Three mango cultivars viz., Himsagar, Bombai and Langra were selected against sooty mould under natural field condition without any control measures. The Sub-Himalayan Terai region of West Bengal is experienced with a high annual rainfall (2500-3500mm) along with high humidity particularly during rainy season which may causes more chance of pest and disease infestation. There is very meagre information on scientific literature regarding the incidence of diseases and pests as well as their prevalence after shoot pruning on this sub Himalayan Terai region of West Bengal. Disease severity of mango was measured at monthly interval after shoot pruning (30cm from the shoot tip) and paclobutrazol application of plants and the cultivars were grouped under different degrees of resistance on the basis of per cent disease index (PDI) using 0-4 scale. The observations on per cent disease index (August to July) were recorded. Pooled data of 2019-20 and 2020-2021 the maximum sooty mould severity was recorded in cv. Bombai (48.11% and 38.48%) in September, followed by Langra (40.97% and 34.21%) in July month. In Himsagar (35.57% and 28.45%) disease recorded in (untreated and paclobutrazol treated plants) the month of September respectively. Where as minimum in cv. Himsagar (14.80% and 11.79%) in March followed by Langra in April month in control (15.65%) and in march month of PBZ treated plants (13.86 %). Among the all cultivars maximum average per cent disease index (Avg. PDI %) recorded in Bombai (34.93% and 25.94%) followed by Langra (27.16% and 22.84%). Whereas, minimum recorded in cv. Himsagar (26.38% and 20.05%) in control and PBZ treated plants in both the years 2019-20, 2020-21 respectively. PBZ treated plants of Himsagar showed moderate resistant reaction and control plants showed moderate susceptible reaction as well as PBZ treated plants of cultivars Langra and Bombai showed moderately susceptible reaction but control plants of same cultivars showed susceptible reaction.

Mango is a vital tropical fruit crop, yet its productivity is often reduced by leaf diseases such as Powdery Mildew, Dieback, Anthracnose, Bacterial Canker, and Sooty Mold. These infections lower yield, degrade fruit quality, and cause major economic losses. Early detection is crucial but challenging for farmers with limited expert access. This study proposes an image-based classification system using Convolutional Neural Networks (CNN) for accurate disease recognition. A curated dataset of mango leaf images was pre-processed and augmented to address class imbalance. The CNN model outperformed traditional classifiers like Support Vector Machine (SVM) and Decision Tree in terms of accuracy, robustness, and efficiency. The system not only detects multiple diseases with high precision but also offers severity estimation, visual feedback, and farmer-friendly treatment recommendations. Designed for real-time use via smartphones or field cameras, it provides a scalable and accessible solution to support precision agriculture.

Aphids are a group of insects considered to be major pests of many cultivated plant species. They are small, soft bodied, colonial, polymorphic insects with complex development cycles, very prolific, vectors for pathogens that cause serious plant diseases. They have sucking mouthparts and feed on plant sap, causing deformation and twisting of leaves in the form of pseudogalls, shortening of shoots, discoloration and yellowing, honeydew coating with the development of sooty mold and stunted growth. The aphids also affect aromatic and medicinal plants, damaging them and causing significant economic losses. This review study presents the species of aphids harmful to the most important aromatic and medicinal plants in Romania, most of which are polyphagous (12) and only 4 specific. Aspects of spread, morphology, bioecology, host plants and mode of damage are presented.

This article presents the results of phytosanitary monitoring conducted in citrus plantations across the Adjara-Guria region during the 2023-2024 growing seasons, within the framework of the Shota Rustaveli National Science Foundation of Georgia project (FR-22-2178). The survey revealed the presence of several citrus diseases caused by fungal, viral, and bacterial pathogens. This study focuses on the identification and description of major fungal diseases detected during the monitoring, including Citrus Scab (causal agent - Elsinoe fawcettii Bitanc. & Jenkins), Anthracnose (causal agent - Colletotrichum gloeosporioides Penz. & Sacc), Alternariosis (causal agent - Alternaria citri Ellis & Pierce), Melanose (causal agent - Phomopsis citri H.S. Fawc), Fusarium wilt/rot (causal agent - Nectria haematococca and Fusarium spp.), and Sooty Mold (causal agent - Capnodium citri). These diseases exhibit high pathogenicity and pose a serious threat to the sustainable development of citrus cultivation in Georgia. The article provides a detailed analysis of disease symptomatology, along with the morphological and cultural characteristics of the associated fungal pathogens.. KEYWORDS :Citrus, Disease, Fungi, Monitoring, Pathogen.

