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Cucurbit yellow stunting disorder virus (CYSDV) is an emerging virus causing significant yield losses in cucurbits. Simple but reliable detection and quantification methods are important tools for disease management. In susceptible germplasm, CYSDV was detected 5 days postinoculation (dpi) by reverse transcriptase polymerase chain reaction (RT-PCR) or by tissue blot immunoassay (TBIA), and 8–9 dpi by dot blot immunoassay (DBIA) or enzyme-linked immunosorbent assay (ELISA). For CYSDV quantification, real-time RT-PCR was the most sensitive method and gave the best linear range of detection, over four orders of magnitude as compared to approximately two orders of magnitude for DBIA and nucleic acid hybridization. DBIA was more sensitive than ELISA and equally sensitive to nucleic acid hybridization with a non-radioactively labelled cDNA probe. Time course studies at 3, 5, 8 and 14 dpi using TBIA revealed that tolerance to CYSDV in three tolerant cucumber germplasms was not correlated with restricted or delayed virus movement. [ABSTRACT FROM AUTHOR]

To enhance food security, food safety, and environmental health, a bio-based integrated pest management (BIPM) strategy was evaluated at two coastal locations in Lebanon as an alternative to toxic pesticide sprays in commercial high-arched plastic tunnels common in many countries. The evaluation occurred during two cucumber and pepper cropping seasons: spring and fall. At each site, two commercial tunnels were used; farmers' conventional practices were applied in one tunnel, while the BIPM approach was followed in the second tunnel. In the farmers' practices, a total of 14 sprays of insecticide/acaricide mixtures were applied during the spring growing season, and 6 sprays were applied during the fall. In the BIPM tunnels, hotspot releases of local strains of Amblyseius swirskii and Phytoseiulus persimilis were applied. By the end of the spring season, the number of whitefly nymphs (WFNs)/leaf and thrips/leaf in the pesticide treatment were 4.8 and 0.06, respectively, compared to 0.1 and 0.33, respectively, in the BIPM treatment. Similarly, at the end of the fall season, the WFNs reached 19.7/leaf in the pesticide control as compared to 1.2/leaf in the BIPM treatment, proving the efficacy of A. swirskii . Farmers using conventional acaricides during both cropping seasons failed to control Tetranychus urticae , the two-spotted spider mite (TSSM). However, hotspot releases of P. persimilis were successful in controlling TSSM. By the end of June, the number of TSSMs reached 7.8/leaf in the BIPM treatment compared to 53/leaf in the pesticide treatment. Likewise, in December, TSSM numbers reached 9/leaf in the BIPM treatment compared to 40/leaf in the pesticide treatment. Preliminary observations of pepper showed that both predatory mites ( A. swirskii and P. persimilis ) gave similar or better efficacy against the three pests. The two local predatory phytoseiid mites seem to be effective in controlling these three major pests and to be adapted to local environmental conditions. A rate of increase of 0.86 was observed for P. persimilis and 0.22 for A. swirskii , in June, when maximum temperatures were close to 40 °C. This also shows a compatibility between the two predators. In conclusion, our BIPM approach was efficient under a Mediterranean climate in arched plastic tunnels with relatively poor aeration.

Deep neural networks can be used to diagnose and detect plant diseases, helping to avoid the plant health-related crop production losses ranging from 20 to 50% annually. However, the data collection and annotation required to achieve high accuracies can be expensive and sometimes very difficult to obtain in specific use-cases. To this end, this work proposes a synthetic data generation pipeline based on generative adversarial networks (GANs), allowing users to artificially generate images to augment their small datasets through its web interface. The image-generation pipeline is tested on a home-collected dataset of whitefly pests, Bemisia tabaci , on different crop types. The data augmentation is shown to improve the performance of lightweight object detection models when the dataset size is increased from 140 to 560 images, seeing a jump in recall at 0.50 IoU from 54.4 to 93.2%, and an increase in the average IoU from 34.6 to 70.9%, without the use of GANs. When GANs are used to increase the number of source object masks and further diversify the dataset, there is an additional 1.4 and 2.6% increase in recall and average IoU, respectively. The authenticity of the generated data is also validated by human reviewers, who reviewed the GANs generated data and scored an average of 56% in distinguishing fake from real insects for low-resolutions sets, and 67% for high-resolution sets., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Karam, Awad, Abou Jawdah, Ezzeddine and Fardoun.)

Phytoplasmas are mollicutes restricted to plant phloem tissue and are normally present at very low concentrations. Real-time polymerase chain reaction (qPCR) offers several advantages over conventional PCR. It is a fast, sensitive, and reliable detection technique amenable to high throughput. Two fluorescent chemistries are available, intercalating dyes or hybridization probes. Intercalating dyes are relatively less expensive than TaqMan ® hybridization probes but the latter chemistry is the most commonly used for phytoplasma detection. qPCR may be designed for universal detection of phytoplasma, group or subgroup specific detection, or for simultaneous detection of up to three or four phytoplasmas (multiplexing). qPCR may be used for relative or absolute quantification in host plants and in insect vectors. Therefore, qPCR plays an important role in phytoplasma detection as well as in host-pathogen interaction and in epidemiological studies. This chapter outlines the protocols followed in qPCR assay for phytoplasma detection and quantification, focusing mainly on the use of TaqMan ® probes.

Background: Almond witches'-broom (AlmWB), a devastating disease of almond, peach and nectarine in Lebanon, is associated with 'Candidatus Phytoplasma phoenicium'. In the present study, we generated a draft genome sequence of 'Ca. P. phoenicium' strain SA213, representative of phytoplasma strain populations from different host plants, and determined the genetic diversity among phytoplasma strain populations by phylogenetic analyses of 16S rRNA, groEL, tufB and inmp gene sequences., Results: Sequence-based typing and phylogenetic analysis of the gene inmp, coding an integral membrane protein, distinguished AlmWB-associated phytoplasma strains originating from diverse host plants, whereas their 16S rRNA, tufB and groEL genes shared 100 % sequence identity. Moreover, dN/dS analysis indicated positive selection acting on inmp gene. Additionally, the analysis of 'Ca. P. phoenicium' draft genome revealed the presence of integral membrane proteins and effector-like proteins and potential candidates for interaction with hosts. One of the integral membrane proteins was predicted as BI-1, an inhibitor of apoptosis-promoting Bax factor. Bioinformatics analyses revealed the presence of putative BI-1 in draft and complete genomes of other 'Ca. Phytoplasma' species., Conclusion: The genetic diversity within 'Ca. P. phoenicium' strain populations in Lebanon suggested that AlmWB disease could be associated with phytoplasma strains derived from the adaptation of an original strain to diverse hosts. Moreover, the identification of a putative inhibitor of apoptosis-promoting Bax factor (BI-1) in 'Ca. P. phoenicium' draft genome and within genomes of other 'Ca. Phytoplasma' species suggested its potential role as a phytoplasma fitness-increasing factor by modification of the host-defense response.