Apple Disease Research Articles

Development of a qPCR assay for early detection and quantification of Phytopythium vexans in kiwifruit plant and soil affected by Vine Decline Syndrome.

Kiwifruit Vine Decline Syndrome (KVDS) is a soilborne disease affecting Actinidia fruit trees in perennial cropping systems. Since its emergence in 2012, studies have increasingly identified the oomycete Phytopythium vexans as a major causative agent of the disease. P. vexans is also implicated in complex soilborne disease systems of woody perennial crops, including replant disease in apple and pear. To date, most molecular assays for the detection of P. vexans target the nuclear ribosomal internal transcribed spacer (ITS), a region which is ill-suited for distinguishing between closely related oomycete species. The cytochrome oxidase subunit I (COI) mitochondrial gene was targeted for the design of new primers because it was previously identified as a better marker for differentiating oomycete species. The FOR2/REV4RCA primer pair gave the best results regarding PCR specificity and was selected for use in a SYBR Green-based qPCR assay. The specificity of the qPCR assay was evaluated using 29 P. vexans strains (including different phylogenetic groups) as well as a wide variety of closely related off-target species associated with pathogenic soil communities of fruit trees. P. vexans strains were successfully quantified down to 20 fg in water and in DNA extracted from kiwifruit roots. P. vexans was also detected in artificially inoculated Actinidia plant roots as well as in a variety of naturally infected field samples of both kiwi and apple trees. These results suggest that the qPCR assay developed in this study is highly sensitive and specific to target pathogen, regardless of sample matrix.

The characterization and antifungal activities of two new Trichoderma antagonistic fungi against four apple disease pathogens

The characterization and antifungal activities of two new Trichoderma antagonistic fungi against four apple disease pathogens

Empowering white shark optimizer for dimensionality reduction with case study of apple disease prediction

Empowering white shark optimizer for dimensionality reduction with case study of apple disease prediction

AppleLeafNet: a lightweight and efficient deep learning framework for diagnosing apple leaf diseases.

Accurately identifying apple diseases is essential to control their spread and support the industry. Timely and precise detection is crucial for managing the spread of diseases, thereby improving the production and quality of apples. However, the development of algorithms for analyzing complex leaf images remains a significant challenge. Therefore, in this study, a lightweight deep learning model is designed from scratch to identify the apple leaf condition. The developed framework comprises two stages. First, the designed 37-layer model was employed to assess the condition of apple leaves (healthy or diseased). Second, transfer learning was used for further subclassification of the disease class (e.g., rust, complex, scab, and frogeye leaf spots). The trained lightweight model was reused because the model trained with correlated images facilitated transfer learning for further classification of the disease class. A dataset available online was used to validate the proposed two-stage framework, resulting in a classification rate of 98.25% for apple leaf condition identification and an accuracy of 98.60% for apple leaf disease diagnosis. Furthermore, the results confirm that the proposed model is lightweight and involves relatively fewer learnable parameters in comparison with other pre-trained deep learning models.

Control of Apple Scab in Commercial Orchards Through Primary Inoculum Management

Apple scab, caused by Venturia inaequalis, is one of the most important diseases in apples in all production regions and its sustainable control is still a challenge. The aim of this work was to optimize the control of apple scab through different environmentally friendly inoculum management strategies, specifically the removal of fallen leaves in winter and the treatment of ground leaves with the biological agent Trichoderma asperellum (T34 BIOCONTROL®) to inhibit or prevent inoculum development in commercial orchards. The results obtained from 4 years of trials in commercial orchards demonstrated that the combination of fungicide treatments and leaf litter management, particularly through aspiration, significantly reduced the development of apple scab in comparison with strategies commonly used by growers that are based solely on fungicide application. Both the incidence and severity of the disease in leaves and fruit decreased by over 90% when inoculum management and fungicide treatments were combined. These results highlight that reducing the source of inoculum by removing fallen leaves is an effective strategy that complements fungicide or biological control agent applications. In conclusion, combining eco-friendly strategies with standard fungicides and monitoring environmental conditions can help to reduce the frequency of phytosanitary applications, ultimately contributing to the goal of minimizing their use in the control of apple scab.

