Explore the vast range of titles available.

Quinone outside inhibitors (QoI) fungicides group were introduced for commercial use against apple scab (Venturia inaequalis) in Israel in 1997. Unlike other regions in the world, in which resistance of V. inaequalis to QoI fungicides was observed within 3–5 years of use, in Israel it only occurred after 14 years of use. Field trials conducted between 2007 and 2017 showed a significant reduction in susceptibility to QoIs in northern Israel only since 2011. The delay in the development of resistance is related to limited fungicidal sprays resulting from unfavorable conditions for the pathogen. Of the 28 isolates collected from infected leaves or fruits of commercial orchards in northern Israel, 27 were resistant to the QoI fungicide Kresoxim-methyl. Amplification of the CYTB gene and sequencing of the G143A mutation region confirmed the resistance of all 27 isolates to QoIs. Resistance is demonstrated in the orchard, in vitro and molecular-based study, which forced the growers to avoid using QoIs against apple scab. We show that foliar applications of tank mixtures of systemic fungicides plus captan or prepacked fungicidal mixtures improved efficacy and can be used as a strategic approach in fungicide resistance management, including in orchards in which resistance to QoIs has been detected.

Progenies of ‘Schmidt's Antonovka’ (SA) have been widely used in Western breeding programs as a source of scab resistance. The identity of SA has remained obscure, especially due to the existence of a series of ‘Antonovka’ cultivars with different origins. In this paper we show Schmidt's Antonovka to be identical to Анто́новка обыкновенный or ‘Common Antonovka’ (CA), an old Russian cultivar of unknown origin, by comparing simple sequence repeat (SSR) and SNP genotyping data from several first-generation descendants of SA from two European collections and a CA accession from the germplasm collection held at VNIISPK (The All-Russian Research Institute of Horticultural Breeding, Orel, Russia). The use of CA in Russian breeding programs is also briefly reviewed.

Premise Apple orchards in the United States are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs and increased environmental and health impacts. Methods and Results We have manually captured 3651 high‐quality, real‐life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset of images, expert‐annotated to create a pilot data set for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for the Plant Pathology Challenge as part of the Fine‐Grained Visual Categorization (FGVC) workshop at the 2020 Computer Vision and Pattern Recognition conference (CVPR 2020). Participants were asked to use the image data set to train a machine learning model to classify disease categories and develop an algorithm for disease severity quantification. The top three area under the ROC curve (AUC) values submitted to the private leaderboard were 0.98445, 0.98182, and 0.98089. We also trained an off‐the‐shelf convolutional neural network on this data for disease classification and achieved 97% accuracy on a held‐out test set. Discussion This data set will contribute toward development and deployment of machine learning–based automated plant disease classification algorithms to ultimately realize fast and accurate disease detection. We will continue to add images to the pilot data set for a larger, more comprehensive expert‐annotated data set for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.

The automatic detection of diseases in plants is necessary, as it reduces the tedious work of monitoring large farms and it will detect the disease at an early stage of its occurrence to minimize further degradation of plants. Besides the decline of plant health, a country’s economy is highly affected by this scenario due to lower production. The current approach to identify diseases by an expert is slow and non-optimal for large farms. Our proposed model is an ensemble of pre-trained DenseNet121, EfficientNetB7, and EfficientNet NoisyStudent, which aims to classify leaves of apple trees into one of the following categories: healthy, apple scab, apple cedar rust, and multiple diseases, using its images. Various Image Augmentation techniques are included in this research to increase the dataset size, and subsequentially, the model’s accuracy increases. Our proposed model achieves an accuracy of 96.25% on the validation dataset. The proposed model can identify leaves with multiple diseases with 90% accuracy. Our proposed model achieved a good performance on different metrics and can be deployed in the agricultural domain to identify plant health accurately and timely.

