Search Results (74)

During phytosanitary monitoring of ornamental conifers conducted across multiple regions of Serbia, two species of spider mites, Eotetranychus libocedri (McGregor, 1936) and E. thujae (McGregor, 1950), were recorded for the first time. Eotetranychus libocedri was found on Platycladus orientalis L. (Franco), Thuja occidentalis L., and Cupressus × leylandii A.B. Jacks & Dallim, while E. thujae was detected on T. occidentalis. These records extend the distribution area of these two allochthonous species in Europe. Monitoring alien arthropod species in Europe is crucial, as they pose a risk to native flora and fauna and can cause significant economic losses. Together with previous findings, the number of registered species of the family Tetranychidae in Serbia now totals 47 across 10 genera. Full article

This study focuses on identifying barley diseases at various stages using the unique spectral signatures of phytopathogen infections. We examined the causal agents of widespread crop diseases, including: loose smut, head blight, fusarium head blight (FHB), stem rust, net blotch, spot blotch, common root rot. Analysing disease-specific spectral characteristics with machine learning (ML) algorithms revealed the most informative spectral ranges: the green region (~520–560 nm), the red chlorophyll absorption zone (~650–680 nm), and the red-edge region (~700 nm). These ranges accurately reflect alterations in the plant’s cellular structure and pigment complexes. Spectral data were processed using five ML algorithms. Random Forest (RF) proved to be the most effective for identifying and differentiating barley diseases, achieving an accuracy of up to 90.13% (MCC = 0.86). This superior performance stems from the ensemble method’s robustness to noise and its ability to extract critical features from high-dimensional hyperspectral data, particularly when distinguishing diseases with overlapping spectral signatures. Furthermore, this study highlights the potential of integrating UAV-based remote sensing to delineate reference zones, proximal hyperspectral imaging (HSI), and ML for robust plant health monitoring. This combined approach shows significant promise for early disease diagnostics, enabling site-specific treatments, curbing disease progression, and reducing pesticide application. Ultimately, these findings offer practical value for the agro-industrial sector in major grain-producing countries, especially in Central Asia, where agricultural advancement is a strategic priority for sustainable development and food security. Full article

The quarantine pathogen Phytophthora ramorum has a high potential for dispersal due to its airborne inoculum, its wide range of hosts, and its ability to spread through the trade of nursery plants. For these reasons, it represents a serious threat to ornamental nursery production and, consequently, to urban, natural and semi-natural ecosystems. This oomycete pathogen (EU1 lineage, A1 mating type) has been detected on Viburnum tinus in a commercial nursery located in the Pistoia nursery district (PND) (Tuscany, central Italy), one of the main nursery areas for the production of ornamentals in Europe. Artificial inoculations were carried out in the laboratory under controlled conditions, following a standard detached-leaf assay protocol, on leaves of 16 ornamental shrub species commonly marketed by the PND. Disease severity was assessed, and susceptibility categories (high, moderate, low, and non-susceptible) were defined based on data collected at 7 and 14 days post-inoculation and validated through statistical analysis. Inoculated species exhibited variable levels of disease severity. The results confirmed the pathogen’s high virulence on Viburnum tinus and Rhododendron hybrid ‘Madame Masson’. The following species were also found to be highly susceptible: Ilex aquifolium, Loropetalum chinense, Magnolia stellata, Osmanthus fragrans, and Trachelospermum jasminoides. Camellia japonica, Nerium oleander, Osmanthus heterophyllus, Prunus laurocerasus, and Rhododendron obtusum showed moderate susceptibility. Arbutus unedo, Laurus nobilis, Photinia fraseri and Syringa vulgaris exhibited low susceptibility. At the end of the trial, no infected species fell into the non-susceptible categories. The oomycete proved particularly aggressive on Ilex aquifolium, the most susceptible host among those tested. This high susceptibility is a new finding that could have significant epidemiological implications. Our findings emphasize the need for rigorous phytosanitary surveillance in nursery systems, based on constant monitoring and the adoption of high-throughput diagnostic protocols, in order to implement effective and rapid control measures. Full article

