Table 4.
Comparison of different plant nematode detection methods.
| Category | Technology | Advantages | Disadvantages | Site | References |
|---|---|---|---|---|---|
| Morphology | Morphological methods | Intuitive, low cost | Difficult to judge accurately; complex to operate and requires specialized technicians | In the lab | (Oliveira et al., 2011) |
| Biochemical Methods | Isozymes | It can reflect phylogenetic relationships; High sensitivity | Mainly used only for root-knot nematodes; Time-consuming | (Dickson et al., 1970) | |
| Mass spectral analyses | fast, reliable, high sensitivity | Time- consuming, requires specialized skills | (Rivero et al., 2022) | ||
| PCR Methods | DNA barcoding | Accuracy | Time-consuming | (Hebert et al., 2003) | |
| Droplet digital PCR | High sensitivity and low amount of template DNA | Expensive reagents and instruments | (Rougemont et al., 2004) | ||
| Gene chip technology | Fast, accuracy | Expensive equipment, immature technology | (Fodor, 1997) | ||
| RFLPs | Reliable and reproducible | Complex operations, requiring large amounts of DNA | Blok and Powers, 2009 | ||
| RAPD | Generates a large amount of information | Lacks repeatability, requires strict experimental reaction | (Feng et al., 2005) | ||
| SCAR | High sensitivity and specificity | Time-consuming | (Chen et al., 2011) | ||
| RT-qPCR | Sensitive, reliable | Time- consuming, equipment relatively expensive | (Berry et al., 2008) | ||
| ddPCR | High sensitivity, Simple, convenient | Expensive instruments | Chen et al., 2022) | ||
| Isothermal Amplification Technology | LAMP | Low cost, simple operation, low equipment demand | False-positive results | Outdoors and in the field | (Ahuja and Somvanshi, 2021) |
| RPA | Fast, high sensitivity and specificity, Low cost, simple operation, low equipment demand, visualization of results | False-positive results; Required to design specific primers, probes, and gRNA | (Babu et al., 2018) | ||
| LRPA- CRISPR/Cas12a | |||||
| Spectral techniques | Remote sensing systems | Fast, large-area detection, dynamic monitoring | Requires technical personnel expertise, difficult to capture detailed changes | (Tao et al., 2020) | |
| Machine Learning | Artificial intelligence | fast, accurate and eliminate human errors | Lack of professional classification experts and a sufficient number of databases | In the lab | (Almalki, 2022) |