Apple Pests Research Articles

Future semiochemical control of codling moth, Cydia pomonella

Codling moth (CM), Cydia pomonella, is a significant pest of apple (Malus domestica) and other orchard crops worldwide, posing challenges due to the decrease in registered insecticides, rising resistance, and a changing climate. The pest exhibits a strong resistance capacity to both synthetic and natural insecticides, while shifting seasonal temperatures disrupt the reliability of phenology-temperature models for predicting targeted control strategies. Alternative control strategies are necessary to future-proof control of this pest. Current control methods primarily rely on chemical insecticide sprays or granulosis virus applications during egg hatching. This review focuses explicitly on semiochemical-based manipulation of CM adults and larvae for control in orchards. Topics covered include the role of semiochemicals in integrated pest management, area-wide control, mating disruption, female attractants, larval kairomones, and incorporation into monitoring and control strategies. The potential of CM repellents in a push–pull strategy is also discussed. Primary sources for identifying relevant literature included GoogleScholar and ResearchGate, with a focus on papers published since 2013 but also include relevant papers from 2003. Nine review papers and 119 papers were reviewed. The review emphasizes that effective control necessitates an area-wide approach targeting all life stages (eggs, larvae, pupae, and adults). Comprehensive monitoring is crucial for identifying CM “hot-spots” and enhancing targeted interventions. Growers must consider landscape context when designing control programs. Lastly, recommendations are provided for future research and CM management strategies. There are opportunities to explore and exploit female kairomone attractants and repellents in control strategies and modify monitoring traps to be more attractive and autonomous.

Tracing the origin of the alien pest Cydia pomonella in Algeria through a worldwide comparison of the species’ DNA barcodes

Abstract DNA barcodes (i.e., gene sequences used as identifiers for species identification) constitute a very useful tool in invasive pest research. Using them, we studied, for the first time, the origin and population structure of the alien Cydia pomonella in Africa, a major apple pest. We sequenced a fragment of the mitochondrial gene Cytochrome c oxidase subunit I (COI) from C. pomonella caterpillars collected in northern Algeria and compared them with DNA barcodes from the rest of the world available at public repositories. The phylogeny built upon that COI database supports the European origin of the species: haplotypes at the base of the phylogeny were registered in this continent. In Algeria, the results suggest that this pest could have been introduced from Europe, with two unique African mutations probably favoured by C. pomonella multivoltinism and large population sizes. Population genetic analyses at three Algerian localities showed neither structure nor founder effects. However, to confirm the underlying process of the observed population patterns, it is necessary to perform analyses using genes with higher mutation rates. Compared to higher resolution markers, mitochondrial DNA barcodes are still a cost‐effective tool for taxonomic determination of unknown specimens and, sometimes, may also allow a preliminary tracing of its origin. This is crucial to detect and stop the spread of invasive pests (i.e., borders). We encourage further studies including functional genes to assess whether any mutations are promoting C. pomonella adaptation to the Northern Africa environment.

First record of Leucoptera malifoliella (O. Costa, 1836) (Lepidoptera: Lyonetiidae) in Tunisia

AbstractThe leaf blister moth, Leucoptera malifoliella is an invasive pest of apple and other Rosaceae species. It causes significant damage to apple leaves, threatening the production quality and the overall health of the tree. In this paper, we report the first record of L. malifoliella in Tunisia and give details of its distribution in apple orchards of central west Tunisia (Kasserine governorate). After signs of the pest were observed, cocoons were collected from the field and reared until adult eclosion. Identification of the species was based on observations of external morphology in the laboratory in addition to the field diagnosis based on larval damage on leaves. Leucoptera malifoliella was collected firstly in Foussana delegation, then in Sbeitla, Sbiba, Djedliane, Kasserine and Feriana delegations, showing that it is widely spread in the Kasserine governorate. This leaf miner was clearly identified as one of the most abundant pests in some localities. This paper describing the first record of L. malifoliella in central west Tunisia aims to raise awareness on the pest and encourage the collaboration between apple producers, researchers and extension services in the region to establish a programme of monitoring and managing the pest.

