Search Results (47)

Volatile organic compounds (VOCs) produced by Medicinal Aromatic Plants (MAPs) are bioactive signaling molecules that play key roles in plant defense, acting against pathogens and triggering resistance responses. Intercropping with VOC-emitting MAPs can therefore enhance disease resistance. This study investigated VOCs emitted by sage (Salvia officinalis) as potential resistance inducers in grapevine (Vitis vinifera) against Plasmopara viticola, the causal agent of downy mildew, under consociated growth conditions. Sage and grapevine plants were co-grown in an airtight box system for 24 or 48 h, after which grape leaves were inoculated with P. viticola. Disease assessments were integrated with grapevine leaf metabolic profiling to evaluate responses to VOC exposure and pathogen infection. Untargeted and targeted metabolomic analysis revealed that sage VOCs consistently reprogrammed grapevine secondary metabolism, without substantial differences between 24 and 48 h exposures. Lipids, phenylpropanoids, and terpenoids were markedly accumulated following VOC exposure and persisted following inoculation. Correspondingly, leaves pre-exposed to sage VOCs exhibited a significant reduction in disease susceptibility. Overall, our results suggest that exposure to sage VOCs induces signaling and metabolic reprogramming in grapevine. Further research should elucidate how grapevines perceive and integrate these signals, as well as the broader processes underlying MAP VOC-induced defense, and evaluate their translation into sustainable viticultural practices. Full article

Downy mildew caused by Plasmopara viticola is an important disease in grape production, particularly in the highly susceptible, widely cultivated Vitis vinifera L. Breeding for disease resistance is an effective solution, and V. vinifera intraspecific crosses can yield progeny with both disease resistance and high quality. To assess the potential of intraspecific recurrent selection in V. vinifera (IRSV) in improving grapevine resistance to downy mildew and to analyze the pattern of disease resistance inheritance, the disease-resistant variety Ecolly was selected as one of the parents and crossed with Cabernet Sauvignon, Marselan, and Dunkelfelder, respectively, creating three reciprocal combinations, resulting in 1657 hybrid F1 progenies. The primary results are as follows: (1) significant differences in disease resistance among grape varieties and, significant differences in disease resistance between different vintages of the same variety were found; (2) the leaf downy mildew resistance levels of F1 progeny of different hybrid combinations conformed to a skewed normal distribution and showed some maternal dominance; (3) the degree of leaf bulbous elevation was negatively correlated with the level of leaf downy mildew resistance, and the correlation coefficient with the level of field resistance was higher; (4) five progenies with higher levels of both field and in vitro disease resistance were obtained. Intraspecific hybridization can improve the disease resistance of offspring through super-parent genetic effects, and Ecolly can be used as breeding material for recurrent hybridization to obtain highly resistant varieties. Full article

There are many products obtained from grapes. The early detection of diseases in an economically important fruit is important, and the spread of disease significantly increases financial losses. In recent years, it is known that artificial intelligence techniques have achieved very successful results in image classification. Therefore, the early detection and classification of grape diseases with the latest artificial intelligence techniques and feature reduction techniques was carried out within the scope of this study. The most well-known convolutional neural network (CNN) architectures, texture-based Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods, Neighborhood Component Analysis (NCA), feature reduction methods, and machine learning (ML) techniques are the methods used in this article. The proposed hybrid model was compared with two texture-based and four CNN models. The features from the most successful CNN model and texture-based architectures were combined. The NCA method was used to select the best features from the obtained feature map, and the model was classified using the best-known ML classifiers. Our proposed model achieved an accuracy value of 99.1%. This value shows that our model can be used in the detection of grape diseases. Full article

Kiwifruits are highly valued for their nutritional and health-related benefits as well as for their economic importance, since they significantly contribute to the economy of many countries that cultivate them. However, kiwifruits are very sensitive to diseases that may substantially impact their final quantity and quality. Computer vision (CV) has been extensively employed for disease recognition in the agricultural sector within the last decade; yet there are limited works dealing with kiwifruit disease recognition, and there is an obvious lack of open datasets to promote relevant research, especially when compared to research on other cultivations, e.g., grapes. To this end, this study introduces the first-reported open dataset for kiwifruit leaf disease recognition, including Alternaria, Nematodes and Phytophthora, while image datasets of Nematodes have not been previously reported. The proposed dataset, named AgriVision-Kiwi Dataset, has been used first for leaf detection with You Only Look Once version 11 (YOLOv11), reporting a bounding box loss of 0.053, and then to train various deep learning models for kiwifruit diseases recognition, reporting accuracies of 98.80% ± 0.5, e.g., 98.30% to 99.30%, after 10-fold cross-validation. The introduced dataset aims to encourage the development of CV applications towards the timely prevention of diseases’ spreading. Full article

