lapse systems are experts in capturing imagery for specialist applications. Our complementary experience, skills and resources provide an opportunity to develop a novel AI platform for detecting the presence of invasive species.Current solutions for surveying an area for INNS include sending ecologists to perform a manual survey, which is time-consuming and costly, or the manual review of photographs taken from high definition digital cameras attached to drones or planes. Using AI technology, our proposal would reduce the time it takes to conduct an ecological survey of this kind, producing cost and time savings for the customer, and providing location specific information to support decision-making and management actions.Our project vision is to assess the feasibility of developing an AI platform for detecting the presence of invasive plant species within linear infrastructure. This innovation will provide a rapid, high quality vegetation survey methodology, which will result in cost and time savings for our customers, and result in an increased understanding of market requirements for an AI innovation of this type. The project will have four key objectives:1. Collection of vegetation imagery of sufficient quality;2. Training of AI algorithms to identify INNS in the image dataset;3. Processing high volumes of images to locate INNS geospatially; and4. Evaluation of the AI model performance.