Non-target impacts of hail netting and insecticides on natural enemy abundance and diversity in a Midwestern U.S. commercial apple orchard

A variety of exclusion systems for integrated pest management (IPM) have become viable alternatives to traditional insecticide use in tree fruit production globally. Exclusion netting has been particularly effective as a control strategy for several insect pests of apple. In Minnesota, USA, hail netting, used to protect high-value fruit from hail damage, was recently shown to effectively exclude the two major insect pests: codling moth (Cydia pomonella L.) and apple maggot (Rhagoletis pomonella Walsh). The present study aimed to determine the impact of single-row, drape-style hail netting (DrapeNet®) on the natural enemy community and how this compared to the impact of insecticides used by a Minnesota grower. Sampling occurred during 2021 and 2022 at two orchard locations in Minnesota using yellow sticky traps. Natural enemies were identified to the family level, and these data were examined to determine family richness, diversity, and relative abundance. Compared to unnetted rows, rows covered with hail netting had a significant reduction in both family richness and predator abundance, as measured by yellow sticky traps. The overall abundance of the three most common predator families, Anthocoridae, Empididae, and Coccinellidae, along with other families, was significantly reduced under netting. By contrast, using Simpson's diversity index we found that family diversity was significantly higher under netting, while the insecticides used did not affect diversity. These findings indicate that non-target effects of netting include a general reduction in the abundance of natural enemies and the disproportionate exclusion of Anthocoridae. However, predator diversity was higher under netting, indicating that biological control services could remain intact under netting even with modest use of insecticides.

Apple pest and pathogen reduction in landscapes with large patch size and small area of orchards: a national-scale analysis

ContextThe 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.ObjectivesThe 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.MethodsUsing 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.ResultsLandscapes 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.ConclusionsOur 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.

Effects of nonnutritive sugar inclusion in laboratory diets and attracticidal spheres on survivorship and mobility of 2 Dipteran species, Rhagoletis pomonella (Diptera: Tephritidae) and Drosophila suzukii (Diptera: Drosophilidae).

Native apple maggot fly, Rhagoletis pomonella, and invasive spotted-wing drosophila, Drosophila suzukii, are key pests of apple and small fruit, respectively, in the United States. Both species are typically managed with standard insecticide applications. However, interest in alternative strategies that result in insecticide reductions has led to evaluations of nonnutritive sugars as toxicants for Drosophila species and development of attracticidal spheres for both species. Here, we evaluated the survivorship of R. pomonella and D. suzukii when provided with standard diets that substituted saccharin, sucralose, aspartame, erythritol, dextrose, or mannitol for the sucrose component and compared them with standard diets and water-only controls for up to 15 days. Presence of erythritol and mannitol significantly decreased survivorship of R. pomonella and erythritol significantly decreased the survivorship of D. suzukii. However, mobility trials following a 2 h exposure to aqueous solutions of each sugar treatment resulted in no strong impact on either species. Survivorship after 30 min exposure to erythritol or mannitol alone, or in combination with varying concentrations of sucrose (serving as a phagostimulant) at 30 min and 24 h were evaluated for both species. Only D. suzukii survivorship was affected with decreased survivorship on erythritol:sucrose solutions of 20:0% and 15:5% for 24 h. Based on all results, erythritol appeared most promising, and was integrated into attracticidal spheres as a toxicant but even at the highest concentration, survivorship remained unaffected for either species, thus making this nonnutritive sugar impractical and ineffective as a toxicant substitute in attracticidal spheres.

Bio-efficacy report of Specol-OSO horticultural mineral oil against major insect pests of apple in Kashmir (J&K)

Bio-efficacy report of Specol-OSO horticultural mineral oil against major insect pests of apple in Kashmir (J&K)

Identification of the major insect pests and their biological characteristics in apple orchards (Uzbekistan)

The study presents biological characteristics of the major apple pests in Uzbekistan. For evaluating the key insects, field observation has been conducted. The in-situ results have identified six major apple pests in three selected research areas of the province. Thus, in this study, the biological behavior and potential harm of the determined pests on apple trees are provided. This field-based investigation helps to establish an integrated method to fight against those harmful pests and their devastating influence.