Cydia pomonella (Linnaeus, 1758) is considered one of the major pests affecting global pome fruit production due to its wide distribution, cryptic feeding habit, high economic impact, and continuous evolution of insecticide resistance. Historically, management of this species has relied on repeated pesticide applications, which have been associated with environmental impacts, occupational exposure, pesticide residues in food, and compromised sustainability of pesticide-dependent agricultural systems, reinforcing the relevance of integrated One Health approaches. This narrative review analyzed global management strategies for C. pomonella published between 2014 and 2024 and indexed in the Scopus, Web of Science, and SciELO databases. The reviewed studies demonstrate a gradual transition from predominantly chemical-based programs toward integrated strategies involving pheromone-based monitoring, mating disruption, biological control, and preventive plant biosecurity measures. Behavioral and biological approaches showed potential to reduce dependence on recurrent insecticide applications, particularly when associated with phytosanitary surveillance and integrated pest management programs. However, the effectiveness of these approaches remains influenced by insecticide resistance, climatic variability, and local ecological conditions. The evidence also suggests that the impacts of C. pomonella management are not limited to phytosanitary protection, involving interactions related to environmental sustainability, food safety, and human exposure to pesticides. Despite the relevance of the One Health approach, its operational incorporation into agricultural pest management remains limited, especially regarding the integration of research conducted under the One Health perspective. In this context, the sustainable management of Cydia pomonella requires integrated strategies capable of connecting phytosanitary surveillance, preventive plant biosecurity, and agricultural and ecological sustainability in order to ensure food security and population health. Full article

Agroforestry and perennial tree crop production systems, particularly in Mediterranean regions, exhibit a high degree of integration among trees, herbaceous, and soil components. They provide essential services including provisioning, regulation, support, and cultural services, which enhance human health, well-being, and economic stability. However, guaranteeing their long-term resilience in the face of environmental challenges, including drought and soil degradation, is essential for the sustainable management of these systems. We examine the impact of microsite conditions (soil and herbaceous layer) and their management on olive trees (Olea europaea L.) under varying levels of drought stress. A fully factorial design was implemented in a Spanish agroforestry system, combining two irrigation regimes (rainfed vs. summer irrigation) and two soil management practices (customary plowing vs. herbaceous layer conservation) across four independent and replicated zones. Twelve olive trees per zone were individually monitored, treating each tree as the experimental unit, with one 50 × 50 cm sampling plot per tree in which microsite conditions were characterized for each tree. Plowed areas (shallow tillage) showed lower industrial extraction yield (%), fat yield based on dry matter (%), olive maturity and phytosanitary status compared to areas conserving their herbaceous layer cover (0.81, 0.96, 0.92, and 0.65-fold lower, respectively). Rainfed areas (i.e., those without supplemental water supply) showed a reduction in both industrial extraction yield (%), olive yield (kg tree⁻¹) and oil yield (kg ha⁻¹) (0.77, 0.86 and 0.67-fold lower, respectively). Under combined tillage and water-deficit conditions, oil yield (kg ha⁻¹), industrial extraction yield (%), and total phenolic content (ppm) were considerably lower (0.50, 0.60, and 0.67-fold lower, respectively). Furthermore, low quality of the herbaceous layer dominated by nitrophilous invasive species were associated with decreased leaf nutrient content, lower industrial extraction yield, reduced olive maturity and poorer phytosanitary status of olives. These findings suggest that maintaining a spontaneous herbaceous layer with a high-quality species (legume incorporation) and well-managed herbaceous cover, i.e., repeated mowing of the herbaceous layer instead of customary plowing, can enhance sustainable olive production by improving soil resilience, reducing water stress, and optimizing nutrient use, thereby supporting long-term ecosystem stability and agricultural productivity. Full article

