Heterodera schachtii is a well-known cyst nematode that causes serious economic losses in sugar beet production every year. Rapid and visual detection of H. schachtii is essential for more effective prevention and control. In this study, a species-specific recombinase polymerase amplification (RPA) primer was designed from a specific H. schachtii sequence-characterized amplified region (SCAR) marker. A band was obtained in reactions with DNA from H. schachtii, but absent from nontarget cyst nematodes. The RPA results could be observed by the naked eye, using a lateral flow dipstick (LFD). Moreover, we combined CRISPR technology with RPA to identify positive samples by fluorescence detection. Sensitivity analysis indicated that 10⁻⁴ single cysts and single females, 4⁻³ single second-stage juveniles, and a 0.001 ng genomic DNA template could be detected. The sensitivity of the RPA method for H. schachtii detection is not only higher than that of PCR and qPCR, but can also provide results in <1 h. Consequently, the RPA assay is a practical and useful diagnostic tool for early diagnosis of plant tissues infested by H. schachtii. Sugar beet nematodes were successfully detected in seven of 15 field sugar beet root samples using the RPA assay. These results were consistent with those achieved by conventional PCR, indicating 100% accuracy of the RPA assay in field samples. The RPA assay developed in the present study has the potential for use in the direct detection of H. schachtii infestation in the field.

Insect pests are primarily controlled by insecticides. However, the sensitivity decreases and insecticide resistance is problematic for the effective management of agriculturally important insects, including Metopolophium dirhodum, which is an aphid that commonly feeds on cereals. The insecticide sensitivity status and potential resistance of M. dirhodum field populations remain relatively unknown. In this study, the susceptibility of 19 M. dirhodum populations from seven provinces in Northern China to neonicotinoids, pyrethroids, organophosphates, and a macrolide (abamectin) was determined in 2017–2019. The results indicated that two populations were highly resistant to thiamethoxam, with a relative resistance ratio (RLR) of 134.03 and 103.03, whereas one population was highly resistant to beta-cypermethrin (RLR of 121.42). On the basis of the RLR, the tested M. dirhodum populations ranging from susceptible to showing moderate levels of resistance to imidacloprid (RLR of 1.50 to 57.29), omethoate (RLR of 1.07 to 18.73), and abamectin (RLR of 1.10 to 25.89), but they were ranging from susceptible to showing tolerance or low levels of resistance to bifenthrin (RLR of 1.14 to 6.02) and chlorpyrifos (RLR of 1.11 to 7.59). Furthermore, a pair-wise correlation analysis revealed a significant correlation between the median lethal concentrations (LC₅₀) for beta-cypermethrin and thiamethoxam, reflecting the cross-resistance between these two insecticides. The data obtained in our study provide timely information about aphid insecticide sensitivity, which may be used to delay the evolution of M. dirhodum insecticide resistance in Northern China.

Wheat stripe rust caused by Puccinia striiformis f. sp. tritici is a destructive disease. Its pathogen frequently adapts to newly invaded regions and overcomes resistance in wheat cultivars. This disease is especially important in China due to its favorable conditions for the stripe rust epidemic and the recombination population structure of pathogens. Xinjiang is a vast epidemic region in China, but very limited research on this disease has been performed in this region. In this study, we identified 25 races from 129 isolates collected from winter wheat fields from five different regions (Nileke, Xinyuan, Gongliu, Huocheng, and Qapqal) of Yili, Xinjiang, using the Chinese set of 19 differential wheat lines. All isolates were virulent on the differentials Fulhad and Early Premium, but no isolates were virulent on Yr5. Among the 25 races, Suwon11-1 was the most prevalent, followed by CYR34. Both races were found in four out of the five locations. It is important to continue monitoring stripe rust and its pathogen races in this region, as it forms a pathway between China and Central Asia. Collaborative research is essential for controlling stripe rust in this region, other regions in China, and neighboring countries.

Deep learning approaches have been widely applied for agricultural disease detection. However, considerable challenges still exist, such as low recognition accuracy in complex backgrounds and high misjudgment rates for similar diseases. This study aimed to address these challenges through the detection of six prevalent wheat diseases and healthy wheat in images captured in a complex natural context, evaluating the recognition performance of five lightweight convolutional networks. A novel model, named MnasNet-SimAM, was developed by combining transfer learning and an attention mechanism. The results reveal that the five lightweight convolutional neural networks can recognize the six different wheat diseases with an accuracy of more than 90%. The MnasNet-SimAM model attained an accuracy of 95.14%, which is 1.7% better than that of the original model, while only increasing the model’s parameter size by 0.01 MB. Additionally, the MnasNet-SimAM model reached an accuracy of 91.20% on the public Wheat Fungi Diseases data set, proving its excellent generalization capacity. These findings reveal that the proposed model can satisfy the requirements for rapid and accurate wheat disease detection.

Wheat stripe rust is globally one of the most important diseases affecting wheat. There is an urgent need to develop environmentally safe and durable biological control options to supplement the control that is achieved with breeding and fungicides. In this study, endophytic bacteria were isolated from healthy wheat through the tissue separation method. Antagonistic endophytic bacteria were screened based on the control effect of urediniospore germination and wheat stripe rust (WSR). The taxonomic status of antagonistic strains was determined based on morphological, physiological, and biochemical characteristics and molecular biological identification (16S rDNA and gyrB gene sequence analysis). Finally, the potential growth-promoting effect of different concentrations of antagonists on wheat seedlings and the biological control effect of WSR were studied. A total of 136 strains of endophytic bacteria belonging to 38 genera were isolated. Pseudomonas was the most common bacterial genus, with 29 isolates (21%). The biological control effect of different isolates was assessed using an urediniospore germination assay. The isolate XD29-G1 of Paenibacillus polymyxa had the best performance, with 85% inhibition of spore germination during primary screening. In the deep screening, the control effect of XD29-G1 on wheat stripe rust was 60%. The antagonist XD29-G1 promoted the germination of wheat seeds and the growth of wheat seedlings at a solution dilution of 10⁻⁷ cfu/mL. The pot experiment results showed that different dilution concentrations of the strain had different levels of antibacterial activity against WSR, with the concentration of 10⁻¹ cfu/mL having the best control effect and a control efficiency of 61.19%. XD29-G1 has better biological control potential against wheat stripe rust.

Sulfoxaflor belongs to a new class of insecticides which are effective against many sap-feeding pests. Sitobion miscanthi, Rhopalosiphum padi, and Metopolophium dirhodum are the predominant pests coexisting on wheat plants. It is unknown whether these aphid species have developed resistance to sulfoxaflor. Here, the susceptibilities of three wheat aphid species from different regions of China to sulfoxaflor were evaluated. The results showed that two S. miscanthi, one R. padi, and two M. dirhodum field populations were highly resistant to sulfoxaflor. Additionally, 13 S. miscanthi, 9 R. padi, and 4 M. dirhodum field populations were moderately resistant to sulfoxaflor. Analysis of differences in toxicity showed that the susceptibility levels of R. padi in 9 of 20 regions, M. dirhodum in 5 of 9 regions, and M. dirhodum in 3 of 9 regions to sulfoxaflor were greater than those of S. miscanthi, S. miscanthi, and R. padi in the same regions, respectively. Thus, each wheat aphid species has field populations that are highly sulfoxaflor resistant. The R. padi and M. dirhodum populations were more susceptible to sulfoxaflor than those of S. miscanthi. These findings provide new insights into insecticide resistance development and rational sulfoxaflor use.