This study evaluates the impact of pre-harvest bagging on yield, quality, shelf life, disease incidence, and economic feasibility in the mango (Mangifera indica L.) cultivar Mallika conducted at the Horticulture Research Center, Pantnagar. The experiment assessed six treatments using various bagging materials (brown paper, white paper, ordinary brown paper, butter paper, newspaper, and control). Pre-harvest bagging significantly enhanced fruit attributes, with organic/UV/water-resistant brown paper yielding the highest fruit weight (630.08 g), pulp weight (530.89 g), and TSS (22.67 °Brix). It also improved ascorbic acid content (41.53 mg/100 g) and total sugars (14.64%). Phytochemical composition analysis revealed a notable increase in carotenoids, flavonoids, and phenolic content with brown paper bag (Organic/ UV/ water resistant) and white paper bag (Organic/ UV/ water resistant), indicating improved nutritional quality. Fruits bagged with brown paper extended shelf life (11.67 days) and reduced incidence of major post-harvest diseases such as anthracnose, stem-end rot, and sooty mould. Economic analysis revealed a 53.18% higher net return and improved benefit-cost ratio with bagging compared to unbagged fruits. Correlation studies highlighted positive relationships between yield, fruit weight, and quality traits, while disease resistance was negatively correlated with fruit quality attributes revealed the effectiveness of this eco-friendly practice. PCA revealed that bagging techniques enhance mango fruit quality and reduce post-harvest diseases, with PC1 explaining most of the variation. This research demonstrates that pre-harvest bagging, particularly with organic/UV/water-resistant brown paper, offers a sustainable and profitable solution for enhancing mango quality, reducing pesticide usage, and meeting global market demands for high-quality, residue-free fruits.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-27233-0.

The background of this study is grounded in the economic importance of Planococcus ficus (P. ficus) Signoret (Hemiptera: Pseudococcidae), commonly known as the vine mealybug, which is a major pest in vineyards across South Africa, the Mediterranean region, the Middle East, Argentina, California, and Mexico. This pest causes both direct damage to grapevines and indirect damage by promoting the development of sooty mold, which reduces fruit quality and marketability. The limited effectiveness of conventional pesticides—due to the pest’s concealed habitats and biological resistance—combined with their negative impacts on beneficial arthropods, underscores the need for alternative and environmentally sustainable pest management approaches. The methodology of this study involved a field trial conducted in Koruk Village, Elazığ Province, Turkey, from March to October 2022. The aim of the study is to determine the repellent and toxic effects of two types of wood vinegar (WV) and hazelnut vinegar (HV) on P. ficus populations under natural vineyard conditions by using statistical data analysis methods used in basic engineering. Various concentrations of each vinegar wereapplied to the vines, and pest population dynamics were monitored. Additionally, the potential repellent effects of the vinegars on beneficial predatory insects, particularly members of the Coccinellidae family, were assessed. The results of the study indicated that wood vinegar (WV) was more effective than hazelnut vinegar (HV) in reducing P. ficus populations. Both vinegars demonstrated statistically significant, dose-dependent reductions in pest numbers compared to the untreated control. Although both treatments also exhibited repellent effects on Coccinellidae species, these effects were not statistically significant when compared with the positive control group. These findings support the potential application of vinegar-based products in integrated pest management. The conclusion of this study is that wood vinegar (WV) and hazelnut vinegar (HV), as natural carbonization-derived products, can serve as environmentally friendly alternatives for controlling P. ficus in vineyards. Their application may reduce reliance on synthetic pesticides, contribute to sustainable viticulture practices, and minimize negative impacts on non-target beneficial organisms. This research introduces an innovative, eco-compatible control that could be effectively integrated into broader Integrated Pest Management (IPM) strategies.

