Apple Disease Research Articles

Polyhydroxyalkanoates (PHAs) from Endophytic Bacterial Strains as Potential Biocontrol Agents against Postharvest Diseases of Apples.

Due to the increasing use and accumulation of petrochemical plastics in the environment and the rapid depletion of natural resources, microbial polyhydroxyalkanoates have great potential to replace them. This study provides new insights in the field of obtaining of polyhydroxyalkanoates (PHAs) from endophytic bacterial strains and applying them as potential biocontrol agents against postharvest diseases of apples. Two strains-Pseudomonas flavescens D5 and Bacillus aerophilus A2-accumulated PHAs in amounts ranging from 2.77 to 5.9 g L-1. The potential to use low-cost substrates such as beet molasses and soapstock for PHA accumulation was shown. The PHAs produced by the Ps. flavescens D5 strain had pronounced antagonistic activity against Penicillium expansum (antifungal property = 62.98-73.08%). The use of PHAs as biocontrol agents significantly reduced the severity of apple blue mold, especially in the preventive treatment option.

Rapid computer vision detection of apple diseases based on AMCFNet

Rapid computer vision detection of apple diseases based on AMCFNet

APPLE PESTS AND DISEASES

APPLE PESTS AND DISEASES

基于语义分割网络的苹果叶片病害分割与分级研究

The key to diagnosing the types and degree of apple leaf diseases is to correctly segment apple leaf disease spots. Therefore, in order to effectively solve the problem of poor segmentation of leaves and diseased areas, the U2Net semantic segmentation network model was used in the research of apple leaf disease identification and disease diagnosis, and compared with the classic semantic segmentation network model DeepLabV3+ and UNet. In addition, the effects of different learning rates (0.01, 0.001, 0.0001) and optimizers (Adam, SGD) on the performance of U2Net network model were compared and analyzed. The experimental results showed that the learning rate is 0.001 and the optimizer is Adam, the average pixel accuracy (MPA) and mean intersection over union (MIoU) of the research model for lesion segmentation reach 98.87% and 84.43%, respectively. The results of this study were expected to provide the theoretical basis for the precise control of apple leaf disease.

Apple Leaf Disease Identification in Complex Background Based on BAM-Net

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network’s attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network’s ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops.

Transcriptomic analyses reveal robust changes in the defense response of apples induced by Hannaella sinensis

Research on yeast antagonists has recently demonstrated their safety and efficacy in effectively preventing apple postharvest infections. Our previous study has revealed that the antagonistic yeast Hannaella sinensis has shown control activity against blue mold decay in apples caused by Penicillium expansum. However, the molecular networks underlying H. sinensis-treated apple's disease control mechanisms are not explored. Hence, we aimed to uncover the molecular changes of apples treated with H. sinensis based on transcriptomic analysis. Differentially expressed genes of apples treated with H. sinensis were identified through RNA-seq (152 up-regulated and 1480 down-regulated DEGs) and bioinformatic analysis. Among the DEGs, significant numbers were annotated into apple defense-related pathways, such as plant-pathogen interaction, plant hormone signal transduction, phenylpropanoid biosynthesis, tyrosine metabolism, glycerolipid metabolism, and alpha-linoleic acid pathways related to resistance regulation. Our results indicated that H. sinensis could induce disease resistance in apples, which leads to increased protection against postharvest pathogens. The current study paves the way for further understanding the biocontrol mechanism of antagonistic yeast against postharvest infections by enhancing host defense.

