AIPPN: Interpretable AI enabled Molecular Identification Pipeline for Plant-parasitic Nematodes

植物寄生线虫（PPNs）是温带、亚热带和热带农业系统中许多作物的破坏性害虫。它们取食造成的损害导致全球作物产量损失14%。此外，ppn可以与真菌和细菌病原体形成破坏性的疾病复合物，并作为病毒载体。有关管理的决定，例如杀线虫剂的施用、生物农药的使用、耐药/耐药品种的选择和轮作长度，通常是根据土壤测试来确定存在的属或种的密度。在土壤取样后，提取的线虫使用分类特征和二分类键进行评估。qPCR等分子检测也可用于线虫诊断，但主要用于鉴定感兴趣的单个物种。为了确定土壤样本中自由生活的ppn的种类，需要训练有素的分类学家，通常至少有3-4年的经验。不幸的是，这样的专家正在减少，培训提供者也很少。囊线虫由经济上重要的球形线虫属和异球形线虫属组成，通常在马铃薯和甜菜等作物中进行评估。马铃薯包囊线虫(globodera_; rostochiensis_；在英格兰和威尔士用于马铃薯生产的土地中，有48%的土地上都有白斑，每年造成的损失达26- 5000万英镑。目前对马铃薯囊肿线虫的评估主要是基于有能力的分析人员对囊肿形状（雌性线虫）的形态学特征。在最近的BBSRC项目中，我们通过构建基于cnn的PPN检测模型（BBSRC项目BB/X01200X/1）开发了一个早期解决方案来解决这个问题。在此，我们建议在该项目成功的基础上，扩展可解释的人工智能模型，以提高PPN识别、分类和量化的准确性和鲁棒性。该项目将通过开发、培训和测试可解释的基于人工智能的自动图像识别技术，为这一技术技能差距提供变革性解决方案，该技术将作为标准PPN识别系统的替代方案。特别是，该技术能够：1)从混合PPN样本中提取形态PPN特征；2)获取PPN特征在PPN识别中的灵敏度；3)实现准确的PPN分类和量化。它将建立在联盟现有资源（BB/X01200X/1）的基础上，包括：2K PPN图像（SparkSoft），世界领先的害虫检测模型（pest net; UoS），农艺和害虫管理（ADAS）。本研究的成果将代表线虫快速筛选的重要进步，并具有许多研究应用。

Plant parasitic nematodes (PPNs) are destructive pests of many crops in temperate, sub-tropical, and tropical agricultural systems. Damage caused by their feeding results in a 14% loss in global crop yield. Furthermore, PPNs, can form damaging disease complexes with fungal and bacterial pathogens and act as virus vectors. Decisions on management, such as nematicide application, biopesticide use, choice of resistant/tolerant varieties and rotation length is frequently based on soil testing to determine the density of genera or species present. Following soil sampling, the extracted nematodes are assessed using taxonomic characteristics and dichotomous keys. Molecular tests such as qPCR may also be used in nematode diagnosis but these are mainly used to identity individual species of interest. To determine the array of free living PPNs in a soil sample, trained taxonomists, typically with at least 3-4 years of experience, are needed. Unfortunately, such specialists are in decline and there are few training providers.Cyst nematodes, consisting of the economically important genera _Globodera_ and _Heterodera_ spp., are routinely assessed in crops such as potatoes and sugar beet. The potato cyst nematodes (_Globodera_ _rostochiensis_ and _G. pallida_) occur in 48% of the land used for potato production in England and Wales and are associated with annual losses amounting to £26-50M. Current assessment of potato cyst nematode is mainly based upon the morphological characterisation of cyst shape (female nematodes) by competent analysts.In a recent BBSRC project we developed an early-stage solution to this problem by building a CNN-based PPN detection model to recognise important PPN genera from soil (BBSRC project BB/X01200X/1). Here, we propose to build on the success of this project by expanding interpretable AI models to improve the accuracy and robustness of PPN identification, classification, and quantification.This project will provide a transformative solution for this technical skills gap by developing, training and testing an interpretable AI based automatic image recognition technique that will act as an alternative to the standard PPN identification system. Particularly, this technique is able to: 1) extract morphological PPN features from mixed PPN samples; 2) access PPN features' sensitivity in PPN recognition; 3) achieve accurate PPN classification and quantification.It will build on existing resources in the consortium (BB/X01200X/1), including: 2K PPN images (SparkSoft), a world-leading pest detection model (PestNet; UoS), agronomic and pest management (ADAS),Outputs of this study would represent an important advancement in the rapid screening of nematodes and have many research applications.