Southern tomato virus (STV) is a dsRNA virus, which belongs to the newly formed genus of the family. Currently there is no report regarding the presence of STV in tomato tissues. In this study, we performed in situ hybridization to examine the distribution of STV in host tissues. STV was found in the leaves, stems, seeds, shoot apexes and root tips of tomato and localized in the cortex tissue, vascular tissues, pith, seed coat, endosperm, cotyledon (including inner cotyledons and outer cotyledons), hypocotyls and radicles of infected tomato tissues. In addition, STV was detected in the apical part of the stems and roots for the first time. This indicates that STV is a systemic infectious virus.

A survey to investigate the occurrence of cassava anthracnose disease (CAD) and distribution of spp. in cassava plantations in different eco-zones of the Reconcavo Region in Bahia, Brazil, investigated during the rainy season of 2014. A total of 50 cassava fields distributed among 18 municipalities were visited and intensity of anthracnose evaluated. The highest disease incidence (DI) (83.3%) was in samples collected in São Félix, and the lowest (34.4%), in Varzedo. Municipalities that presented the highest values for DI were located within the '' Köppen-Geiger eco-zone, also presenting the highest values for the estimated McKinney disease index. Based on previous studies of multilocus phylogeny, seven different species of were identified (, , s.s, , , and ) and a new approach based on ERIC-PCR was used aiming to group the 82 isolates according to these findings. The highest percentage of genetic variance (> 78%) was among isolates within fields. Based on the survey and genetic analysis, is probably the main causal agent of cassava anthracnose in the Recôncavo Region, since this species was present with highest incidence in all eco-zones, 47.61, 42.86 and 57.14% for (tropical rainforest climate), (tropical dry savanna climate) and (tropical wet savanna climate), respectively. This study is the first report of lineages as the most likely pathogen causing anthracnose disease of cassava in Brazil, and these findings may be used to guide the selection of resistant varieties.

We identified ten current key challenges for plant protection in cities each of them belonging to a specific field of action of IPM in urban horticulture according to Directive 2009/128/EC. The challenges are: appropriate plant selection, microbiome engineering, nutrient recycling, smart, digital solutions, diversification of vegetation, avoidance of pesticide side effects on beneficials, biorational efficacy assessment, effective pest diagnosis, efficient outbreak control and holistic approaches. They are discussed on the background of the defined urban horticultural core sectors (a) public green infrastructure, including professional plant care, (b) professional field and greenhouse production systems and (c) non-professional private homegardens and allotments.

The reaction of 636 (sections and ) accessions were evaluated under greenhouse conditions after mechanical inoculation with a Jordanian isolate of the new tobamovirus tomato brown rugose fruit virus (ToBRFV). Local and systemic infections were assayed by symptoms evaluation and virus detection via biotests and RT-PCR. All cultivated tomatoes () and the great majority of wild tomato accessions proved susceptible to ToBRFV. They showed a wide range of symptoms (mosaic, leaf deformations, mottling, shoestring, and stunting). Twenty-six accessions representing var. cerasiforme, , and were tolerant. High levels of resistance have been demonstrated in three accessions of , a close relative to wild tomatoes (member of the sect. ) not only to ToBRFV but also to the tobamoviruses, tobacco mosaic virus (TMV) and tomato mosaic virus (ToMV). After mechanical inoculation, the three tobamoviruses could be detected only in inoculated leaves in the accessions LA2160, LA2162, and LA 2166, which remained symptomless. However, two other accessions PI 473,498 and PI 230,519 reacted unusually. They were demonstrated highly resistant to TMV and ToMV, but proved transiently susceptible to ToBRFV showing mild systemic mosaic followed by total recovery from symptoms and the virus.

Pear decline, induced by the phytoplasma ' Phytoplasma pyri', transmitted by pear psyllids, is one of the most devastating diseases on in Europe and North America. Investigations of pear psyllids in 4 pear orchards in lower Austria showed the presence of , and at all locations. PCR analyses revealed overall phytoplasma infection rates for of 5.4%, for of 4.6%, for remigrants of 9.6% and for emigrants of 0%. The rates of PCR-positive and individuals varied greatly in the course of the year, and the highest infection rates were observed in late summer, autumn and in late winter. In transmission experiments with healthy pear seedlings, winterform individuals of transmitted the pathogen to 19.2% (5 out of 26) and 4.8% (2 out of 41) of the test plants, respectively. The vectoring ability of was experimentally proven for the first time, and in transmission experiments with remigrants, 9.5% (2 out of 21) of the pear seedlings were infected. Our data indicate a significant risk of pathogen transmission in pear orchards during the greater part of the year, especially in late winter, early spring and autumn. Multilocus sequence analysis by aid of the genes and allowed the discrimination between 15 phytoplasma types. Three so far undescribed genotypes and four undescribed genotypes were identified.

