Detection of fungal plant pathogen spores using Advanced AI and Imaging (AAII)

使用高级人工智能和成像 (AAII) 检测真菌植物病原体孢子

• 批准号: 99527
• 金额: $ 14.01万
• 依托单位: PLATFORM KINETICS LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=99527
• 关键词: Detection; fungal; plant; pathogen; spores

The project aims to develop an image-based identification system for air-borne fungal spores of important UK crop pathogens, using image analysis and artificial intelligence approaches in an automated, robotic sampling device. This method will provide real-time, continuous results of monitoring air samples, providing data on inoculum levels for multiple pathogens and allergens. The key objective is to automate the impaction of airborne spores onto an adhesive transparent film, which will be moved past a microscope camera, focusing on three separate planes and with innovative lighting, spectroscopy, image recognition and AI used to classify spores to indicate abundance of selected pathogens. Test samples produced by Rothamsted Research in lab conditions and real farm-based air samples will be used for training and validation of the system.The method will provide rapid identification and quantification of air-borne spores, aiming initially at three key diseases of concern in the UK. Image analysis and artificial intelligence methods will be developed to 'learn' to identify the pathogen species from known spore samples, and the algorithms developed will enable rapid identification of these pathogen spores in field samples. The main benefits will be an accurate and fast indication of infection risk to crops for a range of diseases, which will help with disease management, especially decisions on fungicide applications. Many crops are treated more often than is necessary so as part of integrated pest(and disease) (IPM) and to reduce agricultural emissions, the project will potentially reduce fungicide applications while improving disease control. Added benefits could be reduced spray costs for farmers and a reduced rate of development of resistance by pathogens. In addition, there will be beneficial effects on biodiversity and the environment from limiting fungicide use.The project is led by Platform Kinetics Limited, an innovative SME specialising in deployable, automated scientific instrumentation using imaging, robotics, and artificial intelligence. The project will produce a lab-prototype system and will be tested by Rothamsted using a realistic experimental set-up. Rothamsted Research will use existing air samplers to take samples for image analysis and design of the system and will validate results by lab-testing duplicate air samples. Private investment will be used to follow on fund this technology to commercially exploitation and integration with crop management software.

该项目旨在开发一种基于图像的识别系统，用于识别英国重要作物病原体的空气传播真菌孢子，在自动化机器人采样设备中使用图像分析和人工智能方法。该方法将提供实时、连续的空气样本监测结果，提供多种病原体和过敏原的接种水平数据。其关键目标是将空气中的孢子自动撞击到粘性透明膜上，该膜将通过显微镜摄像机移动，聚焦于三个独立的平面，并使用创新的照明，光谱学，图像识别和AI对孢子进行分类，以指示所选病原体的丰度。Rothamsted Research在实验室条件下生产的测试样品和真实的农场空气样品将用于系统的培训和验证。该方法将提供空气传播孢子的快速识别和定量，最初针对英国关注的三种关键疾病。将开发图像分析和人工智能方法，以“学习”从已知孢子样本中识别病原体物种，开发的算法将能够快速识别田间样本中的这些病原体孢子。主要的好处是准确和快速地指示作物对一系列疾病的感染风险，这将有助于疾病管理，特别是关于杀真菌剂应用的决定。许多农作物的处理频率超出了必要的范围，因此作为综合害虫（和疾病）（IPM）的一部分并减少农业排放，该项目将有可能减少杀真菌剂的应用，同时改善疾病控制。额外的好处可以减少农民的喷洒成本，降低病原体产生抗药性的速度。该项目由Platform Kinetics Limited领导，该公司是一家创新型中小企业，专门从事使用成像、机器人和人工智能的可部署自动化科学仪器。该项目将产生一个实验室原型系统，并将由Rothamsted使用现实的实验装置进行测试。Rothamsted Research将使用现有的空气采样器采集样本进行图像分析和系统设计，并将通过实验室测试重复的空气样本来验证结果。私人投资将用于继续资助这项技术的商业开发和与作物管理软件的集成。