Plant viral diseases threaten the tomato agricultural industry. A smart decentralized diagnostic network was realized across the main Sicilian tomato-producing provinces for real-time detection/monitoring of Begomovirus solanumdelhiense (tomato leaf curl New Delhi virus—ToLCNDV), transmitted by Bemisia tabaci, Tobamovirus fructirugosum (tomato brown rugose fruit virus—ToBRFV), Orthotospovirus tomatomaculae (tomato spotted wilt virus—TSWV) and Amalgavirus lycopersici (southern tomato virus—STV). The network deployed smart portable thermocyclers and ready-to-use molecular diagnostic kits (real-time RT-LAMP, RT-qPCR). Data were remotely analyzed and in situ application of the developed kits was evaluated. Results revealed widespread STV infection (>70%) across all provinces, a variable ToBRFV presence with higher incidence in Ragusa (65%) and Siracusa (55.6%) provinces, ToLCNDV mainly concentrated in Siracusa (61.4%) and Trapani (60.2%) provinces, and localized TSWV outbreaks. ToLCNDV detection in Bemisia tabaci MED specimens confirmed the vector’s role in field transmission (up to 100% incidence). Performance comparison between laboratory and point-of-care conditions showed comparable accuracy, specificity, robustness, and rapid, cost-effective virus detection/monitoring. This diagnostic network enhances early diagnosis and timely phytosanitary interventions in tomato crops. The system supports integrated management strategies by reducing diagnostic delays and improving outbreak containment, control measures application and agroecosystem stability. Full article

Pitahaya (Hylocereus spp.) production is increasingly affected by climatic factors, as well as by phytopathogens and abiotic stress, leading to delays in agronomic interventions and reduced productivity. The objective was to design, implement, and validate a multimodal system (CARYPAR) that enables early disease detection and agile decision-making, characterized by low latency and reduced dependence on cloud connectivity. The methodology integrates climate reanalysis from NASA POWER, biophysical remote sensing variables derived from Sentinel-1/2, and proximal computer vision captured via mobile devices using a late fusion architecture and an optimized convolutional neural network, EfficientNet-V2B0, which discriminates between optimal and pathological conditions in vegetative tissues and fruit. The results of the experimental validation carried out in 160 georeferenced units achieved an overall accuracy of 80.0% and an F1 score of 0.8645 for Bad Fruit. The McNemar test and the operational agreement with agro-industrial experts yielded a Cohen’s Kappa index of κ = 0.6831, with an inference latency reduced to 22.00 ms. It is concluded that the multimodal integration of satellite bio-environmental data with edge computer vision achieves substantial agreement with agronomic expert judgment under heterogeneous field conditions (Cohen’s κ = 0.6831), supporting its role as a decision-support tool rather than a replacement for expert assessment. Therefore, its adoption can enhance real-time irrigation management and crop protection, while contributing to traceability and sustainable resource management in agricultural regions with limited connectivity. Full article

Ips typographus is one of the most destructive bark beetles affecting conifer forests in Europe, where climatic disturbances and the movement of infested wood can rapidly shift populations from endemic levels to severe outbreaks. Early detection through border inspections and forest monitoring is essential to prevent new introductions and limit the spread of established populations. Here, we developed and validated a probe-based TaqMan qPCR assay, targeting the mitochondrial COI barcode region, for the rapid and species-specific detection of I. typographus from both insect material and environmental DNA recovered from frass and exit-hole wood chips. Validation followed EPPO PM7/98(5) guidelines, assessing analytical specificity, sensitivity, repeatability, reproducibility, and inter-laboratory transferability. High analytical specificity was demonstrated against a broad panel of non-target species, and reliable amplification was obtained across different tested matrices. The method showed strong analytical sensitivity, with limits of detection of 0.32 pg/µL for adult-derived DNA and 1.6 pg/µL for artificial frass. Repeatability, reproducibility, and inter-laboratory blind testing further confirmed the diagnostic reliability of the method. This validated qPCR assay provides a rapid and sensitive molecular tool for the early detection of I. typographus, supporting border inspection and phytosanitary diagnostic laboratories in forest biosecurity activities. Full article

Reliable identification of weed species is essential for effective and sustainable weed management. In this study, we explored the use of hyperspectral imaging to distinguish nine weed species based on their spectral signatures. Although the species showed similarities in their spectral curves due to comparable growing conditions, clear differences emerged related to morphological traits and pigment composition. We analysed the spectral data using five classification algorithms: Random Forest, Support Vector Machine, Artificial Neural Network, Maximum Entropy, and SIMCA. Model performance was assessed using per-class and overall accuracy. Random Forest outperformed the other methods, achieving 93.5% accuracy despite limited and imbalanced training data. This work contributes to the development of a spectral library for weed species and demonstrates the value of machine learning for species identification across different crops and environmental conditions. Expanding such spectral databases can enhance the speed and accuracy of weed monitoring, reduce herbicide reliance, and reduce environmental impact. The proposed approach shows strong potential for integration into precision agriculture and agroecological monitoring systems, supporting more efficient and environmentally responsible farmland management. Full article

