Arable - NemaRecognition: An AI-and molecular-driven pipeline for throughput plant parasitic nematode recognition

Arable - NemaRecognition：人工智能和分子驱动的管道，用于植物寄生线虫识别

• 批准号: BB/X01200X/1
• 负责人: Po Yang
• 金额: $ 6.42万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX01200X%2F1
• 关键词: Arable; NemaRecognition; AI; molecular; driven

NemaRecognition will be a machine learning based automatic image recognition technique capable of real-time detection of PPNs using digital images/videos.Plant clinics carry out a suite of services for growers and their advisers. A key service is the assessment of soil samples for PPN. PPN screening is carried out through time-intensive taxonomic identification, this is reliant on taxonomic expertise and several years of training. Trained nematologists are in short supply, causing concern in the industry as accurate and reliable identification of PPN is a critical factor influencing agronomic decisions.PPN affect various crops and can devastate yields, with losses up to 35% (AHDB, 2017). Growers screen fields prior to planting to identify and quantify PPN to help decide on the crop to be planted/avoided, guide variety choice, and advise control strategies. PPN screening can cost £70 per field per season and represents a substantial cost. More rapid, cost-effective assessment methods would represent a cost saving to growers.Alternatives, such as molecular-based tests, have been developed but have substantial shortcomings in accuracy, breadth of use, and grower-confidence. AI algorithms have been developed for nematode identification; however, the majority only identify one PPN genera (Bogale et al., 2020; Akintayo et al., 2018). NemaRecognition would represent an innovative state-of-the-art solution for PPN assessment by providing recognition for multiple PPN genera, and through further development would become one of the first machine learning-based techniques providing plant health services to UK growers.Image-recognition techniques have been developed for other agricultural pests (e.g., insects). However, significant challenges to producing a transformative PPN recognition system using machine learning techniques remain, including recognition of a range of PPN genera, detection in field samples, recognition through video-capture, validation, benchmarking, and selection of appropriate models.NemaRecognition would have myriad benefits, including reduced grower costs (passed down through plant clinic cost savings), increased accessibility to PPN screening in regions where services are inhibited by a taxonomic skills shortage, and as a training tool to help address the taxonomic skills shortage within the industry. Global challenges have been influential in creating this opportunity: UK net-zero farming, EU Sustainable Use Directive, UK path to sustainable farming.The NemaRecognition project will showcase the feasibility and applicability of this technology toward PPN detection and would also represent proof-of-concept for developing similar innovations for other soil-dwelling organisms, with significant potential in the growing area of soil health services.

NemaRecognition将是一种基于机器学习的自动图像识别技术，能够使用数字图像/视频实时检测PPN。植物诊所为种植者及其顾问提供一系列服务。一项关键服务是为PPN评估土壤样本。PPN筛选是通过时间密集的分类鉴定进行的，这依赖于分类学专业知识和数年的培训。训练有素的作物学家供不应求，这引起了行业的担忧，因为准确可靠地识别PPN是影响农艺决策的关键因素。PPN影响各种作物，并可能导致产量下降，损失高达35%（AHDB，2017）。种植者在种植前筛选田地，以识别和量化PPN，以帮助决定要种植/避免的作物，指导品种选择，并建议控制策略。PPN筛查每个赛季每个场地的费用为70英镑，这是一笔可观的费用。更快速、成本效益高的评估方法将为种植者节省成本。替代方法，如基于分子的测试，已经开发出来，但在准确性、使用范围和种植者信心方面存在重大缺陷。人工智能算法已被开发用于线虫识别;然而，大多数算法仅识别一种PPN属（Bogale等人，2020; Akintayo等人，2018年）。NemaRecognition将通过提供对多个PPN属的识别来代表PPN评估的创新性最先进的解决方案，并且通过进一步的开发将成为为英国种植者提供植物健康服务的首批基于机器学习的技术之一。昆虫）。然而，使用机器学习技术生产变革性PPN识别系统仍然存在重大挑战，包括识别一系列PPN属，田间样本检测，通过视频捕获识别，验证，基准测试和选择适当的模型。NemaRecognition将带来无数好处，包括降低种植者成本（通过节省植物诊所成本传递），在分类技能短缺抑制服务的地区增加PPN筛查的可及性，并作为帮助解决行业内分类技能短缺的培训工具。全球挑战对创造这一机会产生了影响：英国净零农业，欧盟可持续利用指令，英国可持续农业之路NemaRecognition项目将展示该技术在PPN检测方面的可行性和适用性，也将代表为其他土壤生物开发类似创新的概念验证，在土壤健康服务领域具有巨大潜力。