An Intelligent and Precise Agriculture Model in Sustainable Cities Based on Visualized Symptoms

Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or damage to these crops. This paper presents an image processing and a deep learning-based automatic approach that classifies the diseases that strike the apple leaves. The proposed system has been tested using over 18,000 images from the Apple Diseases Dataset by PlantVillage, including images of healthy and affected apple leaves. We applied the VGG-16 architecture to a pre-trained unlabeled dataset of plant leave images. Then, we used some other deep learning pre-trained architectures, including Inception-V3, ResNet-50, and VGG-19, to solve the visualization-related problems in computer vision, including object classification. These networks can train the images dataset and compare the achieved results, including accuracy and error rate between those architectures. The preliminary results demonstrate the effectiveness of the proposed Inception V3 and VGG-16 approaches. The obtained results demonstrate that Inception V3 achieves an accuracy of 92.42% with an error rate of 0.3037%, while the VGG-16 network achieves an accuracy of 91.53% with an error rate of 0.4785%. The experiments show that these two deep learning networks can achieve satisfying results under various conditions, including lighting, background scene, camera resolution, size, viewpoint, and scene direction.

Cell Wall Carbohydrate Dynamics during the Differentiation of Infection Structures by the Apple Scab Fungus, Venturia inaequalis.

Scab, caused by the biotrophic fungal pathogen Venturia inaequalis, is the most economically important disease of apples. During infection, V. inaequalis colonizes the subcuticular host environment, where it develops specialized infection structures called runner hyphae and stromata. These structures are thought to be involved in nutrient acquisition and effector (virulence factor) delivery, but also give rise to conidia that further the infection cycle. Despite their importance, very little is known about how these structures are differentiated. Likewise, nothing is known about how these structures are protected from host defenses or recognition by the host immune system. To better understand these processes, we first performed a glycosidic linkage analysis of sporulating tubular hyphae from V. inaequalis developed in culture. This analysis revealed that the V. inaequalis cell wall is mostly composed of glucans (44%) and mannans (37%), whereas chitin represents a much smaller proportion (4%). Next, we used transcriptomics and confocal laser scanning microscopy to provide insights into the cell wall carbohydrate composition of runner hyphae and stromata. These analyses revealed that, during subcuticular host colonization, genes of V. inaequalis putatively associated with the biosynthesis of immunogenic carbohydrates, such as chitin and β-1,6-glucan, are downregulated relative to growth in culture, while on the surface of runner hyphae and stromata, chitin is deacetylated to the less-immunogenic carbohydrate chitosan. These changes are anticipated to enable the subcuticular differentiation of runner hyphae and stromata by V. inaequalis, as well as to protect these structures from host defenses and recognition by the host immune system. IMPORTANCE Plant-pathogenic fungi are a major threat to food security. Among these are subcuticular pathogens, which often cause latent asymptomatic infections, making them difficult to control. A key feature of these pathogens is their ability to differentiate specialized subcuticular infection structures that, to date, remain largely understudied. This is typified by Venturia inaequalis, which causes scab, the most economically important disease of apples. In this study, we show that, during subcuticular host colonization, V. inaequalis downregulates genes associated with the biosynthesis of two immunogenic cell wall carbohydrates, chitin and β-1,6-glucan, and coats its subcuticular infection structures with a less-immunogenic carbohydrate, chitosan. These changes are anticipated to enable host colonization by V. inaequalis and provide a foundation for understanding subcuticular host colonization by other plant-pathogenic fungi. Such an understanding is important, as it may inform the development of novel control strategies against subcuticular plant-pathogenic fungi.

Multiple factors affecting occurrence of soft scald and fungal decay in apple during storage

Some apple cultivars are highly susceptible to soft scald, a physiological disorder that can lead to large losses. The effect of harvest time, gradual cooling regimes and storage conditions on soft scald and fungal decay was investigated in two common apple cultivars, ‘Aroma’ and ‘Frida’ in a three year trial 2018–2020. Further, possible relationships between weather conditions during the growing season and 28 d before harvest and soft scald incidence along with fungal decay after storage were studied. The year with the highest rainfall had the highest incidence of soft scald and fungal decay. Our results suggest that the relative humidity during a period of 28 d before harvest was important for later development of soft scald in ‘Frida’, and together the results from ‘Frida’ and ‘Aroma’ showed a moderate correlation between relative humidity and soft scald. Gradual cooling showed conflicting results, and no treatment consistently lowered soft scald incidence. Gradual cooling led to inconclusive results, and storage in ambient air led to higher incidence of soft scald as compared to some investigated ULO storage conditions. Advanced maturity was associated with soft scald development and more fungal decay in one out of three years in ‘Aroma’, but did not affect incidence in ‘Frida’. The etiology of soft scald seems to be dependent of multiple factors.