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

The Asian longhorned beetle (ALB) is a dreaded quarantine pest that attacks a wide range of hardwood tree species. However, some of the specified host plants which have to be eradicated in infestation zones such as lime and beech trees almost never get infested or may even be resistant to the ALB. As a result, the specified host plant list needs to be revised based on the data from the infestation areas in the EU, in order to avoid further felling of whole lime avenues and major clear-cutting in parks with old growth. As large differences may exist between species of the same genus, exact species not merely genera should be specified on the host list.

The red palm mite Hirst attacks coconut, banana and açai trees. In 2007, the mite was recorded for the first time in South America. In the State of Pará, was recorded in the municipalities of the northwest, a region that is closer to the State of Amazonas, called 'Low Amazons'. Between October and November 2019, it was observed, by chance, that coconut and banana plants infested by , in residential backyards in municipalities from Marabá and Parauapebas, Southest of Pará, Brazilian Amazon. Three species from the Araceae family, two from Arecaceae, one Heliconiaceae and one Zingiberaceae, in addition to the banana Musaceae and açaí plantations, were examined. All stages of development of were found on coconut, banana trees and imperial palms, and few individuals were found on areca palm and red ginger leaves. This is the first record of the occurrence of the pest mite in the Southeast Pará sub-region.

This study was designed to investigate the insecticidal activity of the essential oils (EOs) and extracts from and . The EOs were extracted from the leaves of and by hydro-distillation and their chemical components were analyzed by gas chromatography-mass spectrometry (GC-MS). The repellency, contact toxicity and antifeedant activity of the EOs and extracts were evaluated against and along with those of their main components. A total of nine compounds were identified from the EO of , and the most abundant component was myristicin (79.72%). The EO of exhibited repellent activities at different levels and its main compound myristicin showed contact toxicity and repellent effects against and . Meanwhile, by bioassay-guided fractionation, four compounds with strong antifeedant activities against , 24-methylenecycloartanyl-2'E, 4'Z-tetradecadienoate (1), methyl thyrsiflorin B acetate (2), friedelin (3) and Excoecarin R1 methyl ester (4) were separated and identified from the ethanol extract of for the first time. Considering the significant anti-insect activities, the EOs and extracts of and might be used in integrated pest strategies, establishing a good perspective for the comprehensive use of natural plant resources of genus.

Innovation in environmental fields such as plant health is complex because of unbounded challenges and lack of certainty of commercial uptake. In this paper we present a Technology Readiness Level (TRL) framework, specifically to assist with assessment of technologies to support detection of tree pests and pathogens, but also for wider potential adaptation. Biosecurity can be enhanced by improved early detection of pests and pathogens, but development and deployment of new technologies requires robust scrutiny. We critically analyse the concept, practice and applicability of TRLs. Interviews revealed scientist perspectives during the development process of five novel early plant pest and pathogen detection technologies. A retrospective, collective narrative of one technology from concept to commercial deployment was undertaken. We then developed a calculator tool for assessment of biosecurity TRLs. Our findings illustrate the iterative process of technology development, the challenges in final TRLs of acquiring funding to move from proven success to viable product, inefficiencies created through the need for multiple projects for each technology and the imperative to consider the wider socio-ecological technical landscape, including policy context. End user engagement was particularly valuable at beginning and end of the TRL scale. We conclude that the TRL framework comprises a robust approach to assess technologies in that it facilitates progress tracking, evaluation of success likelihood and identification of opportunities for investment. However, its potential will only be realised for environmental management if it is integrated into the socio-ecological technical landscape and wider discussions regarding knowledge co-production and valuing nature.

Recent outbreaks of ' Phytoplasma solani' resulted in severe losses in potatoes, vegetable crops and grapevines in certain regions of Austria and constituted a major challenge for seed potato production. Therefore, the effects of various insecticides and insect deterrents on pathogen spread were studied both in laboratory and field experiments from 2018 to 2021. In laboratory transmission experiments, field captured were caged on differently treated for five days The insecticides lambda-cyhalothrin, deltamethrin, esfenvalerate, acetamiprid and chlorpyriphos showed the most rapid impact on insect survival and fully prevented phytoplasma transmission. The particle film forming products kaolin and diatomaceous earth had some effect. A transfer of the promising laboratory results to potato fields, however, was achieved to a limited extent only. Treatments with pyrethroids and acetamiprid every 8-10 days over the flight period of roughly halved the number of symptomatic plants and tubers in case of moderately susceptible varieties and moderate infection pressure. In the event of susceptible varieties and high disease pressure, treatment effects were hardy discernible. In practical terms, the experiments indicate that insecticide applications alone are not sufficient to mitigate the disease. Spraying of diatomaceous earth and mineral oil did not affect disease incidence in the field.

