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NemaRec: a deep learning-based web application for nematode image
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identification and ecological indices calculation
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Xue Qing a #, Yihao Wang a, b #, Xuequan Lu b, Xuan Wang a, Hongmei Li a*, Xiaojun Xiec*
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a Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
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b Deakin University, Waurn Ponds, VIC 3216, Australia
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c College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210095, China
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# These authors contribute equally to this work.
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*Corresponding author, E-mail address: lihm@njau.edu.cn, xxj@njau.edu.cn
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Dr. Hongmei Li, Dr. Xiaojun Xie
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Telephone: +86 25 84396432
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Fax: +86 25 84395240
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Mailing address: Department of Plant Pathology, Nanjing Agricultural University, Tongwei
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Road No. 6, Xuanwu District, Nanjing 210095, China
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ABSTRACT
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Nematodes are ubiquitous in soil representing different trophic levels and occupying a central
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position in the detritus food web. Nematodes have been widely used for biomonitoring of soil
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quality and health. However, their application in bio-indicator is limited due to the taxonomic
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identification is laborious, and largely relying on morphological characters which require
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substantial prior nematology experiences. In present study, we developed the web application
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NemaRec using the I-Nema dataset. NemaRec adopts the deep convolutional neural networks
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approach for nematode image identification, incorporate I-Nema dataset in a user friendly
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interface, and further extends it to the calculation of feeding types, c-p values, MI and PPI
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indices for environment evaluation. Within 19 studied genera, the model can properly identify
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up to 60% genera, 76% of c-p values and 76% feeding types in specimen-based dataset
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(images taken from microscopy), and 94%-97% in augmented dataset (image treated with
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random flip and Gaussian noise). The pipeline was further incorporated into a user-friendly
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web application NemaRec. NemaRec offers high-throughput online identification while
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simultaneously collect images uploaded by users for future modeling training. To our
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knowledge this is the first soil nematode image identification system using deep learning
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approach. NemaRec is available http://8.140.109.225:8866/).
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Keywords: Maturity index; Machine learning; Soil ecology; Biodiversity; Convolutional
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neural networks
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1. Introduction
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Nematodes are an evolutionarily successful group of organisms occupying nearly all
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habitats that provide available organic carbon sources. With different feeding behavior and
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life strategies, nematodes are classified to at least five functional or trophic groups (Yeates et
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al., 1993) and placed in a central position in the detritus food web (Moore and de Ruiter,
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1991). Nematodes are considered as useful ecological indicators due to several unique
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attributes (Freckman, 1988). Based on reproductive potential from explosive opportunists to
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conservative survivalists, families of nematodes can be ordered on a colonizer–persister (c-p)
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scale. The maturity index (MI) and plant-parasite index (PPI) are consequently calculated
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based on the weighted mean c-p value of the individuals.
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The MI and PPI indices have been widely used to evaluate soil health or condition
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(Yeates et al., 1993; Neher, 2001; Ferris and Bongers, 2006; Wilson and Khakouli-Duarte,
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2009), for example, a google scholar search of “nematode, environmental indicators” returned
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41,600 items up to January of 2020. These indices are based on the taxonomic identification
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which largely relies on morphological characters. This is practically laborious as ecological
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studies are typically large in samples size and rich in nematode species. Moreover, the high
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phenotypic plasticity (Coomans, 2002; Nadler, 2002), the vague diagnostic characters (Erwin,
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1982; Derycke et al., 2008), and frequently encountered juveniles (Anderson, 2000) upgrade
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the difficulties in identification and thus a well-established experience and expertise is hence
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essential. Given the number of taxonomists are rapidly declining worldwide, the nematode
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taxonomic identification often become the bottleneck for the application of these ecological
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indicators. With the rapidly declining cost and improved availability of genetic sequencing
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and related technologies, molecular barcoding holds great promise as a tool to simplify and
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standardize species identification (Hebert et al., 2003). The molecular barcoding further
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evolved as metabarcoding technique for batch evaluation of environmental samples, and this
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method has been proofed as a powerful tool in resolving soil nematode community
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(Porazinska et al., 2009; Porazinska et al.,2010). However, due to the PCR bias, lack of
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effective primers, and the requirement in bioinformatics experiences, their application remain
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an alternative choice for nematode community study.
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Beside taxonomic identification, manual calculation of indices are usually performed in
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Microsoft Excel or other spreadsheet software and may require manual adjustment of
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formulae and data format during the process. Although a computational SAS code (Neher and
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Campbell, 1996) and a more user-friendly automatic program were developed
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(Sieriebriennikov, 2014), they all need manual taxonomic identification and preparation of
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data sheet.