ABSTRACT Trichomeriaceae, the most common sooty mold family under Chaetothyriales, are most common in tropical to subtropical regions worldwide. However, no trichomeriaceous fossil fungi have been reported until now. Here, we report for the first time trichomeriaceous fossil fungi on cuticle fragments of a compressed angiosperm (cf. Fagaceae) leaf recovered from the middle Siwalik (late Miocene; ca. 12–8 Ma) flora of Himachal Himalaya. They are characterized by superficial, septate, cylindrical hyphae; circular, setiferous ascostromata; a 3-septate ascospore; and a 3-armed stauroconidium. The above significant morphological attributes reveal a close resemblance of the Siwalik fungi with modern Trichomerium (Eurotiomycetes: Chaeothyriales: Trichomeriaceae). Here, we describe two new fossil species, namely, Trichomerium palaeoindicum Kundu & Khan, sp. nov. (sexual morph), and T. palaeostauroconidium Kundu & Khan, sp. nov. (asexual morph). Additionally, we discuss their paleoecological significance in terms of the present-day ecological conditions of modern analogues. These new findings play a great role in the evolutionary context and diversification of the Trichomeriaceae in time and space.

Coffea arabica (Rubiaceae) is one of the most popular coffees worldwide (Zhu et al. 2024). In April 2024, symptoms of sooty mold appeared in the largest coffee-growing region in Yunnan Province, China (22°66'N; 100°96'E; 1073 m). The disease initially appears as a black powdery coating on coffee leaves, cherries, and twigs, later forming a thick, adherent layer that cracks or peels off under dry conditions. A field survey was conducted over approximately 20 hectares (300 mu) of coffee plantations, and fresh symptomatic leaves were collected and observed. Micro and macro photographs were prepared for photomicrography. Pure cultures were obtained following the methods in Senanayake et al. (2020). Pure cultures were grown on potato dextrose agar at 28 °C for one month and used for DNA extraction and the pathogenicity test. Morphology and multi-locus phylogeny confirmed that the sooty mold is a distinct new species in Trichomerium: Trichomerium puerense L. Lu, K.D. Hyde & Tibpromma, sp. nov. MycoBank number: MB 857498. Etymology: The epithet refers to the location "Pu'er" from where the holotype was collected. Holotype: MHZU 24-0582. It forms black sheets which are superficial and thin, cover the leaves, and are composed of cylindrical hyphae. Superficial hyphae 4-6 μm wide also form, are septate, constricted at the septum, unbranched, brown, dense, with cylindrical hyphal cells, and produce guttules. Ascostromata are 100-150 × 120-150 μm, superficial, solitary, globose to subglobose, and brown to black. Setae are 40-80 × 4-7 μm, straight or flexuous, brown, and septate. Peridia are 15-20 μm wide, thick-walled, hyaline to brown, and comprised of textura angularis cells. Asci are 45-70 × 20-28 μm (n = 30), 8-spored, bitunicate, ellipsoid to clavate, or cylindrical, with a short pedicle and an ocular chamber. Ascospores are 20-25 × 7-10 μm (n = 10), fusoid to ellipsoid, hyaline, 1-3-septate, constricted at the septum, with narrow ends, somewhat tapering towards the base when mature, smooth-walled, and produce guttules. DNA fragments were amplified and sequenced with ITS, LSU, and SSU (Hyde et al. 2020). The sequences were deposited in GenBank (ITS, PV056513, PV056514; LSU, PV056517, PV056518; SSU, PV056515, PV056516). The morphology of the collection (sm4) was consistent with Trichomerium (Rana et al. 2019; Hyde et al. 2020). Maximum likelihood and Bayesian Inference analyses on CIPRES confirmed that our isolates formed a distinct lineage within Trichomerium with 100% ML/1.00 PP statistical support. Based on morphology and phylogeny, sm4 was identified as a new species, T. puerense. To fulfill Koch's postulates (Xu et al. 2019), ten healthy arabica coffee plants were selected. Five plants were inoculated with two 3-mm fungus plugs per leaf sheath, and the other five plants were inoculated with PDA agar plugs (control group). All plants were sealed in transparent plastic bags and incubated in a greenhouse at 26 ± 2 °C for 28 days, with daily moisturizing using a 5% bee honey solution (with relative humidity maintained at 70%). Four weeks after inoculation, black sooty mold spread on the leaves that were inculated with the fungus plugs, while no symptoms were observed on the control leaves. This study represents the first report of T. puerense associated with coffee sooty mold in China. This finding will facilitate further research on defense strategies against coffee sooty mold and support the development of improved management practices for this disease.