Would You Like Wood or Plastic? Bin Material, Sanitation Treatments, and Bin Inoculum Levels Impact Blue Mold Decay of Stored Apple Fruit.

Blue mold, caused primarily by Penicillium expansum, is a significant postharvest disease of apples. It not only causes economic losses but also produces mycotoxins that contaminate processed fruit products, which contributes to food waste and loss. Previous research has shown that packing and storage bins harbor Penicillium spores and that steam and hot water efficiently reduce spore inoculum levels. However, studies using wooden and plastic bins regarding their ability to harbor spores, the effect of chemical sanitation treatments on spore levels, and the impact of rinsate from treated bins on apple fruit decay have not been investigated for the Mid-Atlantic area (Okull et al. 2006; Rosenberger 2009). We evaluated different sanitation treatments (chemical and physical) to reduce P. expansum inoculum levels on wooden and plastic bins. We determined that wooden bins bound P. expansum spores four orders of magnitude higher than plastic. When both bin types were treated with steam (wooden) or sterile hot water (plastic), Thyme Guard, or Academy, all treatments resulted in significantly (P < 0.05) lower spore levels compared to untreated controls. Although, plastic bins retained lower numbers of spores after inoculation with contaminated spore rinsate and required much higher concentrations of P. expansum spores in rinsate to retain spores at levels that would lead to decay on apple fruit. Overall, we demonstrated that plastic bins retain fewer spores than wooden bins and that both can be sanitized by various physical or chemical treatments. We envision that our findings will be applicable in the future as the techniques implemented in this study were used to investigate industry-relevant questions. Our goal is that the research techniques and findings become feasible with advancements in technology and/or accompany other shifts in existing processes in commercial pome fruit packing and storage facilities.

Identification and Fungicide Sensitivity of Colletotrichum spp. from Apple Flowers and Fruitlets in Brazil.

Glomerella leaf spot (GLS) and bitter rot (BR), caused by Colletotrichum spp., are major diseases on apple in southern Brazil. Among integrated pest management tools for disease management in commercial orchards, fungicides remain an important component. This study aimed to identify Colletotrichum spp. from cultivar Eva in Paraná state orchards; evaluate their in vitro sensitivity to cyprodinil, tebuconazole, iprodione, and fluazinam; and determine the baseline in vitro sensitivity of these isolates to benzovindiflupyr and natamycin. Most isolates belonged to Colletotrichum melonis and C. nymphaeae of the C. acutatum species complex. The two species varied in sensitivity to fluazinam and tebuconazole, but no variability was found for any other fungicide. The lowest 50% effective concentration (EC50) values of Colletotrichum spp. were observed for cyprodinil (mean EC50 < 0.02) and benzovindiflupyr (mean EC50 < 0.05); EC50 values were intermediate for fluazinam (mean EC50 < 0.33) and tebuconazole (mean EC50 < 0.14), and they were highest for natamycin (mean EC50 < 5.56) and iprodione (mean EC50 > 12). Cyprodinil and fluazinam are registered for use in Brazil for apple disease management but not specifically for GLS and BR. Tebuconazole is one of the few products registered for Colletotrichum spp. control in apples. In conclusion, flowers and fruitlets can serve as sources of inoculum for GLS and BR disease; C. acutatum was the predominant species complex in these tissues; cyprodinil and fluazinam applications may suppress GLS and BR; and benzovindiflupyr and natamycin warrant further investigation for GLS and BR disease control of apple due to comparably high in vitro sensitivity.

