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

外来入侵物种（INNS）是指被引入其原生地区以外的地区，然后威胁生态系统的生物。它们被视为全球生物多样性的五大威胁之一（IPBES, 2019），并具有重大的经济影响，运输和公用事业等各个行业的公司花费了大量的时间和资源来识别和消除它们。目前确定是否存在国际生态系统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内。Keen AI，英国生态和水文中心（CEH）和延时系统正在结合他们在人工智能（AI）， INNS和图像收集方面的专业知识，提供一种更快，更有效的进行此类调查的方法。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，有助于简化其视觉状态评估过程。CEH在入侵物种研究方面有着悠久的历史，并在日本虎杖的图像识别服务方面具有开创性。延时系统是为专业应用捕获图像的专家。我们互补的经验、技能和资源为开发一个新的人工智能平台来检测入侵物种的存在提供了机会。目前，INNS对一个地区进行调查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，或者对无人机或飞机上的高清数码相机拍摄的照片进行人工审查。利用人工智能技术，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，以支持决策和管理行动。我们的项目愿景是评估开发一个人工智能平台的可行性，用于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，并使我们更加了解这种类型的人工智能创新的市场需求。该项目将有四个主要目标：1。1 .收集足够质量的植被影像；2 .训练识别图像数据集中INNS的AI算法；对大量图像进行处理，实现INNS的地理空间定位；and4。人工智能模型性能评估。

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.