Background Scab, caused by the biotrophic fungus Venturia inaequalis , is the most economically important disease of apples worldwide. During infection, V. inaequalis occupies the subcuticular environment, where it secretes virulence factors, termed effectors, to promote host colonization. Consistent with other plant-pathogenic fungi, many of these effectors are expected to be non-enzymatic proteins, some of which can be recognized by corresponding host resistance proteins to activate plant defences, thus acting as avirulence determinants. To develop durable control strategies against scab, a better understanding of the roles that these effector proteins play in promoting subcuticular growth by V. inaequalis , as well as in activating, suppressing, or circumventing resistance protein-mediated defences in apple, is required. Results We generated the first comprehensive RNA-seq transcriptome of V. inaequalis during colonization of apple. Analysis of this transcriptome revealed five temporal waves of gene expression that peaked during early, mid, or mid-late infection. While the number of genes encoding secreted, non-enzymatic proteinaceous effector candidates (ECs) varied in each wave, most belonged to waves that peaked in expression during mid-late infection. Spectral clustering based on sequence similarity determined that the majority of ECs belonged to expanded protein families. To gain insights into function, the tertiary structures of ECs were predicted using AlphaFold2. Strikingly, despite an absence of sequence similarity, many ECs were predicted to have structural similarity to avirulence proteins from other plant-pathogenic fungi, including members of the MAX, LARS, ToxA and FOLD effector families. In addition, several other ECs, including an EC family with sequence similarity to the AvrLm6 avirulence effector from Leptosphaeria maculans , were predicted to adopt a KP6-like fold. Thus, proteins with a KP6-like fold represent another structural family of effectors shared among plant-pathogenic fungi. Conclusions Our study reveals the transcriptomic profile underpinning subcuticular growth by V. inaequalis and provides an enriched list of ECs that can be investigated for roles in virulence and avirulence. Furthermore, our study supports the idea that numerous sequence-unrelated effectors across plant-pathogenic fungi share common structural folds. In doing so, our study gives weight to the hypothesis that many fungal effectors evolved from ancestral genes through duplication, followed by sequence diversification, to produce sequence-unrelated but structurally similar proteins.

Sprinkler irrigation systems can release ascospores of Venturia inaequalis , the cause of apple scab, from infected leaves on the ground under conditions unsuitable for infection, and thus reducing the primary inoculum. Under-canopy irrigation was carried out for two hours in the middle of the day over overwintered apple leaves heavily infected with scab, either in a wind-protected enclosure or in a wind-exposed orchard. Ascospores were captured with rotating-arm spore traps at heights ranging from 0.3 m to 3.0 m above the ground. Ascospores dispersed above the irrigated layer and were detected at all heights above the sprinklers. Wind played a critical role in spore transport, evident from the set-up where wind interference was minimised by a wind fence, resulting in higher airborne spore numbers across all measured heights compared with the orchard exposed to unrestricted wind conditions. Furthermore, vertical temperature gradients significantly correlated with spore distributions, particularly where negative gradients at heights between 0.3 m and 0.05 m and positive gradients at heights between 1.0 m and 0.3 m led to spore retention within the irrigated zone. The findings highlight that ascospores, dispersed above the irrigated layers, could settle on susceptible tissues. It thus becomes imperative to ensure a rain-free period of at least 24 h post-irrigation and, if a rainfall shortly occurs after irrigation, the application of curative fungicides becomes essential following unexpected rain. Reliable weather forecasts are therefore crucial in determining the effectiveness of under-canopy irrigation to reduce apple scab incidence.

The apple ( Malus x domestica ) scab ( Venturia inaequalis ) resistance genes Rvi4 and Rvi15 were mapped to a similar region on the top of linkage group 2 and both resistance genes elicit the same type of resistance reaction, i.e., a hypersensitive response; hence, it is suspected that the two genes may be the same. As the two resistance genes Rvi4 and Rvi15 are currently used in apple breeding, it is important to clarify whether the two resistance genes are the same or not. Several approaches were used to make this determination. First, the pedigree of the genotype GMAL 2473, the source of Rvi15 , was reconstructed. GMAL 2473 was found to be an F1 of ‘Russian seedling’, the genotype, which is known to also be the source of Rvi4 . Next, it was further demonstrated that ‘Regia’, a cultivar known to carry Rvi4 (and Rvi2 ), carries the same gene ( Vr2-C ), which was demonstrated to be the gene inducing Rvi15 resistance. Finally, it was shown that transgenic lines carrying Vr2-C are compatible with race 4 apple scab isolates. Taken all together, these results definitively demonstrate that Rvi4 and Rvi15 are the same resistance gene. For future studies, we suggest referring to this resistance with the first name that was assigned to this gene, namely Rvi4 .

