Frequent monitoring of emerging viruses of agricultural crops is one of the most important missions for plant virologists. A fast and precise identification of potential harmful viruses may prevent the occurrence of serious epidemics. Nowadays, high-throughput sequencing (HTS) technologies became an accessible and powerful tool for this purpose. The major discussion of this strategy resides in the process of sample collection, which is usually laborious, costly and nonrepresentative. In this study, we assessed the use of sewage water samples for monitoring the widespread, numerous, and stable plant viruses using HTS analysis and RT-qPCR. Plant viruses belonged to 12 virus families were found, from which , , , , , and were the most abundant ones with more than 20 species. Additionally, we detected one quarantine virus in Brazil and a new tobamovirus species. To assess the importance of the processed foods as virus release origins to sewage, we selected two viruses, the tobamovirus pepper mild mottle virus (PMMoV) and the carlavirus garlic common latent virus (GarCLV), to detect in processed food materials by RT-qPCR. PMMoV was detected in large amount in pepper-based processed foods and in sewage samples, while GarCLV was less frequent in dried and fresh garlic samples, and in the sewage samples. This suggested a high correlation of virus abundance in sewage and processed food sources. The potential use of sewage for a virus survey is discussed in this study.

Many pathogens are dispersed by airborne spores, which can vary in space and time. We can use air sampling integrated with suitable diagnostic methods to give a rapid warning of inoculum presence to improve the timing of control options, such as fungicides. Air sampling can also be used to monitor changes in genetic traits of pathogen populations such as the race structure or frequency of fungicide resistance. Although some image-analysis methods are possible to identify spores, in many cases, species-specific identification can only be achieved by DNA-based methods such as qPCR and LAMP and in some cases by antibody-based methods (lateral flow devices) and biomarker-based methods ('electronic noses' and electro-chemical biosensors). Many of these methods also offer the prospect of rapid on-site detection to direct disease control decisions. Thresholds of spore concentrations that correspond to a disease risk depend on the sampler (spore-trap) location (whether just above the crop canopy, on a UAV or drone, or on a tall building) and also need to be considered with weather-based infection models. Where disease control by spore detection is not possible, some diseases can be detected at early stages using optical sensing methods, especially chlorophyll fluorescence. In the case of infections on wheat, it is possible to map locations of severe infections, using optical sensing methods, to segregate harvesting of severely affected areas of fields to avoid toxins entering the food chain. This is most useful where variable crop growth or microclimates within fields generate spatially variable infection, i.e. parts of fields that develop disease, while other areas have escaped infection and do not develop any disease.

In Benin, yam production continues to face numerous production constraints, including yield and quality reduction by . Implementation of efficient management techniques against this pest requires an improved understanding, including at the molecular level, of the pest. The current study aimed at identifying the spp. associated with yam in Benin and investigating the phylogenetic relationships between populations. Nematodes of the genus were obtained from tubers exhibiting external dry rot symptoms. DNA was extracted from nematodes belonging to 138 populations collected from 49 fields from 29 villages. For 51 of these populations, both the ITS1 and COI regions could be amplified PCR, sequenced, compared with available sequences in the NCBI database and were identified as . Maximum likelihood was used to construct 60% consensus phylogenetic trees based on 51 sequences. This phylogenetic analysis did not reveal any genetic separation between populations by cultivar, village, cropping system nor by agroecological zone. Neither could any subgroups within be separated, indicating that no subspecies were present. An earlier published species-specific primer set was verified with the DNA of the 51 sequences and was considered a reliable and rapid method for identification.

The severity of plant diseases, traditionally defined as the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases but is prone to error. Plant pathologists face many situations in which the measurement by nearest percent estimates (NPEs) of disease severity is time-consuming or impractical. Moreover, rater NPEs of disease severity are notoriously variable. Therefore, NPEs of disease may be of questionable value if severity cannot be determined accurately and reliably. In such situations, researchers have often used a quantitative ordinal scale of measurement-often alleging the time saved, and the ease with which the scale can be learned. Because quantitative ordinal disease scales lack the resolution of the 0 to 100% scale, they are inherently less accurate. We contend that scale design and structure have ramifications for the resulting analysis of data from the ordinal scale data. To minimize inaccuracy and ensure that there is equivalent statistical power when using quantitative ordinal scale data, design of the scales can be optimized for use in the discipline of plant pathology. In this review, we focus on the nature of quantitative ordinal scales used in plant disease assessment. Subsequently, their application and effects will be discussed. Finally, we will review how to optimize quantitative ordinal scales design to allow sufficient accuracy of estimation while maximizing power for hypothesis testing.