Multi-Scale Region-Of-Interest based Deep Learning for fruit disease identification and classification

Agricultural productivity is mainly affected by the different types of fruit diseases. To reduce such impacts, automated fruit disease identification and categorization model is vital. So, a Multi-scale Deep Learning (MDL) model was designed to identify and categorize the variety of strawberry fruit diseases. This model has an Adaptive Receptive Field Module (ARFM), a Bottleneck Block (BB), Depth-wise Residual Blocks (DRBs) and the Squeeze Excitation Residual Network (SE-ResNet) units to extract the Feature Maps (FMs) related to all disease classes. But, this model cannot partition individual fruit samples. Also, the number of fruits with multiple infections within a particular image was not easily captured. So, this article develops a Multi-scale Region-Of-Interest (ROI)-based DL model called (MRDL) to improve the accuracy of identification and categorization of a single fruit sample with a certain disease in a real-time scenario. This model is performed in 2 different phases. First, more robust features are extracted by the DRB, ARFM, and BB in the initial phase. Then, an ROI Fused (ROIF) unit, the SE-ResNet unit, a classification unit, and a mask unit are involved in the second phase. The ROIF unit obtains the FMs for all candidate ROIs using the candidate bounding boxes from the initial phase. The extracted FMs per sampled position are aggregated to get the fused FM, which is resized by integrating the attention strategy with the SE-ResNet unit. Those resized fused FMs are independently passed to the classification and mask units to classify the candidate bounding boxes and localize the infected pixels in the candidate ROIs, respectively. Finally, the test results exhibit that the MRDL model achieves 91.1%, 90.8%, and 91.6% accuracy on apple, citrus, and tomato disease databases, respectively compared to the classical models.

From Endophyte Community Analysis to Field Application: Control of Apple Canker (Neonectria ditissima) with Epicoccum nigrum B14-1

Apple canker, caused by Neonectria ditissima (Tul. and C. Tul.) Samuels and Rossman, is a major disease of apples (Malus domestica) worldwide. N. ditissima infects through natural and artificial wounds. Infected wood develops canker lesions which girdle branches and main stems causing reduced yield and tree death. N. ditissima is difficult to control; removal of inoculum (cankers) is expensive and therefore seldom practiced, whilst effective chemical products are being banned and no biocontrol products have been found to be effective against N. ditissima. This study used cues from a previous apple endophyte community analysis to isolate and test fungal endophytes belonging to the genus Epicoccum as potential endophytic biocontrol agents. Epicoccum nigrum B14-1, isolated from healthy apple trees, antagonised N. ditissima in vitro and reduced the incidence of N. ditissima infections of leaf scars by 46.6% and pruning wounds by 5.3% in field conditions at leaf fall. Autumn application of B14-1 conidia increased E. nigrum abundance in apple tissues at 10–20 days post-inoculation by ca. 1.5×, but this returned to control levels after one year. E. nigrum B14-1 did not cause detrimental effects on apple foliage, buds, fruit, or growth and could therefore present a new biocontrol agent to manage N. ditissima in commercial apple production.

Tissue and Time Optimization for Real-Time Detection of Apple Mosaic Virus and Apple Necrotic Mosaic Virus Associated with Mosaic Disease of Apple (Malus domestica).

Besides apple mosaic virus (ApMV), apple necrotic mosaic virus (ApNMV) has also been found to be associated with apple mosaic disease. Both viruses are unevenly distributed throughout the plant and their titer decreases variably with high temperatures, hence requiring proper tissue and time for early and real-time detection within plants. The present study was carried out to understand the distribution and titer of ApMV and ApNMV in apple trees from different plant parts (spatial) during different seasons (temporal) for the optimization of tissue and time for their timely detection. The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Reverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) was carried out to detect and quantify both viruses in the various plant parts of apple trees during different seasons. Depending on the availability of tissue, both ApMV and ApNMV were detected in all the plant parts during the spring season using RT-PCR. During the summer, both viruses were detected only in seeds and fruits, whereas they were detected in leaves and pedicel during the autumn season. The RT-qPCR results showed that during the spring, the ApMV and ApNMV expression was higher in leaves, whereas in the summer and autumn, the titer was mostly detected in seeds and leaves, respectively. The leaves in the spring and autumn seasons and the seeds in the summer season can be used as detection tissues through RT-PCR for early and rapid detection of ApMV and ApNMV. This study was validated on 7 cultivars of apples infected with both viruses. This will help to accurately sample and index the planting material well ahead of time, which will aid in the production of virus-free, quality planting material.