Enhancing Sustainable Automated Fruit Sorting: Hyperspectral Analysis and Machine Learning Algorithms

Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for fruit sorting by applying hyperspectral analysis and machine learning to improve product quality and reduce losses. The employed hyperspectral technologies and machine learning algorithms enable the rapid and accurate detection of defects on the surface of fruits, enhancing product quality and reducing the number of rejects, thereby contributing to the sustainability of agriculture. This study seeks to advance commercial fruit quality control by comparing hyperspectral image classification algorithms to detect apple lesions caused by pathogens, including sunburn, scab, and rot, on three apple varieties: Honeycrisp, Gala, and Jonagold. The lesions were confirmed independently using expert judgment, real-time PCR, and 3D fluorimetry, providing a high accuracy of ground truth data and allowing conclusions to be drawn on ways to improve the sustainability and safety of the agrocenosis in which the fruits are grown. Hyperspectral imaging combined with mathematical analysis revealed that Venturia inaequalis is the main pathogen responsible for scab, while Botrytis cinerea and Penicillium expansum are the main causes of rot. This comparative study is important because it provides a detailed analysis of the performance of both supervised and unsupervised classification methods for hyperspectral imagery, which is essential for the development of reliable automated grading systems. Support Vector Machines (SVM) proved to be the most accurate, with the highest average adjusted Rand Index (ARI) scores for sunscald (0.789), scab (0.818), and rot (0.854), making it the preferred approach for classifying apple lesions during grading. K-Means performed well for scab (0.786) and rot (0.84) classes, but showed limitations with lower metrics for other lesion types. A design and technological scheme of an optical system for identifying micro- and macro-damage to fruit tissues is proposed, and the dependence of the percentage of apple damage on the rotation frequency of the sorting line rollers is obtained. The optimal values for the rotation frequency of the rollers, at which the damage to apples is less than 5%, are up to 6 Hz. The results of this study confirm the high potential of hyperspectral data for the non-invasive recognition and classification of apple diseases in automated sorting systems with an accuracy comparable to that of human experts. These results provide valuable insights into the optimization of machine learning algorithms for agricultural applications, contributing to the development of more efficient and accurate fruit quality control systems, improved production sustainability, and the long-term storage of fruits.

Eugenol, Isoeugenol, Thymol, Carvacrol, and Ester Derivatives as an Ecofriendly Option to Control Glomerella Leaf Spot and Bitter Rot on Apple.

Glomerella leaf spot (GLS) and bitter rot (BR) are severe diseases of apple. Colletotrichum nymphaeae and Colletotrichum chrysophillum are the main species in Brazil. To control GLS and BR in Brazilian apple orchards, mancozeb and thiophanate-methyl fungicides are still used despite reported Colletotrichum resistance to these active ingredients. In addition, mancozeb has been banned from apple-importing countries and it has been a great challenge for apple producers to find products for its replacement that are eco-friendly. So, this study aimed to search for alternatives to control the diseases. We assessed the antifungal activity of eugenol, isoeugenol, thymol, carvacrol, and some of their ester derivatives. The best products to inhibit the pathogen in in vitro assays were thymol, thymol butyrate, and carvacrol, completely inhibiting mycelial growth at 125 mg L-1 and conidial germination at 100 mg L-1. In detached apple fruit, eugenol, eugenyl acetate, carvacryl acetate, and thymol butyrate, significantly reduced BR symptoms caused by Colletotrichum species with some variation between experiments and species, decreasing the risk of BR with the time compared to control. In detached leaves, all tested compounds significantly reduced the risk of development of GLS symptoms with disease control varying from 30 to 100%. The compounds tested are promising alternatives to replace fungicides to control bitter rot and Glomerella leaf spot on apple culture and should be tested for field conditions.

Signs, Causes, Fighting Apple Diseases

Numerous diseases often damage apples in home orchards each year. These include fly agaric and sooty mold, summer blight, black rot, powdery mildew, and cedar apple rust. This article discusses the signs, causes, progression, treatment, and preventive measures for several apple tree diseases. Some infections attack the tree roots in the fall, but symptoms do not appear until the following spring, when the tree can no longer absorb enough water and nutrients to continue growing. Disease control in apple orchards is difficult due to the lack of effective chemical treatments. Preventive measures can help control these diseases.

Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers

Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation for diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction and large dataset requirements. EDA–ViT overcomes these by using a multi-scale weighted feature aggregation and a feature interaction module, enhancing both local and global feature extraction. The adaptive data augmentation method refines the training process, boosting accuracy and robustness. With a dataset of 8226 images, EDA–ViT achieved a classification accuracy of 96.58%, an F1 score of 96.10%, and a Matthews Correlation Coefficient (MCC) of 92.24%, outperforming other models. The inclusion of the Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced feature capture. Ablation studies revealed that the adaptive augmentation contributed to a 0.56% accuracy improvement and a 0.34% increase in MCC. In summary, EDA–ViT presents an innovative solution for custard apple disease diagnosis, with potential applications in broader agricultural disease detection, ultimately aiding precision agriculture and crop health management.