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Image-based automatic identifications have shown promising results in various computer
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vision problems including the nematode detection and counting (Silva et al., 2003; Holladay,
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2016). Deep learning methods have been introduced to this task driven by the success of the
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Convolutional Neural Networks (CNNs). Several efforts have been made using CNNs (Chou
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et al., 2017; Javer et al., 2019; Liu et al., 2018; Chen et al., 2020; Wang et al., 2020). However,
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these applications are predominantly designed for model organism Caenorhabditis elegans or
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concentrated on image stacking, and limited in detection of behavior, age, development stages
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and morphological adaptation to stress. Recently, CNNs model and nematode images dataset
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I-Nema (Lu et al., 2021) was created. However, I-Nema focus on dataset, with all source as
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command code, limit their application among biologists. In present study we aimed to
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develop an automatic ecological indices calculation pipeline by using CNN-based deep
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learning in image identification, and incorporated it in a user friendly interface. To our
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knowledge this is the first soil nematode image identification system using deep learning
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approach.
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2. Material and methods
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2.1. Sample collection and nematode identification
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Soil samples were collected from various ecosystems in China. Nematodes were extracted
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from soil samples using a Baermann tray, concentrated using a 500 mesh sieve (25 µm
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opening). After removing water, nematodes were manually picked up, fixed with 4% formalin
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and gradually transferred to anhydrous glycerin (Seinhorst, 1959). Slides were examined and
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photographed using an Olympus BX51 DIC Microscope (Olympus Optical, Tokyo, Japan),
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equipped with an Olympus C5060Wz camera. Specimens were identified to genus level based
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on morphology using available keys (e.g. Andrassy, 2005; Andrassy, 2007). For each studied
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genera, 2-3 representatives were further examined by 28S rRNA sequences to confirm the
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identity. Briefly, nematode was kill by heat and mounted in a temperate slide for microscopy
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inspection. DNA of single individual was freshly extracted by adding 10 µL solution of 0.05N
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NaOH and 1 µL 4.5% Tween 20, incubating for 15 min at 95°C. Then 30 µL of
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double-distilled water was added to each DNA sample, and subsequently amplified in PCR
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using primer sets D2A (5’-ACA AGT ACC GTG AGG GAA AGT TG-3’) and D3B (5’-TCG
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GAA GGA ACC AGC TAC TA-3’) (De Ley et al., 1999).
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A total of 2769 individuals belonging to 19 genera (Acrobeles, Acrobeloides,
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Amplimerlinius, Aphelenchoides, Aporcelaimus, Axonchium, Discolaimus, Ditylenchus,
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Dorylaimus, Eudorylaimus, Helicotylenchus, Mesodorylaimus, Miconchus, Mylonchulus,
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Panagrolaimus, Pratylenchus, Pristionchus, Rhabditis, and Mesocriconema) were
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photographed, resulting 24 to 347 images per genus. These genera were selected as they are
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cosmopolitan in soil, covering all nematode trophic guilds, and presenting in a large
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population that allow us to acquire sufficient images for model training. Both juveniles and
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adult stages were included in images, and head regions were given a higher priority, as they
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involve more informative taxonomic characters. Tail, middle body regions and overall body
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shape were also photographed, since these regions are also informative for species recognition
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and easy to locate. Images were taken at three image planes, with magnification of 100X
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objective for head and tail, and 10X for overall body.
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2.2. Image pre-processing
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The acquired images were pre-processed to facilitate the upcoming training process. The
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images were gray-scale transformed to keep the consistency between each image, followed by
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the edge detection. Canny edge detection algorithm was used to crop the region of interest
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(ROI), which allowing nematodes be separated from the background while maximum
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valuable information being preserved (Fig. 1). Briefly, the edge detector firstly computes the
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horizontal and vertical coordinates of the ROI, then the nematode structures are automatically
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cropped from the original image. After the cropping and Gray-scale transformation, the
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processed images were rescaled to the size 224×224 pixels to meet the input requirement of
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the chosen CNN.
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2.3. Classification architecture and model training
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The present study use the classification architecture and model developed by Lu et al.
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(2021). Briefly, ResNet 101 pre-trained on ImageNet (He et el., 2016) was selected to
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construct our model as it outperformed other mainstream networks in our preliminary tests.
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The detail architecture for our model is presented in Fig. 2. Pytorch framework was used to
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construct our neural network as it has been widely used in the research and computer
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visualization industry. To avoid overfitting and to correlate with our dataset, dropout layers
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were added and the number of SoftMax output was changed to 19 in ResNet 101 structure.
