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Abstract：
Counting nematodes is a labor-intensive and time-consuming task, yet it is a pivotal step in various quantitative nematological studies; preparation of initial population densities and final population densities in pot, micro-plot and field trials for different objectives related to management including sampling and location of nematode infestation foci. Nematologists have long battled with the complexities of nematode counting, leading to several research initiatives aimed at automating this process. However, these research endeavors have primarily focused on identifying single-class objects within individual images. To enhance the practicality of this technology, there\'s a pressing need for an algorithm that cannot only detect but also classify multiple classes of objects concurrently. This study endeavors to tackle this challenge by developing a user-friendly Graphical User Interface (GUI) that comprises multiple deep learning algorithms, allowing simultaneous recognition and categorization of nematode eggs and second stage juveniles of Meloidogyne spp. In total of 650 images for eggs and 1339 images for juveniles were generated using two distinct imaging systems, resulting in 8655 eggs and 4742 Meloidogyne juveniles annotated using bounding box and segmentation, respectively. The deep-learning models were developed by leveraging the Convolutional Neural Networks (CNNs) machine learning architecture known as YOLOv8x. Our results showed that the models correctly identified eggs as eggs and Meloidogyne juveniles as Meloidogyne juveniles in 94% and 93% of instances, respectively. The model demonstrated higher than 0.70 coefficient correlation between model predictions and observations on unseen images. Our study has showcased the potential utility of these models in practical applications for the future. The GUI is made freely available to the public through the author\'s GitHub repository (https://github.com/bresilla/nematode_counting). While this study currently focuses on one genus, there are plans to expand the GUI\'s capabilities to include other economically significant genera of plant parasitic nematodes. Achieving these objectives, including enhancing the models\' accuracy on different imaging systems, may necessitate collaboration among multiple nematology teams and laboratories, rather than being the work of a single entity. With the increasing interest among nematologists in harnessing machine learning, the authors are confident in the potential development of a universal automated nematode counting system accessible to all. This paper aims to serve as a framework and catalyst for initiating global collaboration toward this important goal.
摘要：
计算线虫是一项劳动密集型且耗时的任务，然而，它是各种定量线虫研究的关键步骤；准备盆栽中的初始种群密度和最终种群密度，与管理相关的不同目标的微绘图和田间试验，包括线虫侵染病灶的采样和位置。线虫学家长期以来一直在与线虫计数的复杂性作斗争，导致了几项旨在自动化这一过程的研究举措。然而,这些研究工作主要集中在识别单个图像中的单类对象。为了增强这项技术的实用性，迫切需要一种算法，该算法不仅可以同时检测多类对象，而且可以对其进行分类。本研究致力于通过开发包含多种深度学习算法的用户友好的图形用户界面（GUI）来应对这一挑战。允许同时识别和分类线虫卵和Meloidogynespp的第二阶段少年。使用两种不同的成像系统总共生成了650张鸡蛋图像和1339张青少年图像，产生8655个鸡蛋和4742个使用边界框和分割进行注释的Meloidogyne少年，分别。深度学习模型是通过利用称为YOLOv8x的卷积神经网络（CNN）机器学习架构开发的。我们的结果表明，在94％和93％的实例中，这些模型正确地将卵识别为卵，将Meloidogyne幼体识别为Meloidogyne幼体，分别。该模型在模型预测与未见图像的观察之间显示出高于0.70的系数相关性。我们的研究展示了这些模型在未来实际应用中的潜在实用性。GUI通过作者的GitHub存储库（https://github.com/bresilla/nematode_counting）免费提供给公众。虽然这项研究目前集中在一个属，有计划扩大GUI的能力，包括其他具有经济意义的植物寄生线虫属。实现这些目标,包括提高不同成像系统的模型精度，可能需要多个线虫团队和实验室之间的合作，而不是单一实体的工作。随着线虫学家对利用机器学习的兴趣日益浓厚，作者对所有人都可以使用的通用自动线虫计数系统的潜在开发充满信心。本文旨在作为启动实现这一重要目标的全球合作的框架和催化剂。