First report of Erwinia pyrifoliae causing flower blight and fruit rot on greenhouse-grown Fragaria × ananassa in the United States.

Erwinia pyrifoliae causes disease of pear (Pyrus spp.), apple (Malus spp.), and strawberry (Fragaria × ananassa) (Wenneker and Bergsma-Vlami 2015), which are economically important commodities in the US. Disease symptoms on pear and apple are indistinguishable from those caused by the non-quarantine fire blight pathogen, E. amylovora (Kim et al. 1999), which also causes disease on strawberries (Atanasova et al. 2005). Samples of greenhouse-grown strawberry 'Albion' from Ohio were submitted to the Purdue Plant and Pest Diagnostic Lab in December 2023. Fruits were stunted with brown lesions, while sepals and pedicels had brown-black water-soaked lesions. Cut fruit exuded bacterial ooze from the main vascular bundle. Bacterial streaming was observed microscopically from symptomatic tissue which tested positive with the E. amylovora ImmunoStrip® (Agdia Inc., Elkhart, IN); reported by the manufacturer to cross-react with E. pyrifoliae. Isolation from symptomatic tissue produced pure cultures on sucrose peptone agar and Kings medium B after incubation at 27°C for 48 hr, and colonies appeared circular and white/opaque. Crude DNA extractions were prepared by boiling colony suspensions in Tris-EDTA buffer. Two independent real-time PCR tests specific for E. pyrifoliae (Lehman et al. 2008; Yasuhara-Bell et al. 2024) produced positive results. Conventional PCR using an E. pyrifoliae-specific primer set targeting a divergent region between pstS and glmS genes (Wensing et al. 2011) also produced positive results. The amplicon was Sanger-sequenced and deposited into NCBI GenBank (Accession PP757383). BLASTn analysis using the Nucleotide collection and Whole-genome shotgun contigs revealed top matches (100% query coverage; 97.5% identity to type strain DSM 12163) with E. pyrifoliae only; next closest match was E. amylovora (53% query coverage). To confirm Koch's postulates, immature fruit of six healthy strawberry 'Albion' plants were wounded with a sterile pipette tip and then submersed in a bacterial suspension in sterile deionized water (DI H2O) (3.1×107 cells/ml). Fruit of six additional plants were mock inoculated using sterile DI H2O. Plants were placed in plastic bags for 48 hr at room temperature with a 12-hr photoperiod. Symptoms were first observed on inoculated plants 1.5 days post-inoculation (DPI). Brown discoloration was observed within fruit and as spreading lesions on fruit pedicels by 4 DPI; mock-inoculated plants remained asymptomatic. Bacterial streaming from symptomatic tissue allowed successful re-isolation of the bacterium. Molecular testing confirmed isolates to be E. pyrifoliae, thus completing Koch's postulates. Following initial confirmation, additional samples of infected strawberry ('Albion' and 'GB96') from the same greenhouse were confirmed positive for E. pyrifoliae by molecular testing and sequencing. To our knowledge this is the first time Erwinia pyrifoliae was detected in the US. There are many known pathways of introduction from Asia and Europe; however, pstS-glmS sequence comparison with strawberry isolates from the Netherlands (sequences provided by M.J.C. Pel) suggests this US strawberry strain is unique, but most closely related to Japanese strains (98.5% identity). Potential origin of this strain is unknown, but comparative genomics studies to investigate relatedness among strains are planned.

Incremental RPN: Hierarchical Region Proposal Network for Apple Leaf Disease Detection in Natural Environments.

Apple leaf diseases can seriously affect apple production and quality, and accurately detecting them can improve the efficiency of disease monitoring. Owing to the complex natural growth environment, apple leaf lesions may be easily confused with background noise, leading to poor performance. In this study, a cascaded Incremental Region Proposal Network (Inc-RPN) is proposed to accurately detect apple leaf diseases in natural environments. The proposed Inc-RPN has a two-layer RPN architecture, where the precursor RPN is leveraged to generate diseased leaf proposals, and the successor RPN focuses on extracting target disease spots based on diseased leaf proposals. In the successor RPN, a low-level feature aggregation module is designed to fully utilize the bridged features and preserve the semantic information of the target disease spots. An incremental module is also leveraged to extract aggregated diseased leaf features and target disease spot features. Finally, a novel position anchor generator is designed to generate anchors based on diseased leaf proposals. The experimental results show that the proposed Inc-RPN performs very well on the FALD_CED and Apple Leaf Disease datasets, showing that it can accurately perform apple leaf disease detection tasks.