Grapes, one of the most delicious and refreshing fruits in the world, are a source of sugar, minerals, and vitamins. Summer pruning affects ripening, disease control, yield, and quality parameters by controlling the vine microclimate. In our study, leaf removal, fruit thinning, and cluster thinning and their combination were applied to the Alphonse Lavallée grape variety, aiming to improve yield, cluster, and berry characteristics. As a result of the applications, cluster and berry characteristics, SSC, pH, titratable acidity (TA), total phenolic content, antioxidant activity, and color parameters were examined. In our study, all summer pruning applications and their combinations caused increases in cluster and berry parameters (weight, length, and width) compared to the control. In addition to these, the SSC, pH, and maturity index increased and TA decreased. All these applications also increased berry detachment and skin rupture force, which have an important place in road resistance in table grape varieties. Significant improvements were also seen in the quality parameters of total phenolic content and antioxidant activity. In addition, there were increases in the lightness and chroma values that determine the fruit quality in table grapes. Considering all these data, the summer pruning applications we made had significant effects on yield and quality. It is thought that cutting a part of the clusters instead of the whole cluster will especially prevent the yield loss experienced in cluster thinning applications. Full article

In view of the issues of high complexity, significant computational resource consumption, and slow inference speed in the detection algorithm for grape leaf diseases, this paper proposes GCS-YOLO, a lightweight detection algorithm based on an improved YOLOv8. The lightweight feature extraction module C2f-GR is proposed to replace the C2f module. C2f-GR achieves lightweight design while effectively capturing detailed features of multi-scale information by replacing partial convolutions in C2f with Ghost Modules. Additionally, RepConv is incorporated into C2f-GR to avoid the complexity of multi-branch structures and enhance gradient flow capability. The CBAM attention mechanism is added to the model to improve the extraction of subtle features of lesions in complex environments. Cross-scale shared convolution parameters and separated batch normalization techniques are used to optimize the detection head, achieving a lightweight design and improving the detection efficiency of the algorithm. Experimental results indicate that the improved model has a number of parameters and computational load of 1.63 M and 4.5 G, respectively, with a mean average precision (mAP@0.5) of 96.2% and a model size of only 3.5 MB. The number of parameters and computational load of the improved model have been reduced by 45.7% and 45.1%, respectively, compared to the baseline model, while the mAP has increased by 1.3%. This lightweight design not only ensures detection accuracy to meet the real-time detection needs of grape leaf diseases but is also more suitable for edge deployment, demonstrating broad application prospects. Full article

Agricultural productivity is increasingly threatened by plant diseases, which can spread rapidly and lead to significant crop losses if not identified early. Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. This paper aims to develop models capable of accurately identifying plant diseases across diverse conditions, overcoming the limitations of existing methods. A combined dataset was utilised, incorporating the PlantDoc dataset with web-sourced images of plants from online platforms. State-of-the-art convolutional neural network (CNN) architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, were employed and fine-tuned for plant leaf disease classification. A key contribution of this work is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. The results demonstrated varied performance across the datasets. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved an accuracy of 73.31%. In cross-dataset evaluation, where the model was trained on PlantDoc and tested on a web-sourced dataset, EfficientNet-B3 reached 76.77% accuracy. The best performance was achieved with the combination of the PlanDoc and web-sourced datasets resulting in an accuracy of 80.19% indicating very good generalisation in diverse conditions. Class-wise F1-scores consistently exceeded 90% for diseases such as apple rust leaf and grape leaf across all models, demonstrating the effectiveness of this approach for plant disease detection. Full article

This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns. This approach allows for a holistic analysis of spatially distributed symptoms, crucial for accurately diagnosing diseases in trees. Extensive experiments conducted on apple, grape, and tomato leaf disease datasets demonstrate the model’s superior performance, achieving over 99% accuracy and significantly improving F1 scores compared to traditional methods such as ResNet, VGG, and MobileNet. These findings underscore the effectiveness of the proposed model for precise and reliable plant disease classification. Full article