Orange spiny whitefly (Aleurocanthus spiniferus Quaintance) is a major pest with economic significance to tea plants, as both nymphs and adults suck plant sap and contribute to the development of tea sooty mold. The occurrence of this pest varies considerably among different tea cultivars, even within the same plantation. This study aims to characterize the bioactive constituents of tea volatiles mediating A. spiniferus host selection, and leverage these semiochemicals to develop effective field trapping systems. Through field investigations and Y-tube olfactometer tests, we identified two highly preferred tea cultivars ('Huangjinya' and 'Fuding white tea') and two cultivars ('Baiye No. 1' and 'Longjing 43') that were not preferred. Behavioral assays and gas chromatography-mass spectrometry (GC-MS) analysis revealed four attractive compounds [hexanol, (E)-2-hexenal, linalool, and (E,E)-α-farnesene] and two repellent compounds [nonanal and (Z)-3-hexenol] in the volatile emissions of the four cultivars. A hexane solution (10 µL) of nonanol, (Z)-3-hexenol, linalool, and (E,E)-α-farnesene at a concentration of 100 µg/µL was able to elicit an obvious electrophysiological (EAG) response. In field trials, the synergistic bait trap equipped with two types of attractants, 500 µL of hexane solution of the mixture of linalool and (E,E)-α-farnesene (3:1, v/v), and the mixture of linalool and (Z)-3-hexenol (3:1, v/v) at the concentration of 100 μg/μL, showed significantly higher attractant efficacy and selectivity. Overall, this study indicates that tea volatiles play a crucial role in the host selection of A. spiniferus, and the synthetic mixtures of tea volatiles have the potential to be developed as commercial plant-based attractants for adult A. spiniferus. This study contributes to the development of sustainable, environmentally friendly management strategies for a pest that is challenging to prevent and control.

In the digital era, it is necessary to develop an online booklet on crystal guava pests and diseases. Pests and diseases are major constraints on agricultural productivity, disrupting plant physiology and growth, and thus reducing yields. The objective of this research is to develop an online booklet on crystal guava pests and diseases and how to manage them. Data collection was conducted through observation and interviews with owners and managers to obtain information on pests and diseases. Data were analyzed using descriptive qualitative methods. The study identified five pests of crystal guava: fruit fly (Bactrocera dorsalis), ladybug (Epilachna admirabilis), leaf caterpillar (Lepidoptera: Pyralidae), armyworm (Spodoptera litura), and mealybug (Paracoccus marginatus). The diseases identified were: Capnodium citri fungus (sooty mold), anthracnose disease (Colletotricum sp), red rust disease (Chephaleuros virescens), white fungus Fusarium sp, fruit rot disease (Botryodiplodia sp), and leaf spot disease (Cercospora psidii). Crystal guava care is carried out by maintaining garden cleanliness, adjusting plant spacing, fertilizing, spraying insecticides, setting fly traps, and wrapping young fruit. The conclusion of this research can be developed into a YouTube-based booklet as a guide for crystal guava farmers.