CNN Models Approaches for Robust Classification of Apple Diseases

In this research, the performance of several CNN models for diagnosing diseases in apple leaves is critically assessed. Due to this, early identification of diseases in agriculture is essential to boost productivity and avoid losses. In this context, disease identification was carried out with CNN models including ResNet50 and InceptionV4, Xception, DenseNet121, EfficientNetV2_m, and VGG13. While diagnosing the performance of each model the key parameters like Accuracy, Precision, Recall, and F1 score were computed. The accuracy values obtained show that all the discussed models are very accurate. However, it should be pointed out that the EfficientNetV2_m model is the best one in terms of accuracy and F1 score of 100%. This has shown that the EfficientNetV2_m gives a better detection of diseases in apple leaves compared to other models. The outcomes of the research reveal that in comparison to the conventional machine learning methodologies, the outcomes of the deep learning methods can prove to be much more beneficial for the agricultural sector by presenting a vision of how they can be incorporated and employed for improving the different sectors of management in the agricultural field. Summing up, the present research has shown the efficiency of the deep learning models in detecting diseases on apple leaves and has made a substantial contribution to the development of this line of study. In regards to the recommendations, the authors propose considering increasing the size of the sets and switching to the utilization of more advanced algorithms to enhance the present model. Thus, diseases found in agriculture will be identified easily and may also be more efficient in creating efficiency in the agriculture business.

The MdERF61-mdm-miR397b-MdLAC7b module regulates apple resistance to Fusarium solani via lignin biosynthesis.

Apple replant disease (ARD) is a worldwide problem that threatens the industry. However, the genetic mechanism underlying plant disease resistance against ARD remains unclear. In this study, a negative regulatory microRNA in Malus domestica, mdm-miR397b, and its direct target MdLAC7b (Laccase) was selected for examination based on our previous small RNA and degradome sequencing results. Overexpressing the mdm-miR397b-MdLAC7b module altered the lignin deposition and jasmonic acid contents in apple roots, which also led to increased resistance to Fusarium solani. Additionally, Y1H library screening using mdm-miR397b promoter recombinants identified a transcription factor, MdERF61, that represses mdm-miR397b transcriptional activity by directly binding to 2 GCC-boxes in the mdm-miR397b promoter. In summary, our results suggest that the MdERF61-mdm-miR397b-MdLAC7b module plays a crucial role in apple resistance to F. solani and offers insights for enhancing plant resistance to soil-borne diseases in apples.

Patulin inhibition of specific apple microbiome members uncovers Hanseniaspora uvarum as a potential biocontrol agent.

Penicillium expansum is a major postharvest pathogen of apples, causing loss in fruits through tissue damage, as well as in apple products due to contamination with the mycotoxin patulin. During infections, patulin is a cultivar-dependent virulence factor that facilitates apple lesion development. Patulin also has characterized antimicrobial activity and is important for inhibiting other competitive phytopathogens, but the role of this inhibitory activity has not been investigated in the context of the apple microbiome. In our current study, we isolated 68 apple microbiota and characterized their susceptibility to P. expansum extracts. We found Gram-negative bacteria and Basidiomycete yeast to demonstrate largely patulin-specific growth inhibition compared to Gram-positive and Ascomycete isolates. From co-cultures, we identified a Hanseniaspora and Gluconobacter pairing that reduced P. expansum biomass and found that Hanseniaspora uvarum alone is sufficient to reduce apple disease progression in vivo. We investigated possible mechanisms of H. uvarum biocontrol activity and found modest inhibition on apple puree plates, as well as a trend toward lower patulin levels at the wound site. Active biocontrol activity required live yeast, which also were effective in controlling Botrytis cinerea apple infections. Lastly, we explored the breadth of H. uvarum biocontrol activity with over 30 H. uvarum isolates and found consistent inhibition of P. expansum apple disease.