Deep learning-based methods have proven to be effective for various purposes in the agricultural sector. However, these methods require large amounts of labelled data, which are difficult to prepare and preprocess. To overcome this problem, we propose the use of a latent diffusion model for plant disease detection by generating unseen class images. In this study, we used images of healthy and diseased grape leaves as training datasets and utilized the latent diffusion model, known for its superior performance in image generation, to generate images of diseased apple leaves that were not included in this dataset. Image-to-image generation was utilized to preserve the original healthy leaf features, which enabled the appropriate image generation of diseased apple leaves. To ascertain whether the generated diseased apple leaf images could be used to detect leaf diseases, a deep learning-based classification model was trained to discriminate between diseased and healthy apple leaves from a dataset with a mixture of actual and generated images. Results showed that leaves were accurately classified, indicating that diseased apple leaves not included in the training data could be used to identify the actual diseased apple leaves. Our approach opens up new avenues for improving plant disease detection methods. Full article

Xylella fastidiosa is an aerobic, Gram-negative bacterium that is responsible for many plant diseases. The bacterium is the causal agent of Pierce’s disease in grapes and is also responsible for citrus variegated chlorosis, peach phony disease, olive quick decline syndrome and leaf scorches of various species. The production of biofilm is intrinsically linked with persistence and transmission in X. fastidiosa. Biofilm formation is regulated by members of the Diffusible Signal Factor (DSF) quorum sensing signalling family which are comprised of a series of long chain cis-unsaturated fatty acids. This article describes the evaluation of a library of N-acyl sulfonamide bioisosteric analogues of BDSF, XfDSF1 and XfDSF2 for their ability to control biofilm production in X. fastidiosa. The compounds were screened against both the wild-type strain Temecula and an rpfF* mutant which can perceive but not produce XfDSF. Planktonic cell abundance was measured via OD600 while standard crystal violet assays were used to determine biofilm biomass. Several compounds were found to be effective biofilm inhibitors depending on the nature of the sulfonamide substituent. The findings reported here may provide future opportunities for biocontrol of this important plant pathogen. Full article

Leaf spot, a major apple disease, manifests in diverse symptoms. In this study, the pathogen was isolated from diseased ‘Yanfu 3’ apple leaves in Yantai, Shandong Province, and identified as Neopestalotiopsis clavispora through morphological observation, molecular identification, and multi-gene (ITS, TEF1α, and TUB2) phylogenetic analysis. Three isolates (YTNK01, YTNK02, and YTNK03) were selected for pathogenicity tests to verify Koch’s postulates. To our knowledge, this is the first report of N. clavispora being responsible for apple leaf spots in China, and the disease has been named ‘apple Neopestalotiopsis leaf spot’. Additionally, N. clavispora was found to infect crabapple, sweet cherry, grape, peach, and pear under laboratory conditions, indicating that these fruit trees may be potential hosts for N. clavispora in the field. The in vitro toxicity of ten fungicides to the pathogen was assessed using the mycelial growth rate method. All ten fungicides were effective in inhibiting the growth of N. clavispora. Among them, those based on pylocyanonitrile, propiconazole, pyraclostrobin, tebuconazole, diphenoxazole, and osthole showed higher toxicity to N. clavispora, with EC₅₀ values of 0.11, 0.41, 0.47, 1.32, 1.85, and 3.82 µg/mL, respectively. These fungicides could be used as alternatives to prevent this disease in production. Overall, these findings provide valuable insights into the characteristics of N. clavispora causing apple leaf spot and are crucial for developing effective management strategies. Full article

Most previously studies had considered that plant fungal disease spread widely and quickly by airborne fungi spore. However, little is known about the release dynamics, aerodynamic diameter, and pathogenicity threshold of fungi spore in air of the greenhouse environment. Grape gray mold is caused by Botrytis cinerea; the disease spreads in greenhouses by spores in the air and the spore attaches to the leaf and infects plant through the orifice. In this study, 120 μmol/L propidium monoazide (PMA) were suitable for treatment and quantitation viable spore by quantitative real-time PCR, with a limit detection of 8 spores/mL in spore suspension. In total, 93 strains of B. cinerea with high pathogenicity were isolated and identified from the air samples of grapevines greenhouses by a portable sampler. The particle size of B. cinerea aerosol ranged predominately from 0.65–3.3 μm, accounting for 71.77% of the total amount. The B. cinerea spore aerosols were infective to healthy grape plants, with the lowest concentration that could cause disease being 42 spores/m³. Botrytis cinerea spores collected form six greenhouse in Shandong Province were quantified by PMA-qPCR, with a higher concentration (1182.89 spores/m³) in May and June and a lower concentration in July and August (6.30 spores/m³). This study suggested that spore dispersal in aerosol is an important route for the epidemiology of plant fungal disease, and these data will contribute to the development of new strategies for the effective alleviation and control of plant diseases. Full article

