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

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。植物诊所为种植者和他们的顾问提供一整套服务。一项关键服务是评估土壤样品的PPN。PPN筛选是通过耗时的分类鉴定进行的，这依赖于分类专业知识和数年的培训。训练有素的线虫学家供不应求，这引起了业界的关注，因为准确可靠的PPN鉴定是影响农艺决策的关键因素。PPN影响多种作物，并可能破坏产量，损失高达35% （AHDB, 2017）。种植者在种植前筛选田地，以确定和量化PPN，以帮助决定种植/避免种植的作物，指导品种选择，并建议控制策略。PPN筛选每季每片田地的成本为70英镑，这是一笔可观的成本。更快速、更具成本效益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。已经开发了用于线虫识别的人工智能算法；然而，大多数只鉴定出一种PPN属（Bogale等人，2020；Akintayo等人，2018）。NemaRecognition将通过对多个PPN属的识别，代表一种创新的最先进的PPN评估解决方案，并通过进一步开发，将成为首批为英国种植者提供植物健康服务的基于机器学习的技术之一。对其他农业害虫（如昆虫）也开发了图像识别技术。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，现场样本检测，通过视频捕获识别，验证，基准测试以及选择适当的模型。NemaRecognition将带来无数好处，包括降低种植者成本（通过植物诊所节省成本），在因分类学技能短缺而限制服务的地区增加PPN筛查的可及性，以及作为帮助解决行业内分类学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项目将展示该技术对PPN检测的可行性和适用性，也将代表为其他土壤生物开发类似创新的概念证明，在土壤健康服务的增长领域具有巨大潜力。

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.