Classifying early apple scab infections in multispectral imagery using convolutional neural networks

Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab (Venturia inaequalis) disease in commercial orchards. Near-infrared (NIR) imagery can display apple scab symptoms earlier and at a greater severity than visible-spectrum (RGB) imagery. Early apple scab diagnosis based on NIR imagery may be automated using deep learning convolutional neural networks (CNNs). CNN models have previously been used to classify a range of apple diseases accurately but have primarily focused on identifying late-stage rather than early-stage detection. This study fine-tunes CNN models to classify apple scab symptoms as they progress from the early to late stages of infection using a novel multispectral (RGB-NIR) time series created especially for this purpose.This novel multispectral dataset was used in conjunction with a large Apple Disease Identification (ADID) dataset created from publicly available, pre-existing disease datasets. This ADID dataset contained 29,000 images of infection symptoms across six disease classes. Two CNN models, the lightweight MobileNetV2 and heavyweight EfficientNetV2L, were fine-tuned and used to classify each disease class in a testing dataset, with performance assessed through metrics derived from confusion matrices. The models achieved scab-prediction accuracies of 97.13 % and 97.57 % for MobileNetV2 and EfficientNetV2L, respectively, on the secondary data but only achieved accuracies of 74.12 % and 78.91 % when applied to the multispectral dataset in isolation. These lower performance scores were attributed to a higher proportion of false-positive scab predictions in the multispectral dataset. Time series analyses revealed that both models could classify apple scab infections earlier than the manual classification techniques, leading to more false-positive assessments, and could accurately distinguish between healthy and infected samples up to 7 days post-inoculation in NIR imagery.

Fruit and vegetable leaf disease recognition based on a novel custom convolutional neural network and shallow classifier.

Fruits and vegetables are among the most nutrient-dense cash crops worldwide. Diagnosing diseases in fruits and vegetables is a key challenge in maintaining agricultural products. Due to the similarity in disease colour, texture, and shape, it is difficult to recognize manually. Also, this process is time-consuming and requires an expert person. We proposed a novel deep learning and optimization framework for apple and cucumber leaf disease classification to consider the above challenges. In the proposed framework, a hybrid contrast enhancement technique is proposed based on the Bi-LSTM and Haze reduction to highlight the diseased part in the image. After that, two custom models named Bottleneck Residual with Self-Attention (BRwSA) and Inverted Bottleneck Residual with Self-Attention (IBRwSA) are proposed and trained on the selected datasets. After the training, testing images are employed, and deep features are extracted from the self-attention layer. Deep extracted features are fused using a concatenation approach that is further optimized in the next step using an improved human learning optimization algorithm. The purpose of this algorithm was to improve the classification accuracy and reduce the testing time. The selected features are finally classified using a shallow wide neural network (SWNN) classifier. In addition to that, both trained models are interpreted using an explainable AI technique such as LIME. Based on this approach, it is easy to interpret the inside strength of both models for apple and cucumber leaf disease classification and identification. A detailed experimental process was conducted on both datasets, Apple and Cucumber. On both datasets, the proposed framework obtained an accuracy of 94.8% and 94.9%, respectively. A comparison was also conducted using a few state-of-the-art techniques, and the proposed framework showed improved performance.

Colletotrichum Species Associated with Apple Bitter Rot and Glomerella Leaf Spot: A Comprehensive Overview.

Species of the genus Colletotrichum are among the most important plant pathogens globally, as they are capable of infecting many hosts-apple (Malus spp.) and other fruit and woody plant species-but also vegetable crops, cereals, legumes, and other annual and perennial herbaceous plants. The apple (Malus spp.) is attacked by various species from the genus Colletotrichum, whereby 27 different species from this genus have been described as the causative agents of apple bitter rot (ABR) and 15 as the cause of Glomerella leaf spot (GLS). These species generally belong to one of three species complexes: Colletotrichum acutatum, Colletotrichum gloeosporioides, and Colletotrichum boninense. The largest number of apple pathogens of the genus Colletotrichum belong to the species complex C. acutatum and C. gloeosporioides. However, further data on these species and the interactions between the species complexes of the genus Colletotrichum that cause these two apple diseases is needed for the development of effective control measures, thus ensuring successful and profitable apple cultivation. To contribute to this endeavor, a comprehensive review of the causative agents of ABR and GLS from the genus Colletotrichum is provided. In addition to presenting the species' current names, distribution, economic significance, and the symptoms they cause in apple, their development cycle, epidemiology, and molecular detection strategies are described, with a particular emphasis on control measures.