Applying the transfer learning models on the dataset on the effect of diseases on Nagvel-betel (Piper betle) leaves

This study presents the first record of the spruce shoot aphid, Cinara pilicornis (Hartig, 1841) (Hemiptera: Aphididae: Lachninae), in South Korea. Native to Europe, C. pilicornis has expanded its distribution globally and is recognized as a significant quarantine pest in South Korea, posing substantial ecological and economic risks to native spruce trees and forestry ecosystems. Through detailed morphological and molecular analyses, including scanning electron microscopy (SEM) and mitochondrial COI gene sequencing, the identity of C. pilicornis was confirmed. Phylogenetic analyses (BI and NJ) and species delimitation methods (ABGD, ASAP, and bPTP) further validated the species classification, with all South Korean populations belonging to haplotype 1 a putative ancestral haplotype widely distributed across other regions. Population analyses revealed limited genetic diversity in South Korea, suggesting a recent introduction. Climatic niche modeling indicated that C. pilicornis has the potential to establish populations in temperate and subtropical regions, including Europe, North America, coastal South America, and East Asia. Observations of colony behavior on Picea abies revealed high honeydew production and the formation of black sooty mold, causing visible damage to host plants. This study underscores the importance of strengthening quarantine measures and monitoring native spruce trees in national parks to mitigate the spread and impact of this invasive pest. Effective management strategies are essential to prevent further ecological disruption and economic losses caused by C. pilicornis.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-12656-6.

Even in the age of digitalisation and capitalism, agriculture still plays a significant role in many economies, such as in certain Asian countries where mangoes have become an important export commodity. However, plant diseases put serious constraints on both productivity and quality. Existing methods for identifying disease typically rely on the experience of farmers and are time-consuming and error-prone. In this study, we propose a new hybrid framework consisting of a custom Convolutional Neural Network (CNN) and a Support Vector Machine (SVM) classifier to classify eight mango leaf conditions: Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Healthy, and Sooty Mould. The model uses a dataset of 4000 images collected from mango orchards throughout Bangladesh and incorporates rigorous pre-processing and data augmentation to help improve model robustness and generalisability. The results indicate that the hybrid CNN-SVM model performs best, outperforming state-of-the-art models with an accuracy of 99.75%. The research thus emphasises the role of deep learning and machine learning in enabling more accurate disease detection in agriculture, benefitting farms and the environment via sustainable practices and higher crop yields. KEYWORDS Classification, custom CNN model, deep learning, disease detection, feature extraction, machine learning

Soursop (Annona muricata) is a valuable tropical fruit crop that is highly susceptible to leaf diseases caused by fungal, bacterial, and viral infections. These diseases can significantly impact crop yield and quality, posing challenges for farmers, especially when early detection is delayed. This study proposes an automated solution using Convolutional Neural Networks (CNNs) to detect soursop leaf diseases through image classification. A dataset of 400 labelled leaf images, including healthy and diseased leaves (Leaf Rust, Leaf Spot, and Sooty Mold), was collected and preprocessed for the dataset. Three CNN architectures—MobileNetV2, VGG19, and ResNet50—were evaluated based on accuracy, precision, recall, and F1-score. Among them, MobileNetV2 outperformed the others, achieving 73% accuracy, 72% precision, 65% recall, and 66% F1-score and demonstrated strong consistency across classes. The best-performing model was deployed using the Flask web framework, enabling users to upload soursop leaf images and receive instant disease classification along with suggested treatments and preventive measures. This study’s novelty lies in the end-to-end pipeline, from model training to deployment via Flask, providing a ready-to-use solution for farmers.

The paraphyletic order Capnodiales s. lat. was recently divided into eight orders based on phylogeny, morphology, and fungal lifestyle. As a consequence, Capnodiales s. str. was redefined to include fungi called sooty moulds, which grow on sugary exudates (honeydew) excreted by sap-feeding insects. Despite some exceptions, Capnodiales s. str. constitutes a highly specialized order comprising fungi adapted to this specific habitat. The order includes many genera, of which one of the best studied is the genus Chaetocapnodium. To date, Chaetocapnodium has accommodated 10 species, which are strictly sooty moulds. In this study, two novel species of Chaetocapnodium are described from Poland-Chaetocapnodiummagnum and Chaetocapnodiumpolonicum-based on morphology and multi-gene phylogenetic analyses. Importantly, both species were isolated from resin flows of conifers, which represents a new ecological niche for the genus Chaetocapnodium as well as for the entire order Capnodiales s. str. Members of the genus Chaetocapnodium were previously reported mostly from the tropics or subtropics (exceptionally from the sub-Antarctic), and therefore they are reported from the temperate zone in Europe for the first time.

