Plant-Parasitic Nematode: History
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Contributor:
Top Bahadur Pun
,
Arjun Neupane
,
Richard Koech

Plant-parasitic nematodes (PPN), especially sedentary endoparasitic nematodes like root-knot nematodes (RKN), pose a significant threat to major crops and vegetables. They are responsible for causing substantial yield losses, leading to economic consequences, and impacting the global food supply. The identification of PPNs and the assessment of their population is a tedious and time-consuming task.

  • plant-parasitic nematodes
  • root-knot nematodes
  • nematode detection/counting

1. Introduction

While the majority of nematodes in the soil are free-living and beneficial to plant growth [1], plant parasitic nematodes (PPN) infest economically important crops worldwide [2]. The damage caused by PPN is estimated to be 215 billion USD globally [3]. These parasites are categorised into two groups: ectoparasitic and endoparasitic. Ectoparasitic nematodes stay outside their host and feed on root tissue using a stylet, whereas endoparasitic nematodes enter the root system and damage root tissue [1]. Sedentary endoparasitic PPNs are most harmful as they inhabit inside of their host plant tissues for the majority of their life span and evolve to feed on root tissue perpetually [1]. The root-knot nematodes (RKN) and cyst nematodes are widely studied pestiferous sedentary endoparasites due to their worldwide agronomic influences [4]. These PPNs use a stylet to penetrate the host plant cell and also release protein to modify the host cell into a nutrient source [5]. On the other hand, plants infested by PPN release molecules that help nematodes move toward the plants and explore feeding sites [6]. The deformation and damage caused by PPN may lead to a malfunction of the root system, making it incapable of absorbing nutrients and water from the rhizosphere. PPN invasion may appear obscure because farmers are often unclear about the symptoms of the microscopic pest infestation [7]. In addition, the symptoms of the affected plants are similar to those caused by nutrient deficiency, poor yield, and wilt appearance [1]. These nematodes can survive through the infestation period of most crops [8][9].
To mitigate the problem caused by PPN, farmers commonly use a range of traditional and mechanical methods such as crop rotation, resistant cultivars, and destruction of crop root remnants [10][11]. PPN may also be controlled using chemical nematicides (e.g., organophosphates and carbamates) to optimise crop yield [12]. However, overuse of chemicals may degrade soil quality, become hazardous to the environment [13], and may cause groundwater contamination and poison food materials. Chemical nematicides are thus restricted because of their potentially harmful effects [13].
Effective and efficient application of pesticides can eliminate pests while minimising soil damage and environmental contamination, thus maximising crop productivity. This can be facilitated by the accurate identification and quantification of PPN. Traditionally, PPN are identified and counted using a manual microscope. Although this method is relatively simple, it is laborious and time consuming. Alternative approaches to the manual identification and quantification of nematodes include methods based on Deoxyribonucleic Acid (DNA) [14][15], morphological image [16][17], and sequence [18][19].
Recently, state-of-the-art deep learning methods have been applied to agricultural problems such as plant phenotyping [20] and fruit detections [21][22]. It has also been used to identify PPN [23]. For instance, a deep learning model was employed to detect and count soybeancyst nematode eggs in the microscopic images [24]. Traditional image processing and computer vision methods are still irreplaceable in domain-specific problems (virtual reality, video processing, and motion captures); however, they have been outperformed by deep learning methods in object detection, image classification, and semantic segmentation [25]. Deep learning and computer vision were used to identify Globodera pallida and Globodera rostochiensis morphological features (stylet length) [26]. Another deep learning model, NemaNet was developed to detect phytonematodes in soybean crops [27]. The NemaNet model utilised features of the DenseNet and Inception models and achieved an accuracy of 0.9817 and an F1 score of 0.9821. The deep learning model based on ResNet 101 was developed to detect and classify different genera of nematodes [28]. The deep learning models EfficientNetV2B0 ResNet101v2, EfficientNetV2M, and CoAtNet-0 were used to detect nematodes in Indonesian soils [29]. EfficientNetV2M scored the highest accuracy with a 98.66% mean class accuracy and a 98.26% average precision. All these models were only used to detect and classify nematodes. Nevertheless, there remains a gap between model innovation and practical implementation strategies for the benefit of farmers. A significant benefit of pest monitoring and management systems is to collect data and enable farmers to make rapid pest control decisions [30].