Deciduous fruit trees and grapevines as alternative crops in Demathophora necatrix (syn. Rosellinia necatrix) infested soils

White root rot, caused by Dematophora necatrix (syn. Rosellinia necatrix), affects deciduous trees. A D. necatrix infection-distribution survey found widespread disease in apple and cherry orchards in northern Israel bordering a Mediterranean forest, although the forest trees were unaffected. Because cherry and apple orchards must be abandoned due to long fungal survival in infested soils, alternative deciduous fruit trees and grapevines were assessed for growth in these D. necatrix-infested orchards. In the field, grapevine rootstocks and the almond–peach rootstock GF-677 were most tolerant to D. necatrix-infested soil. Apple was the most sensitive crop, with the rootstock Hashabi being more tolerant than PI80 or MM104. The Mediterranean forest tree Pistacia atlantica, which can serve as a rootstock for pistachio, was as sensitive as kiwifruit and apple, and persimmon rootstock sensitivity was not different from grapevine. Those results show that beside the above mentioned crops, vineyards can also replace apple orchards in D. necatrix-infested soils and so broadens the list of possible crops for the local farmers in a Mediterranean climate at altitudes above 440 m above sea level. This has also been observed in a commercial 10-year-old vineyard of ‘Shiraz’ grapevines grafted on SO4 rootstock. The almond-peach rootstock GF-677 can also be grown in infested soil, but in commercial orchards requires additional treatment for adequate disease control.

Colletotrichum species causing Glomerella leaf spot and apple bitter rot in the southeastern United States exhibit disparities in relative frequency, morphological phenotype, and QoI sensitivity.

Glomerella leaf spot (GLS), Glomerella fruit rot (GFR) and apple bitter rot (ABR), caused by Colletotrichum spp. are amongst the most devastating apple diseases in the southeastern United States. While several species have been identified as causal pathogens of GLS, GFR, and ABR, their relative frequency and fungicide sensitivity status in the southeastern U.S. is unknown. In total, 381 Colletotrichum isolates were obtained from symptomatic leaves and fruit from 18 conventionally managed apple orchards and two baseline populations in western North Carolina and Georgia in 2016 and 2017. Multilocus DNA sequence analysis revealed that C. chrysophilum was the predominant cause of GLS and GFR, and C. fioriniae was the causal agent of ABR. Baseline and commercial populations of Colletotrichum spp. were evaluated for sensitivity to pyraclostrobin and trifloxystrobin and no statistical differences in sensitivity between the two species were observed for conidial germination. However, EC50 values were significantly lower for C. fioriniae compared to C. chrysophilum for both fungicides regarding mycelial inhibition. Isolates recovered from commercial orchards revealed that 5 populations of C. chrysophilum and 1 population of C. fioriniae had reduced sensitivity to trifloxystrobin, and 1 C. fioriniae population had reduced sensitivity to pyraclostrobin via conidial germination assays. The cytb gene for 27 isolates of C. fioriniae, C. chrysophilum, and C. fructicola with different QoI sensitivities revealed the G143A mutation in a single isolate of C. chrysophilum with insensitivity to both fungicides. Results of these studies suggest that two Colletotrichum spp. predominantly cause GLS and ABR in the southeastern U.S. and that a reduction in sensitivity to some QoI fungicides may be responsible for control failures. This study also provides basis for monitoring shifts in QoI sensitivity in Colletotrichum spp. causing disease on apple in the southeastern U.S.

Online detection of moldy apple core based on diameter and SSC features

Moldy core is a common internal disease in apples, with current visible/near-infrared (Vis/NIR) spectroscopy-based detection methods often suffering from inaccuracies due to variations in fruit diameter and soluble solids content (SSC). To address this issue, an online detection system was developed that integrates spectral corrections for both diameter and SSC information. The spectra were first corrected using a hyperbolic sine function based on fruit diameter, followed by a second correction utilizing the spectral features of SSC through the Variational Mode Decomposition (VMD) algorithm. This two-step correction process effectively mitigated the influence of diameter and SSC variations on the spectral data. A Partial Least Squares Discriminant Analysis (PLS-DA) model was subsequently constructed using the corrected spectra and moldy core labels. Compared to the uncorrected model, the improved model exhibited substantial enhancements in accuracy, recall, and precision, achieving 94.44%, 92.59%, and 96.15%, respectively. Additionally, online validation with independent samples demonstrated an accuracy of 88.33%, highlighting the system's stability and robustness for efficient online detection of apple moldy core.