Cultivated species of the Prunus genus are of great economic importance worldwide and can be severely affected by viral diseases that compromise both yield and fruit quality. Among the most significant is Potyvirus plumpoxi (PPV), the causal agent of sharka disease, which has a direct and severe impact on stone fruit production. In this study, high-throughput RNA sequencing was employed to detect and characterize viruses present in commercial peach and apricot orchards located in different regions of Spain. After processing five samples, a total of ten viruses were identified, with PPV being the predominant virus in all analyzed samples, specifically the Marcus strain (PPV-M), which is described as one of the most aggressive PPV strains. In addition, other viruses were detected with high sequencing depth, including Luteovirus nucipersicae (nectarine stem pitting associated virus, NSPaV) and Peach-associated luteovirus (PaLV). Single-nucleotide variation (SNV) analysis of PPV-M populations revealed specific mutations distributed across the viral genome. Furthermore, phylogenetic analyses indicated the presence of multiple infection sources of European origin. These results highlight the presence of PPV-M in Spain, providing evidence of different routes of exchange of infected plant material. These findings underscore the need to strengthen monitoring programs, certification of planting material, and phytosanitary control measures to limit the dissemination of viruses and minimize their impact on stone fruit production. Full article

Woody tissue diseases of avocado (Persea americana Mill. var. Hass) pose a major phytosanitary threat due to their chronic progression, late symptom expression, and severe impact on tree stability and productivity. Although white rot has traditionally been attributed to saprobic basidiomycetes, increasing evidence suggests corticioid fungi may act as facultative pathogens in agricultural systems. This study examined corticioid basidiomycetes associated with white rot in stems and branches of avocado in Michoacán, Mexico. Field surveys revealed consistent symptoms of structural weakening, branch dieback, and wood decay. Fungal isolates obtained from symptomatic tissues and sporomes were characterized morphologically and identified through ITS-based phylogenetic analyses. Representative isolates of Grammothele spp. and Dentocorticium portoricense were evaluated in pathogenicity assays under controlled conditions. All isolates reproduced field symptoms, confirming pathogenicity, though aggressiveness varied. D. portoricense exhibited the highest incidence, severity, and AUDPC values, indicating greater virulence, while Grammothele isolates showed slower, moderate progression. Phylogenetic analyses provided robust support for D. portoricense, whereas Grammothele was resolved at genus level. Integration of field, pathogenicity, and molecular data demonstrates corticioid fungi are not merely secondary saprotrophs but relevant pathogens in avocado white rot. These findings highlight the need to include corticioid fungi in diagnostic, monitoring, and management strategies for trunk and branch diseases. Full article

The excessive use of phytosanitary products represents a growing concern, due to their persistence and potential environmental and toxicological impacts. Among these compounds, glyphosate, a glycine-derived chemical marketed as a broad-spectrum herbicide, is one of the most widely used pesticides worldwide. Breast milk is a complex biological matrix that can reflect environmental exposure, making it highly suitable for assessing glyphosate contamination. This study aimed to demonstrate a screening method to determine glyphosate concentrations in the breast milk of 100 postpartum women residing in Tupã, São Paulo, Brazil—90 in urban areas and 10 in rural areas—using high-performance liquid chromatography (HPLC) for rapid detection. By validation parameters, it was possible to verify, through the correlation coefficient (r), that the method is linear within the working range; the LD was 0.14 mg/L and the LQ was 0.43 mg/L. The recovery obtained by standard sample fortification was 92%. All analyzed samples presented detectable levels of glyphosate, indicating consistent exposure patterns and suggesting relevant environmental contamination routes in the region. These findings provide evidence of glyphosate presence in human milk and reinforce the importance of continuous monitoring strategies and preventive public health measures aimed at reducing exposure to agricultural contaminants. Full article