BackgroundIntroduction of non-native insect species and extreme wildfire events threaten terrestrial ecosystems and their services worldwide. However, the effect of invasive sap-feeding insect species outbreaks on fire severity is poorly understood, particularly regarding their effects on fire behavior and the probability of crown fire ignition. We set up two experimental designs to investigate how the alien tortoise scale Toumeyella parvicornis influenced fire behavior dynamics and canopy surface reflectance in Mediterranean Pinus pinea stands that were severely burnt in the summer of 2017. We combined Rothermel’s model for fire surface spread and Van Wagner’s crown ignition model to simulate fire behavior and employed data from the Landsat 8 collection to detect canopy wilt symptoms related to the multivoltine T. parvicornis abundance.ResultsSimulating fire behavior in single-story P. pinea thinned and unthinned stands indicated that all the predicted fires were surface fires. Uncertainty analysis of the canopy fuel attribute model inputs revealed that fires in thinned stands were entirely classified as surface fires. In contrast, only 62.7% were surface fires in unthinned stands, whereas 37.3% were categorized as conditional fire types. Among the Landsat reflectance bands, only the NIR, green, and SWIR 2 were sensitive to the abundance of T. parvicornis. Based on these sensitive bands, two-band NIR-multiplied vegetation indices were significantly associated with the abundance of T. parvicornis from the fall generation onward when sooty mold consistently covered the canopy needles.ConclusionThe divergence between observed and predicted fires in pine stands highlights the need to investigate the processes and variables linked to T. parvicornis feeding activity on P. pinea trees to enhance fire behavior prediction. Therefore, understanding how insect outbreaks can modify fire behavior in pine stands is crucial for effective management at the local and landscape levels. Identifying the vegetation index based on sensitive bands represents an essential step toward the early recognition of insect outbreaks on a large spatial scale.

Citrus farming, particularly Mandarin orange cultivation, is a crucial economic activity in Nepal’s hilly regions. However, disease detection and management remain major challenges. This study presents an efficient method for identifying and controlling five key citrus diseases affecting the Nepali orange market: black spot, canker, Huanglongbing (HLB), leaf miner, and sooty mold. We employ the MobileNetV2 model for disease prediction and a One-Class SVM model for initial leaf classification. Additionally, we integrate a Llama-3.2-11bvision RAG-based chatbot, which analyzes leaf images and provides real-time guidance on disease prevention and orchard management. A mobile application has been developed to integrate the chatbot with a user-friendly interface, making it accessible for farmers. Our approach achieves 95.6% accuracy in disease identification and 85.6% accuracy in orange leaf classification. With its intuitive mobile platform and AI-driven chatbot, this system has the potential to transform citrus farming in Nepal by enabling timely interventions and improved disease management.