Septin GTPases are morphogenetic proteins that are widely conserved in eukaryotic organisms fulfilling diverse roles in cell division, differentiation and development. In the filamentous fungal pathogen , the causal agent of the devastating blast diseases of rice and wheat, septins have been shown to be essential for plant infection. The blast fungus elaborates a specialised infection structure called an appressorium with which it mechanically ruptures the plant cuticle. Septin aggregation and generation of a hetero-oligomeric ring structure at the base of the infection cell is indispensable for plant infection. Furthermore, once the fungus enters host tissue it develops another infection structure, the transpressorium, enabling it to move between living host plant cells, which also requires septins for its function. Specific inhibition of septin aggregation-either genetically or with chemical inhibitors-prevents plant infection. Significantly, by screening for inhibitors of septin aggregation, broad spectrum anti-fungal compounds have been identified that prevent rice blast and a number of other cereal diseases in field trials. We review the recent advances in our understanding of septin biology and their potential as targets for crop disease control.

Plant pathogens are highly adaptable, and have evolved to overcome control measures including multiple classes of fungicides. More effective management requires a thorough understanding of the evolutionary drivers leading to resistance. Experimental evolution can be used to investigate evolutionary processes over a compressed timescale. For fungicide resistance, applications include predicting resistance ahead of its emergence in the field, testing potential outcomes under multiple different fungicide usage scenarios or comparing resistance management strategies. This review considers different experimental approaches to in vitro selection, and their suitability for addressing different questions relating to fungicide resistance. When aiming to predict the evolution of new variants, mutational supply is especially important. When assessing the relative fitness of different variants under fungicide selection, growth conditions such as temperature may affect the results as well as fungicide choice and dose. Other considerations include population size, transfer interval, competition between genotypes and pathogen reproductive mode. However, resistance evolution in field populations has proven to be less repeatable for some fungicide classes than others. Therefore, even with optimal experimental design, in some cases the most accurate prediction from experimental evolution may be that the exact evolutionary trajectory of resistance will be unpredictable.

Aphids hide under leaves, reproduce rapidly, and require early detection to prevent crop damage, disease transmission, and ensure effective pest management. This study presents a novel approach for aphid detection by utilizing hyperspectral imaging, multivariate classification methods and spectral information divergence (SID) analyses. The hyperspectral images average spectrum ( = 336) showed significant differences between healthy and infested leaves. Time-series classification was performed over 14 days after infestation using four distinct machine learning algorithms. Early-stage infection detection may not relate to internal physiological alterations within the leaf but rather to the physical presence of the aphid behind the leaf, obstructing subtle physiological signatures. Implementation of spectral endmembers in the VIS-NIR reference spectrum led to the identification of an informative abundance SID map within the 710-825 nm range, useful for further classification. Machine learning classification resulted in support vector machines achieving 99.20 accuracy. Using random forest, twenty-two most important variables found effective in boosting classifier performance. The selected model also extended to real-world scenarios by testing progressing infestation patterns over 14 days on independent data sets, confirming the system's reliability. Signal normal variant pre-treatment with partial least squares regression was effective in the estimation of aphid populations, achieving a 0.81 coefficient of determination ( ) and a 10.29 root-mean-square error of prediction for test datasets. In conclusion, the proposed method was able to successfully detect aphid colony infestation, both earlier and in locations that are invisible during standard human inspection.

is an invasive pest that causes substantial damage to Solanaceous crops, such as tomato and potato. This study evaluated the resistance of five commonly cultivated potato varieties to infestation in field conditions across South Africa. Morphological parameters, gas exchange, and nutrient assimilation parameters were analysed in both infested and uninfested plants. Valor exhibited the highest tolerance, showing minimal leaf damage, fewer mines, optimal gas exchange, and greater assimilation of key nutrients (e.g. iron, zinc, and manganese), which may enhance its defensive capacity against pests. Conversely, Sifra was identified as the most susceptible cultivar, with reduced nutrient uptake and significant physiological disruption in response to infestation. Infested plants showed a decrease in photosynthesis, transpiration, and stomatal conductance, along with an increase in intercellular CO, highlighting the harmful effects of . These findings support the selection of cultivars in breeding and integrated pest management programmes, especially in African contexts where field data are limited.