This article presents the results of a comprehensive study aimed at developing automated diagnostic methods for identifying spring wheat phytopathologies using hyperspectral imaging (HSI). The research aimed to create an effective plant disease detection system, including at the early stages, which is critically important for ensuring food security in regions where wheat plays a key role in the agro-industrial sector. The study analyses the spectral characteristics of major wheat diseases, including powdery mildew, fusarium head blight, septoria glume blotch, root rots, various types of leaf spots, brown rust, and loose smut. Healthy plants differ from diseased ones in that they show a mostly uniform tone without distinct spots or patches on hyperspectral images, and their spectra have a consistent shape without sharp fluctuations. In contrast, disease spectra, differ sharply from those of healthy areas and can take diverse forms. Wheat diseases with a light coating (powdery mildew, fusarium head blight) exhibit high reflectance; chlorosis in the early stages of diseases (rust, leaf spot, septoria leaf blotch) exhibits curves with medium reflectance, and diseases with dark colouration (loose smut, root rot) have low reflectance values. These differences in reflectance among fungal diseases are caused by pigments produced by the pathogens, which either strongly absorb light or reflect most of it. The presence or absence of pigment production is determined by adaptive mechanisms. Based on these patterns in the spectral characteristics and optical properties of the diseases, a classification model was developed with 94% overall accuracy. Random Forest proved to be the most effective method for the automated detection of wheat phytopathogens using hyperspectral data. The practical significance of this research lies in the potential integration of the developed phytopathology detection approach into precision agriculture systems and the use of UAV platforms, enabling rapid large-scale crop monitoring for the timely detection. The study’s results confirm the promising potential of combining hyperspectral technologies and machine learning methods for monitoring the phytosanitary condition of crops. Our findings contribute to the advancement of digital agriculture and are particularly valuable for the agro-industrial sector of Central Asia, where adopting precision farming technologies is a strategic priority given the climatic risks and export-oriented nature of grain production. Full article

Urban green spaces are essential components of city ecosystems, providing environmental and social benefits while simultaneously serving as potential entry points for invasive plant pathogens. In recent years, increasing attention has been directed toward the role of urban environments as reservoirs and transmission corridors for oomycetes, a group of highly destructive microorganisms affecting trees and shrubs. This study aimed to investigate the diversity and potential introduction pathways of oomycetes in three Warsaw parks representing distinct ecological settings: a historical city park, a large landscape park with aquatic features, and a newly constructed linear park. Samples of soil, and surface water were collected and analysed using standard isolation and molecular identification methods. Four species were identified: Phytophthora cactorum, P. cambivora, Phytopythium vexans, and Ph. montanum—the latter two representing first records for urban parks in Poland. The results indicate that nursery plant material, surface water systems, and wildlife activity, particularly birds, are likely contributors to the introduction and spread of these pathogens in city landscapes. The findings underscore the growing phytosanitary risk associated with urban greenery, where the interplay of anthropogenic disturbance, high plant turnover, and complex hydrological networks facilitates pathogen establishment. This research highlights the urgent need to integrate urban biosecurity strategies with routine molecular monitoring, nursery inspections, and wildlife surveillance to limit further dissemination of invasive oomycetes and enhance the resilience of urban tree populations. Full article

The soil microbiome is an essential component of agroecosystems. However, managing it remains a challenge due to our limited knowledge of how various environmental factors interact and shape its spatial distribution. This study presents a hierarchical ecological model to explain the assembly of the microbiome in sloping agricultural landscapes. Through a comprehensive analysis of bacterial and fungal communities, as well as the examination of metabolic and phytopathogenic profiles across a topographic gradient, we have demonstrated that topography acts as the main filter, structuring bacterial communities. Land use, on the other hand, serves as a secondary filter, refining fungal functional guilds. Our results suggest that hydrological conditions in floodplains favor the growth of stress-tolerant bacterial communities with low diversity, dominated by Actinomycetota. Fungal communities, on the other hand, are directly influenced by land use. Long-term fallow periods lead to an enrichment of arbuscular mycorrhiza, while agroecosystems shift towards pathogenic and saprotrophic niches. Furthermore, we identify specific topographic positions that may be hotspots for phytopathogenic pressure. These hotspots are linked to certain taxa, such as Ustilaginaceae and Didymellaceae, which may pose a threat to plant health. The derived hierarchical model provides a scientific foundation for topography-aware precision agriculture. It promotes stratified management, prioritizing erosion control and soil restoration on slopes, customizing nutrient inputs in fertile floodplains, and implementing targeted phytosanitary monitoring in identified risk areas. Our research thus offers a practical framework for harnessing soil spatial variability to improve soil health and proactively manage disease risks in agricultural systems. Full article