Novel species of fungi described in this study include those from various countries as follows: Argentina, Septoria reinamora on leaf spots of Mutisia spinosa. Australia, Cortinarius albofolliculus on mossy soil, Cortinarius descensoriformis among leaf litter, Cortinarius kaki among leaf litter, Cortinarius lissosporus in leaf litter, Cortinarius malogranatus in leaf litter, Cortinarius meletlac on soil in mixed forest, Cortinarius sebosioides in long decayed wood litter, Helicogermslita australiensis as an endophyte from healthy leaves of Archontophoenix cunninghamiana, Puccinia clemensiorum on culms of Eleocharis ochrostachys, Puccinia geethae on leaves of Cyperus brevifolius, Puccinia marjaniae on leaves of Nymphoides indica, Puccinia scleriae-rugosae on leaves of Scleria rugosa. Brazil, Dactylaria calliandrae on living leaf of Calliandra tweediei, Mucor cerradoensis from soil, Musicillium palmae on living leaves of unidentified palm species, Neodendryphiella agapanthi from stalks of Agapanthus praecox, Parafusicladium riodejaneiroanum on living leaves of native bamboo, Parapenidiella melastomatis on living leaves of unidentified Melastomataceae, Pararamichloridium ouropretoense on living leaves of unidentified Poaceae, Pentagonomyces endophyticus (incl. Pentagonomyces gen. nov.) as endophytic from roots of Musa acuminata, Polyschema endophytica from healthy roots of coffee plant, Purimyces endophyticus as root endophyte of Cattleya locatellii, Ramularia rhododendri on living leaves of Rhododendron sp., Staphylotrichum soli from soil, Trichoderma sexdentis from leaves inside a nest of the leaf-cutting ant Atta sexdens rubropilosa, Wiesneriomyces soli from soil. France, Cosmospora nemaniae on dead or effete stromata of Nemania cf. colliculosa, Inocybe alnobetulae in subalpine green alder stands, Stylonectria hygrophila on dead twigs of Betula pubescens. Germany, Coniochaeta corticalis from bark humus, Coniochaeta fermentaria from fermentation residues from biogas plants, Coniochaeta fibricola from softwood fibres, Coniochaeta weberae from bark humus, Inocybe canicularis on calcareous to more acidic soil with conifers. Iceland, Inocybe islandica associated with Dryas octopetala. India, Vishniacozyma indica on dead twigs. Iran, Botryotrichum lycii on rotten leaf of Lycium depressum. Italy, Cuphophyllus dolomiticus among Salix retusa, Salix reticulata and Dryas octopetala, Inocybe subentolomospora on moss with the presence of Alnus incana, Populus nigra and Salix spp. Malaysia, Catenulostroma pellitae on leaf spots of Eucalyptus pellita. Mexico, Colletotrichum mexicanus from fruit of Persea americana cv. Hass. New Caledonia (France), Cortinarius caeloculus, Cortinarius luteigemellus and Cortinarius perpensus on soil under Nothofagus aequilateralis. New Zealand, Cytospora braithwaitei on branch of Malus domestica. Pakistan, Callistosporium khalidii on humus soil, Entoloma lilacinum on litter in conifer forest, Laccaria decolorans on litter in broad-leaved subtropical forest. Poland, Pseudoneoconiothyrium modrzynanum from resin of Larix decidua ssp. polonica, Tuberculiforma enigmatica isolated from sooty mould community on Quercus robur leaves. Portugal, Clavulus hemisphaericus (incl. Clavulus gen. nov.) on mossy slopes and under Laurus leaf litter, Entoloma daegae on sandy, granitic soil, Hygrocybe aurantiocitrina under laurel forest, Hygrocybe sanguineolutea gregarious in laurel forest, Hygrocybe vulcanica on mossy areas of laurel forest areas, Pachyphlodes algarvensis on sandy soil under Cistus salvifolius, Quercus suber and Pinus pinea. South Africa, Amycosphaerella podalyriae on leaf of Podalyria calyptrata, Erythrobasidium eucalypti from the gut of Gonipterus sp., Letendraea goniomae on leaves of Gonioma kamassi, Pezicula brabeji and Sphaerulina brabeji on twigs of Brabejum stellatifolium, Stachybotrys conicosiae on dead flower head of Conicosia elongata, Talaromyces ignescens from soil. Spain, Cortinarius phaeobrunneus on soil under Quercus ilex and Q. faginea, Inocybe pini-halepensis among grass and fallen leaves of Pinus halepensis, Inocybe subporcorum in sandy soils under Quercus ilex subsp. ballota and Pinus pinaster, Mycena morenoi on dead leaves of Betula pubescens and Salix atrocinerea, Pachyphlodes iberica on clayey and loamy soil under Quercus ilex and Quercus rotundifolia, Ramariopsis coronata in laurel forest. Switzerland, Inocybe minata in a bog on very wet acidic soil with Salix spp. and Betula spp. Thailand, Hypocrella khonsanitii on scale insects (Coccidae), Petchiella hymenopterorum on hymenopteran pupae in the nest (Hymenoptera). Trinidad and Tobago, Neodevriesia maravalensis from office swab. Türkiye, Russula anatolica under Quercus vulcanica. UK, Paracylindrosporium dactylorhizae (incl. Paracylindrosporium gen. nov.) on leaf spots of Dactylorhiza sp., Niesslia hepworthiae and Niesslia libertiae on living leaves of Libertia grandiflora. Ukraine, Lichenohendersonia cetrariae on thallus of terricolous Cetraria aculeata. USA, Atromagnispora indianensis (incl. Atromagnispora gen. nov.) on submerged wood in a freshwater stream, Cytospora michiganensis from utility room (settle plate), Exophiala aeris from air (settle plate), Hongoboletus americanus from mixed pine-hardwood forest, Lorrainsmithia pennsylvanica from bedroom, air, Superstratomyces massachusettsanus from lyse buffer. Vietnam, Aspergillus halopiscium on dry marine anchovy Stolephorus commersonnii. Morphological and culture characteristics are supported by DNA barcodes.