In Agriculture Sciences, detection of diseases is one of the most challenging tasks. The mis-interpretations of plant diseases often lead to wrong pesticide selection, resulting in damage of crops. Hence, the automatic recognition of the diseases at earlier stages is important as well as economical for better quality and quantity of fruits. Computer aided detection (CAD) has proven as a supportive tool for disease detection and classification, thus allowing the identification of diseases and reducing the rate of degradation of fruit quality. In this research work, a model based on convolutional neural network with 19 convolutional layers has been proposed for effective and accurate classification of Marsonina Coronaria and Apple Scab diseases from apple leaves. For this, a database of 50,000 images has been acquired by collecting images of leaves from apple farms of Himachal Pradesh (H.P) and Uttarakhand (India). An augmentation technique has been performed on the dataset to increase the number of images for increasing the accuracy. The performance analysis of the proposed model has been compared with the new two Convolutional Neural Network (CNN) models having 8 and 9 layers respectively. The proposed model has also been compared with the standard machine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimental results, it has been observed that the proposed model has outperformed the other CNN based models and machine learning models with an accuracy of 99.2%.

Background Venturia inaequalis is an apple scab causing fungal pathogen. It is a highly contagious and destructive pathogen which rapidly spreads infection in the surrounding orchards if not managed. The management and control of disease require multiple fungicides to be sprayed at different development stages of the apple. Persistent applications of fungicides also raises environmental concerns. Here, we demonstrate the potential of using spray induced gene silencing (SIGS) by developing target specific gene constructs for the synthesis of corresponding double-stranded RNA (dsRNA). Methods and Results The exogenous application of dsRNAs was found to reduce mycelial growth and spore formation of V. inaequalis on culture plates. Four genes of V. inaequalis viz. CIN1, CE5, VICE12 and VICE16 which get upregulated during infection, were selected as targets for the development of gene construct expressing the corresponding dsRNA. The effect of exogenously supplied in vitro synthesized dsRNA on V. inaequalis was assessed in culture bioassay experiments with respect to growth, and spore formation. The expression level of the target genes in treated and control fungus was evaluated using quantitative PCR. Fungus treated with VICE12 targeted dsRNA showed maximum reduction in colony size (~ 55%), conidia formation (~ 93%) and expression level of the corresponding gene (2.2 fold), which was followed by CIN1 -dsRNA. VICE16 -dsRNA treatment was least effective with 32% reduction in growth, the non-significant effect of conidial spore formation and 1.13 fold down regulation of corresponding target gene expression level. Conclusion The result of this investigation validates the hypothesis that RNAi is evoked in V. inaequalis by exogenously supplied dsRNA and spray induced gene silencing (SIGS) based solutions may reduce burden of fungicide usage on apple crop against apple scab disease in future.

Context The composition and configuration of habitats in agricultural landscapes may determine crop damage resulting from pests or pathogens either by directly affecting their population dynamics or through indirect effects on their natural enemies. Objectives The aim of this study was to assess the impact of landscape composition and configuration on the occurrence and damage caused by the codling moth and apple scab in apple orchards. Methods Using monitoring data at the French national scale, we examined how the proportion of landscape area grown with orchards, the mean patch area of orchards, the share of organic orchards and the proportion of woodlands and grasslands affected the occurrence and damage of these two pests from 2015 to 2019 in approximately sixty apple orchards each year. Results Landscapes with a higher proportion of orchards supported a higher occurrence of apple scab and earlier colonisation of codling moths. In addition, we found that codling moth damage decreased with increasing orchard patch area in the landscape. The proportion of seminatural habitats or organic farming in the landscape never significantly explained pest occurrence or damage. Conclusions Our results clearly highlight the importance of considering the amount and spatial arrangement of the pests’ and pathogens’ host crops to understand their infestation levels. Our study calls for the territorial management of orchard distribution to limit pesticide use in apple orchards.