2. Plant-Parasitic Nematode

A typical decision support system (DSS) consists of tools to support decision making [31] and often contains some interactive features. With the advancement of information and communication technologies, decision support systems have been widely applied in production and operation management, transportation, logistics, marketing and finance, hospitals, and healthcare facilities [32]. Poor decision making, crop selection, and a shortage of support systems or tools for enhanced crop output are significant hurdles in agricultural production [33]. The essential purpose of a DSS in agriculture is to support farmers in their decision-making processes [34]. A DSS has the potential to facilitate farmers to solve agriculture problems efficiently with a complete understanding of the farm management process. Various decision support systems used in agriculture are shown in Table 1. These decision support systems collect and analyse soil, pest, crop, and environmental data to maximise crop productivity and financial return. Certain DSS include economic data along with optimisation methods to generate comprehensive solutions for the user.
Table 1. Decision support systems used in agriculture.
DSS Application Methods Reference
NemaDecide Crop rotation Stochastic and probabilistic methods [35]
SBN-Watch Crop rotation Seinhorst equations [36]
Spatial DSS Pest management Degree day model and harvest date estimation [37]
AgroDSS Prediction of pest population Random forest and time series analysis [38]
Soil Navigator Soil function assessment and management Decision Tree, if then rules [39]
Spatial DST Soil temperature prediction Generalised additive mixed model [40]
Great Plains Framework for Agricultureal Resource Management (GPFARM) Weed control, fertilisation, and harvest management If then rules [41]
Smart Irrigation Decision Support System (SIDSS) Irrigation management Partial least square regression and fuzzy inference system [42]
Irrigation decision support system (IDSS) Irrigation management Fuzzy inference system [43]
Land-use Decision Support System (LDSS) Land Management Multivariate linear programming [44]
Deep neural network-based DSS Crop yield prediction Back propagation neural network and grey decision-making system [45]
Bayesian model-based DSS Wireworm pest risk assessment Bayesian model [46]
The NemaMod component serves as the core engine of NemaDecide, providing nematological information necessary for conducting a cost–benefit analysis on the soil sampling methods. SBN-Watch was used to analyse the effect of crop rotation and sugar beet varieties on the Heterodera schachtii Schmidt population [36]. The spatial decision support system was employed to estimate the Rhagoletis cerasi severity and reduce the chemical footprint on cherry fruit and its surroundings [37]. This method used two algorithms: one based on day degree mode and the other relying on parameters such as harvest dates, pre-harvest date, and percentage of the trap. The DSS then estimated the efficacy of chemical control strategies. Soil Navigator is another example of a DSS used to investigate soil function and provide soil management guidance to farmers [39]. This system used if-then rules to analyse soil function data such as climate regulation, water purification, primary production, and nutrient cycling. Similarly, a web-based spatial decision support tool was developed to predict soil temperature using generalised additive mixed modeling [40].
Few machine learning-based decision support systems have been developed and used in the agricultural sector. A rule-based agriculture DSS incorporated soil, weather, and pest data to support farm management decisions [41]. This DSS enabled farmers to easily test farm management rules for farm production and revenue. Smart irrigation decision support systems use soil sensors to estimate the soil moisture and temperature in addition to weather data such as temperature, rainfall, relative humidity, and dew point [42]. Then, partial least square regression (PLSR) and an adaptive neuro-fuzzy inference system were used to help farmers in irrigation management [42]. Likewise, fuzzy inference system-based IDSS was implemented for irrigation management [43]. The IDSS analysed weather data, soil moisture, and alfalfa height to infer appropriate irrigation time and quantity. Similarly, LDSS was developed to manage agricultural land based on environmental constraints [44]. LDSS analysed soil quality, irrigation data, and ecological factors and computed the optimal land-use structure. In agriculture, decision support systems can be applied for better management of fertiliser, crop rotation, pest monitoring, and optimising livestock diets [47]. AgroDSS uses data mining methods that include predictive modeling, time series clustering functionalities, and structure change detection to assist farmers in effective farm management [38]. The neural network-based DSS was developed to analyse geospatial data and predict seasonal catastrophes and agricultural indicators [45]. Agriculture DSS was also used to manage nutrients and irrigation on the farm [48]. A Bayesian model-based DSS was developed to predict the potential risk of wireworm [46]. The model used soil, field, and weather data to analyse the effects on the wireworm population.

Nematode Management

Once the population of PPN in the sample is determined, their severity is estimated by subtracting the damage threshold, which is defined as the density of the nematode population tolerable based on the particular crops and soil type without causing yield losses [49]. The damage threshold for a particular crop differs for each nematode genus. Damage thresholds are presented in different standards and are expressed in 100 cm3 soil and per gram dry root [49]. However, a 200 g sample size was reported to be a reliable and precise measure of the nematode population density [50]. Another study expressed a damage threshold per 250 g of soil [51]. After an accurate assessment of plant parasitic nematodes, a suitable nematode control measure is applied to suppress the nematode population. There are different nematode control methods such as biological control [52], chemical control [53], and cultural practice [54].
The most common control strategy for nematodes involves the utilisation of chemical compounds called nematicides. There are more than 20 chemicals available for chemical control and the most common chemicals are methylbromide and chloropropene [53]. These are fumigant nematicides used before sowing crops and are transferred through the soil in the form of gas. The use of these nematicities, including methyl bromide, are restricted to use because of increasing environmental safety concerns [55]. These products destroy beneficial rhizosphere microorganisms [56] and soil exposed to fumigants is more susceptible to nematode reinfestation [57]. Alternative to fumigants, non-fumigant nematicides are water soluble or formulated in solid. Non-fumigant nematicides such as fluopyram (Velum) and fluensulfone (Nimitiz) are capable of suppressing PPN without damaging the natural ecosystem [58]. Amides, esters, ketones, thioethers, hydrazones, and tioxazafen are the current nematicides used for nematode population suppression [59]. The appropriate dose of nematicide can eliminate 85-90%. However, improperly applied nematicide can delay plant growth and be highly toxic to some plants.
Crop rotation is one common cultural practice to minimise the nematode population [54]. Crop rotation is accomplished by cultivating highly resistant crops to particular species of nematode. Crop rotation is not applicable to suppress all types of nematode species. Organic materials are used to minimise the RKN population. These organic amendments include cattle manure, chicken litter, and compost [60]. These organic amendments not only suppress the nematode population but also improve plant health and crop yield. Predatory nematodes can be grown from decomposing organic material that feeds plant-parasitic nematodes. Cultivating crop varieties with resistant nematodes is another possible way to minimise plant parasitic nematode infestation. Some genetic varieties of crops have nematode resistance that limits the nematode population growth because of an unsuitable host for nematode reproduction and poor feeding environment [61]. Also, the transgenic plant can decrease the root galls and eggs by suppressing the parasitism gene [62]. These control measures are implemented based on the impact of PPN on crops, seasonal variabilities, and soil properties.

This entry is adapted from the peer-reviewed paper 10.3390/jimaging9110240

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