Mango is an important tropical and subtropical fruit crop in India. However, due to climate change and nonadoption of good management practices in mango leads to increased pest and disease incidence. Hence, farmers are using pesticides indiscriminately to control these pests leads to pesticide residues on fruits which is a major hurdle for exports of mango fruits from India. Preharvest fruit bagging possess prospectus in mango to avoid pest and disease incidence thereby increasing the quality of fruit. Hence, the field experiment was conducted at Mango Research Station Nuzvid, Dr. YSR Horticultural University during 2021 to 2024 for four years to study the effect of pre-harvest fruit bagging on quality and pest and disease incidence in mango cultivar chinnarasam which is a popular commercial variety of Andhra Pradesh. The experiment was laid out in Randomized Block Design (RBD) with 3 replications and 7 treatments consisted three colours of bags and two variants in each colour and control as no bagging. The findings clearly indicated that among the treatments, T4 (Yellow colour with flip) recorded significantly maximum content of ascorbic acid (54.25 mg/100g), which was on par with T2 (Reddish brown with flip) while minimum content (27.42 mg/100g) was found in T7 (Control). Both reddish brown and yellow colour were superior for nonreducing sugars total sugars followed by white bags. Thrips and sooty mould damage was recorded minimum (10.19% and 10.96%, respectively) in reddish brown bags, which was followed by yellow and white bags. Fruit fly damage was not found in bagged fruits while control recorded highest incidence of 42.33%. Based on these results, it can be concluded that pre-harvest bagging of fruits with reddish brown or yellow colour found to be best for improving the fruit quality and to avoid pests and disease incidence thereby pesticide residues in mango.

Citrus sooty mould caused by Capnodium spp. alters the quality of fruits on the tree and affects their productivity. Past laboratory and hand-held spectrometry tests have concluded that sooty mould exhibits a typical spectral response in the near-infrared spectrum region. For this reason, this study aims at developing an automatic method for remote sensing of this disease, combining 10 m spatial resolution Sentinel-2 satellite images and 0.25 m spatial resolution orthophotos to identify sooty mould infestation levels in small orchards, common in Mediterranean conditions. Citrus orchards of the Comunitat Valenciana region (Spain) underwent field inspection in 2022 during two months of minimum (August) and maximum (October) infestation. The inspectors categorised their observations according to three levels of infestation in three representative positions of each orchard. Two synthetic images condensing the monthly information were generated for both periods. A filtering algorithm was created, based on high-resolution images, to select informative pixels in the lower resolution images. The data were used to evaluate the performance of a Random Forest classifier in predicting intensity levels through cross-validation. Combining the information from medium- and high-resolution images improved the overall accuracy from 0.75 to 0.80, with mean producer’s accuracies of above 0.65 and mean user’s accuracies of above 0.78. Bowley–Yule skewness coefficients were +0.50 for the overall accuracy and +0.28 for the kappa index.