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The acquired data were divided into trainset and testset at the ratio of 8:2, while augmentation
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methods including random flip and Gaussian noise addition were also conducted to during
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training. With more diverse training data, the classifier can learn features more thoroughly
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while the immunity to certain level of noise can also be gained, which will improve the
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general classification accuracy and prevent overfitting.
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2.4. Web application and ecological indices calculation
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To improve accessibility of above method for broad users, our trained model was
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incorporated into a straightforward and user-friendly web application NemaRec (Fig. 3). The
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NemaRec is composed of both front and back end. The front end was written in Javascript on
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Visual Studio code, which includes general layout and user interaction scheme. The back end
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was written with Flask in Python, which integrates the trained model as well as the
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transformational pre-processing of the uploaded images.
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During the identification, users can upload a serial of images taken from a specific
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sample. As the classification results are returned to the front end, the number of each
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identified species will be displayed and counted. The images will be turned into binary form
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before being fed into the model, which subsequently outputs the predicted class and sends the
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results to the front end. Based on CNN identification output, the NemaRec annotates
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nematodes to five feeding types (bacterivore, fungivore, plant-parasitic, omnivore and
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predator) and further calculates MI and PPI indices using the c-p values stated in Bongers
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(1990). The web app is available at http://8.140.109.225:8866/, which allows the users to
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upload their images via the front end and hence receive the results.
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2.5. User data collection
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Given the accuracy of a CNN model is related to the number of images used in model
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training, the NemaRec can collect user uploaded images for future model adjustment. During
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operation, users will upload their images (preferable the head and tail parts) via the front end.
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The data will then be sent to the back end, where the trained model will analyse the output
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and make the prediction. Finally, the results will be sent back to the front end to display, while
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the ecological indices will also be computed (Fig. 3). After this procedure, user uploaded
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images will be store in website server, available for download in a flexible time interval
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depends on the amount of images. These images will be manually labelled to genus level
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based on morphology, and subsequently split them into trainset and testset and used from
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model training.
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2.6. Test of effectiveness
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The performance of NemaRec was evaluated using newly photographed nematode
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samples and augmented images that independent from those used for model training.
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First, a total of 190 images (10 images for each of genera) were acquired under the same
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conditions (microscopy, nematode materials and photographer) of generating training set.
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These images are identified in NemaRec, and the recognition rates are shown in Table 1.
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To further verify the performance of NemaRec in multi-scenario, the newly
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photographed nematodes were assigned to four groups of datasets. The datasets A, B and C
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consist of nematodes from grassland, the wheat field, and the temperate deciduous broad-leaf
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forest, respectively. These ecosystems have contrasting nematode communities, feeding types,
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and ecological indices. For these datasets, nematode images were collected by students
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without nematology experiences, containing blurred (Fig. 4 A, B), debris (Fig. 4C), broken
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(Fig. 4D) and crushed nematode under cover slide (Fig. 4E), but representing the frequently
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encountered scenarios. Since we only trained model with a limited number of soil nematode
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taxa, we test the NemaRec in a more realistic scenario by adding 4-10 images which do not
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belong to any of trained genera to these datasets A-C (e.g. Fig. 4F-J). The dataset D contained
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nematodes from grassland, and were carefully prepared by first author, presenting images
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with high quality (Fig. 4 K-O). All these datasets are available upon request from first author.
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Aside from specimen-based dataset, we also generated the augmented dataset from the
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images having been used in model training. This increases the data size and allows us to test
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performance at optimal condition. We implemented two types of augmentation in which
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testset images were randomly flip (vertical and horizontal) and Gaussian blurred. A total
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number of 67 images were generated and used for modelling test.
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3. Results
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3.1. Model evaluation using augmented and specimen-based dataset
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Currently, our developed application is capable to identify 19 genera (see the genera used
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for photograph). To evaluate our models, we tested identification success for each of genera
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and results are given in Table 1. In general, the success rate various greatly among genera
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from 0% (Dorylaimus) to 90% (Acrobeles and Mesocriconema) with an average of 54.7%.
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Apart from 6 genera (Dorylaimus, Discolaimus, Amplimerlinius, Helicotylenchus,
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Mesodorylaimus and Aphelenchoides), all test genera have a success rate of 50% or higher.
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Although our model failed to identify any of Dorylaimus, it mostly assigns query images as a
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morphologically similar genus Eudorylaimus (70%). Indeed, these two genera often confused
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by taxonomist. Similar misidentification is lead to most of low success rate, e.g.