The pathogenicity of grapevine geminivirus A (GGVA), a recently identified DNA virus, to grapevine plants remains largely unclear. Here, we report a new GGVA isolate (named GGVA^QN) obtained from grapevine ‘Queen Nina’ plants with severe disease symptoms. The infectious clone of GGVA^QN (pXT-GGVA^QN) was constructed to investigate its pathogenicity. Nicotiana benthamiana plants inoculated with GGVA^QN by agroinfiltration displayed upward leaf curling and chlorotic mottling symptoms. A simple, quick, and efficient method for delivering DNA clones of GGVA^QN into grapevine plants was developed, by which Agrobacterium tumefaciens cells carrying pXT-GGVA^QN were introduced into the roots of in vitro-grown ‘Red Globe’ grape plantlets with a syringe. By this method, all ‘Red Globe’ grape plants were systemically infected with GGVA^QN, and the plants exhibited chlorotic mottling symptoms on their upper leaves and downward curling, interveinal yellowing, and leaf-margin necrosis symptoms on their lower leaves. Our results provide insights into the pathogenicity of GGVA and a simple and efficient inoculation method to deliver infectious viral clones to woody perennial plants. Full article

With the application of computer vision in the field of agricultural disease recognition, the convolutional neural network is widely used in grape leaf disease recognition and has achieved remarkable results. However, most of the grape leaf disease recognition models have the problem of weak generalization ability. In order to overcome this challenge, this paper proposes an image identification method for grape leaf diseases in different domains based on Fourier domain adaptation. Firstly, Fourier domain adaptation is performed on the labeled source domain data and the unlabeled target domain data. To decrease the gap in distribution between the source domain data and the target domain data, the low-frequency spectrum of the source domain data and the target domain data is swapped. Then, three convolutional neural networks (AlexNet, VGG13, and ResNet101) were used to train the images after style changes and the unlabeled target domain images were classified. The highest accuracy of the three networks can reach 94.6%, 96.7%, and 91.8%, respectively, higher than that of the model without Fourier transform image training. In order to reduce the impact of randomness, when selecting the transformed image, we propose using farthest point sampling to select the image with low feature correlation for the Fourier transform. The final identification result is also higher than the accuracy of the network model trained without transformation. Experimental results showed that Fourier domain adaptation can improve the generalization ability of the model and obtain a more accurate grape leaf disease recognition model. Full article

The precise detection of small lesions on grape leaves is beneficial for early detection of diseases. In response to the high missed detection rate of small target diseases on grape leaves, this paper adds a new prediction branch and combines an improved channel attention mechanism and an improved E-ELAN (Extended-Efficient Long-range Attention Network) to propose an improved algorithm for the YOLOv7 (You Only Look Once version 7) model. Firstly, to address the issue of low resolution for small targets, a new detection head is added to detect smaller targets. Secondly, in order to increase the feature extraction ability of E-ELAN components in YOLOv7 for small targets, the asymmetric convolution is introduced into E-ELAN to replace the original 3 × 3 convolution in E-ELAN network to achieve multi-scale feature extraction. Then, to address the issue of insufficient extraction of information from small targets in YOLOv7, a channel attention mechanism was introduced and improved to enhance the network’s sensitivity to small-scale targets. Finally, the CIoU (Complete Intersection over Union) in the original YOLOv7 network model was replaced with SIoU (Structured Intersection over Union) to optimize the loss function and enhance the network’s localization ability. In order to verify the effectiveness of the improved YOLOv7 algorithm, three common grape leaf diseases were selected as detection objects to create a dataset for experiments. The results show that the average accuracy of the algorithm proposed in this paper is 2.7% higher than the original YOLOv7 algorithm, reaching 93.5%. Full article