Detecting diseases in apple tree leaves using FPN–ISResNet–Faster RCNN

ABSTRACT Apple leaf diseases typified by small disease spots are generally difficult to detect in images. This study proposes a deep learning model called the feature pyramid networks (FPNs) –inception squeeze-and-excitation ResNet (ISResNet)–Faster RCNN (region with convolutional neural network) model to improve the accuracy of detecting apple leaf diseases. Apple leaf diseases were identified, evaluated, and validated by using the FPN–ISResNet–Faster RCNN. The results were compared with those obtained by the single-shot multibox detector (SSD), Faster RCNN, and ISResNet–Faster RCNN. The detection accuracies obtained by using different feature extraction networks, positions and numbers of SE, inception modules, scales of FPN structures, and scales of anchor frames were also compared. The results showed that the values of average precision (AP), and APs with the thresholds of the intersection over union of 0.5 and 0.75 (AP50 and AP75), obtained from the FPN–ISResNet–Faster RCNN were 62.71%, 93.68%, and 70.94%, respectively, which are higher than those of the SSD, VGG–Faster RCNN, GoogleNet–Faster RCNN, ResNet50–Faster RCNN, ResNeXt–Faster RCNN, and ISResNet–Faster RCNN. FPN–ISResNet–Faster RCNN was shown to be able to detect diseases in apple leaves with high accuracy and generalizability.

LAD-Net: A Novel Light Weight Model for Early Apple Leaf Pests and Diseases Classification.

Aphids, brown spots, mosaics, rusts, powdery mildew and Alternaria blotches are common types of early apple leaf pests and diseases that severely affect the yield and quality of apples. Recently, deep learning has been regarded as the best classification model for apple leaf pests and diseases. However, these models with large parameters have difficulty providing an accurate and fast diagnosis of apple leaf pests and diseases on mobile terminals. This paper proposes a novel and real-time early apple leaf disease recognition model. AD Convolution is firstly utilized to replace standard convolution to make smaller number of parameters and calculations. Meanwhile, a LAD-Inception is built to enhance the ability of extracting multiscale features of different sizes of disease spots. Finally, the LAD-Net model is built by the LR-CBAM and the LAD-Inception modules, replacing a full connection with global average pooling to further reduce parameters. The results show that the LAD-Net, with a size of only 1.25MB, can achieve a recognition performance of 98.58%. Additionally, it is only delayed by 15.2ms on HUAWEI P40 and by 100.1ms on Jetson Nano, illustrating that the LAD-Net can accurately recognize early apple leaf pests and diseases on mobile devices in real-time, providing portable technical support.

Diagnosis of some apple fruit diseases by using image processing and artificial neural network

Farmers typically lack the knowledge of diagnosis and control of different apple diseases. However, some apple diseases have visual symptoms and can be diagnosed by eyes, but their diagnosis by eyes is time-consuming and costly for farmers. A solution is to design an automatic disease diagnosis system using image processing techniques. This paper presents a low-cost method of apple disease diagnosis using a neural network and fruit classification into four classes of scab, bitter rot, black rot, and healthy fruits. This method uses color and texture features. The research used a multi-layer perceptron neural network whose input was the features extracted from the images and its output was the defined classes. After the network was trained by 60% of the images and the remaining images were reserved for its testing, the accuracy of the proposed method was assessed with different structures of single-layer and two-layer neural network structures. Based on the results, the application of a two-layer structure with eight neurons in the first layer and eight neurons in the second layer resulted in an optimal accuracy of 73.7%. • Developing a novel automatically method for diagnosing apple fruit diseases. • Using image processing and artificial neural network for diagnosing apple fruit diseases. • Providing a new dataset of apple images in field of diagnosing fruit diseases.

