Current advances in the identification of plant nematode diseases: From lab assays to in-field diagnostics

Abstract

Plant parasitic nematodes (PPNs) cause an important class of diseases that occur in almost all types of crops, seriously affecting yield and quality and causing great economic losses. Accurate and rapid diagnosis of nematodes is the basis for their control. PPNs often have interspecific overlays and large intraspecific variations in morphology, therefore identification is difficult based on morphological characters alone. Instead, molecular approaches have been developed to complement morphology-based approaches and/or avoid these issues with various degrees of achievement. A large number of PPNs species have been successfully detected by biochemical and molecular techniques. Newly developed isothermal amplification technologies and remote sensing methods have been recently introduced to diagnose PPNs directly in the field. These methods have been useful because they are fast, accurate, and cost-effective, but the use of integrative diagnosis, which combines remote sensing and molecular methods, is more appropriate in the field. In this paper, we review the latest research advances and the status of diagnostic approaches and techniques for PPNs, with the goal of improving PPNs identification and detection.

Biosecurity risks to human food supply associated with plant-parasitic nematodes

“…Early, accurate and swift detection and diagnosis are paramount for effective nematode population management ( Shao et al., 2023 ). Kantor et al.…”

“…Isothermal amplification methods like LAMP (Loop-mediated Isothermal Amplification) and RPA (Recombinase Polymerase Amplification) are lauded for their simplicity and rapid application in the field, addressing some of these challenges. Furthermore, the integration of remote sensing, molecular techniques, and the advent of machine learning and artificial intelligence promise to further refine nematode detection processes, especially for handling large sample sizes efficiently ( Akintayo et al., 2018 ; Shao et al., 2023 ). Recent advancements in deep learning have already shown promising results in the classification and detection of plant pests, achieving accuracy rates as high as 93% (Cheng et al., 2017; Xie et al., 2018; Kasinathan et al., 2021; Picek et al., 2022).…”

Editorial: Detection, characterization, and management of plant pathogens

“…Recent molecular techniques have been proposed for diagnosing nematodes in the field ( Carneiro et al., 2017 ). These technologies are considered useful since they are quick, accurate, and inexpensive; nevertheless, integrated diagnostics, which combines remote sensing and molecular techniques, is more appropriate for the field ( Shao et al., 2023 ). In this section, we discuss recent research advances as well as the current state of parasitic nematode species detection, characterization, and management measures, with the goal of improving long-term nematode prevention and control in agricultural settings.…”

Revolutionizing nematode management to achieve global food security goals - An overview

“…Fungi, bacteria, viruses, and nematodes are common plant pathogens that can cause significant damage to crops and affect food security [ [6] , [7] , [8] ]. Among the diverse plant pathogens, nematodes can cause severe damage to crops [ 9 , 10 ]. Plant parasitic nematodes are microscopic, worm-like organisms that live in the soil and feed on plant roots, leading to stunted growth, reduced yields, and in severe cases, plant death [ 11 ].…”