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Amplimerlinius was identified as other stylet-bearing genera (80%) and Helicotylenchus was
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identified as Pratylenchus (70%). In compare, Discolaimus was surprisingly assigned to
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several unrelated genera, regardless its distinct morphology.
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Besides, we report the accuracy in a more realistic scenario, by using real ecological
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datasets and different image qualities (Fig. 5). c-p values were calculated based on
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identification results to examine how much it can be accidently correct, as different taxa may
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share same value thus mask error in genus identification. As expected, the augmented dataset
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outperforms the specimen-based dataset. Within specimen-based dataset, images with low
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quality are generally hard to be identified, with only 29%-39% been correctly identified to
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genus level, 53%-63% been correctly assigned with feeding type, and 51%-64% been
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correctly assigned with c-p value. In contrast, high quality images can improve model
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performance up to 76% in c-p value assignment.
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3.3. Evaluation of maturity index calculation
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The MI obtained from our model generally underestimated actual value, ranging from
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17.1% in dataset B to 21.8% in dataset A, with the only exception of augmented dataset where
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predicted value is 2.25% higher than the actual value. The augmented dataset predicts most
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cases accurately while high quality dataset D surprisingly did not perform better than poor
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images datasets in MI evaluation.
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4. Discussion
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The deep convolutional approaches have been a growing trend in the computer vision
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field demonstrating impressive results in various tasks involving natural images. In present
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study, we developed the web application NemaRec using the I-Nema dataset (Lu et al., 2021).
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NemaRec adopts the deep CNN approach for nematode image identification, incorporate
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I-Nema dataset in a user friendly interface, and further extends it to the calculation of MI and
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PPI indices for environment evaluation. Compare with morphology based identification
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which is time-consuming and expert-demanding, NemaRec is incorporated into a
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user-friendly web application, and allows high-throughput image identification at a few
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minutes. Such advantage is especially important for ecological studies as these researches
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typically involve large dataset and majority ecologists are not expected to be nematode
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taxonomists.
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Although our model fails to achieve accuracy in a confident level (e.g. 95%), it is capable to
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capture the general trends among samples. Besides, we observed that the manual
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identification by non-specialists can inflict substantial mistakes that appears in similar
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magnitude or even higher. Therefore, the advantage of our model can still serve as an valuable
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tool for those without nematology experience.
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The bottleneck that limits the accuracy in machine learning is the size of training
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dataset. Specifically, sufficient number of nematode images is essential to achieve a
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satisfactory identification result. However, this is barely possible by the efforts from one
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single lab. Although the performance of current application is deemed far from satisfactory,
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we demonstrated that the machine learning is promising in nematode identification, as well as
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in MI and PPI indices calculation. More importantly, NemaRec opens a window of
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international collaboration, and will be substantially benefit from collecting numerous user
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uploaded images for the model training. Consequently, a much more functional and accurate
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program is expected in future.
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Funding
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The work was fully supported by the National Natural Science Foundation of China
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(grant number 32001876), and partly supported by the research grants PJ03906, PG00507 and
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F002.251301 from Australia.
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Credits authorship contribution statement
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XQ conceived this work, collected image data, wrote this manuscript. YW and XL
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developed software and analyzed data, XX, conducted model test and will maintain the web
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application. XQ, YW, XL, XW and HL revised this manuscript.
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Declaration of competing interest
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We declare that there is no conflict of interest in the submission of this manuscript.
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Legends of figures.
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Fig. 1. The example showing the effects of Canny edge detection and cropping algorithm
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Fig. 2. The overall architecture of ResNet101 used in this study.
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Fig. 3. The NemaRec user interface and the model re-training system by collecting user
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uploaded images in program back end.
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Fig. 4. Examples of dataset used for modelling test. Example of low quality images for (A)
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and (B) Blurred. (C) Contains debris. (D) Broken specimen. (E) Crushed under cover slide.
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(F-I) Example of species that were not used for modeling training but included in modelling
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test. Example of images considered as high quality for (J) Acrobeles sp. (K) Pristionchus sp.
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(L) Mylonchulus sp. (M) Ditylenchus sp.; (N) Mesodorylaimus sp.; (O) Aphelenchoides sp.
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Fig. 5. Evaluation of NemaRec using different datasets. (A-C) Datasets with low quality
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images and contain species not been included in training datasets. (D) Dataset with high
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quality images. (E) Augmented dataset with images been randomly flipped and blurred. (F)
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Comparison of true maturity index calculated based on manually identification (T) and
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predicted value from NemaRec (P).
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