Detecting the asymptomatic colonization of apple branches by Neonectria ditissima, causing European canker of apple

European canker is one of the most devastating fungal diseases of apple in most temperate regions. The causal agent, Neonectria ditissima, infects trees through wounds in the bark forming cankers that girdle the stem and eventually cause tree death. Timely protection of the trees is challenged by stagnation of symptom expression after infections for a long period of time. The objective of this research is to use a novel TaqMan PCR assay to detect and quantify N. ditissima during the asymptomatic colonization of apple wood. Pruning wounds on branches of the cultivars Elstar and Gala were inoculated with N. ditissima and wood discs were sampled at 2–6, 10–14, and 30–34 mm distance from the inoculation site after 3 hours, 2 weeks, 4 weeks and 8 weeks for the detection and quantification of the pathogen. The TaqMan PCR assay detected N. ditissima in 51% of the inoculated apple tree samples. This was more sensitive than the culturing method detecting N. ditissima in 11% of the samples. An accumulation of N. ditissima DNA up to 34 mm distance from the inoculation site was observed without development of visible symptoms. To our knowledge this is the first time colonization of N. ditissima was detected and quantified in the absence of symptoms of European canker. The implications of this research are discussed.

Novel Detection and Quantification Approach of Erwinia amylovora In Vitro and In Planta Using SYBR Green-Based Real-Time PCR Assay.

Fire blight, caused by the bacterial pathogen Erwinia amylovora, is a highly destructive disease of apple and pear. Because the apple tree gets systemically infected with E. amylovora and eventually dies, E. amylovora is a considerably important pathogen in the orchard that requires long-term management. In addition, it is crucial to prevent the spread of the pathogen by expeditious diagnosis. In this study, via comparative approaches to the genome sequences of the strains of various Erwinia spp., we designed specific primers targeting a hypothetical gene that is single copy and located in the chromosomal DNA of E. amylovora. This primer set specifically amplified the DNA of E. amylovora but no other bacteria, including E. pyrifoliae, Pectobacterium spp., Pantoea spp., and Dickeya chrysanthemi. Furthermore, the SYBR Green-based real-time PCR using the primer set allowed accurate estimation of the population of E. amylovora. Developing a rapid and accurate diagnostic method using the novel primer set enables effective defense against pathogen spread through continuous monitoring and quick response.

Monitoring Spore Dispersal and Early Infections of Diplocarpon coronariae Causing Apple Blotch Using Spore Traps and a New qPCR Method.

Apple blotch (AB) is a major disease of apple in Asia and recently emerged in Europe and the United States. It is caused by the fungus Diplocarpon coronariae (formerly Marssonina coronaria; teleomorph: Diplocarpon mali) and leads to severe defoliation of apple trees in late summer, resulting in reduced yield and fruit quality. To develop effective disease management strategies, a sound knowledge of the pathogen's biology is crucial. Data on the early phase of disease development are scarce: No data on spore dispersal in Europe are available. We developed a highly sensitive TaqMan qPCR method to quantify D. coronariae conidia in spore trap samples. We monitored temporal and spatial dispersal of conidia of D. coronariae and the progress of AB in spring and early summer in an extensively managed apple orchard in Switzerland in 2019 and 2020. Our results show that D. coronariae overwinters in leaf litter, and spore dispersal and primary infections occur in late April and early May. We provide the first results describing early-season dispersal ofconidia of D. coronariae, which, combined with the observed disease progress, helps to understand the disease dynamics and will be a basis for improved disease forecast models. Using the new qPCR method, we detected D. coronariae in buds, on bark, and on fruit mummies, suggesting that several apple tissues might serve as overwintering habitats for the fungus, in addition to fallen leaves. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.