Environmental complexity and predator density mediate a stable earwig-woolly apple aphid interaction

Regulation of insect pests by predators is an integral component of biological pest control. However, predator-prey interactions often reach a stable state leading to coexistence, which can be problematic for plant protection if the persisting herbivores exceed a damage threshold. Consequently, understanding the factors mediating this stability is crucial for successful biocontrol. Here, we investigated the predator-prey interaction of an important apple pest, the woolly apple aphid (WAA) (Eriosoma lanigerum Hausm.) (Hemiptera: Aphididae), and its predator, the omnivorous common earwig (Forficula auricularia L.) (Dermaptera: Forficulidae). To this end, we introduced increasing densities of earwigs in gauze-bagged branches harbouring defined amounts of WAA and recorded predation on individual WAA colonies for almost one month. We showed that the spatial complexity of the predation environment and the earwig density determined the predation efficiency on WAAs. Earwigs could eradicate WAAs regardless of density on short apple branches without lateral twigs (first trial). However, on longer, more complex branches (second trial), stable states of WAAs and earwigs were observed. Only the highest earwig density could completely consume WAA regardless of branch complexity. We introduce a conceptual model to describe the searching capability of earwigs, which determines their efficiency in locating prey. Our model predicts that searching capability is reduced by environmental complexity; this effect is alleviated by increased earwig density, as more predators possess a higher searching capability. Conclusively, our model explains why only the highest earwig density could completely consume WAA irrespective of complexity and illustrates how predator density and environmental complexity jointly influence predation. Thus environmental characteristics not inherent to our predator-prey interaction were a decisive factor for effective predation on gauze-bagged branches. Based on these results, reducing tree complexity by pruning or augmenting earwig populations in orchards can increase biocontrol of the WAA.

Chemosensory Receptor Expression in the Abdomen Tip of the Female Codling Moth, Cydia pomonella L. (Lepidoptera: Tortricidae).

In insects, the chemical senses influence most vital behaviors, including mate seeking and egg laying; these sensory modalities are predominantly governed by odorant receptors (ORs), ionotropic receptors (IRs), and gustatory receptors (GRs). The codling moth, Cydia pomonella, is a global pest of apple, pear, and walnut, and semiochemically based management strategies limit the economic impacts of this species. The previous report of expression of a candidate pheromone-responsive OR in female codling moth ovipositor and pheromone glands raises further questions about the chemosensory capacity of these organs. With an RNA-sequencing approach, we examined chemoreceptors' expression in the female codling moth abdomen tip, sampling tissues from mated and unmated females and pupae. We report 37 ORs, 22 GRs, and 18 IRs expressed in our transcriptome showing overlap with receptors expressed in adult antennae as well as non-antennal candidate receptors. A quantitative PCR approach was also taken to assess the effect of mating on OR expression in adult female moths, revealing a few genes to be upregulated or downregulating after mating. These results provide a better understanding of the chemosensory role of codling moth female abdomen tip organs in female-specific behaviors. Future research will determine the function of specific receptors to augment current semiochemical-based strategies for codling moth management.

Evaluation of the aphicidal activity of Salvia microphylla (Lamiaceae) aqueous extracts against Aphis pomi (Aphididae)

Abstract The aphid Aphis pomi represents an important pest of apples. The use of botanical extracts may be a safe and effective way to control this pest. In this context, we studied the aphicidal and repellent effects of aqueous extracts of Salvia microphylla against A. pomi. Five concentrations were tested on two main aphid parameters: mortality rate and percentage of repellency. The results showed that concentrations of 10% recorded the highest mortality rate (73.33%) and percentage of repellency (62.59%). After further field investigations, the use of S. microphylla may constitute a component of the integrated management of aphids.

Spontaneous flowering vegetation favours hoverflies and parasitoid wasps in apple orchards but has low cascading effects on biological pest control

Floral resources support several ecosystem services in agroecosystems, such as pollination or biological control. Many beneficial organisms feed on nectar or pollen for an important part of their life cycle. Providing adequate and sufficient floral resources through the conservation of spontaneous flowering vegetation may be a strategy to improve biological pest control. However, the role of spontaneous flowering plant species has mainly been evaluated in the recruitment of natural enemies. Cascading effects on pest regulation and damage reduction are rarely studied. Here we evaluated the effect of spontaneous flowering vegetation on pest regulation in 18 Mediterranean apple orchards. We focused on two main apple pests, Cydia pomonella and Dysaphis plantaginea, and on two groups of their natural enemies depending on floral resources: hoverflies and parasitoid wasps. We combined generalised linear mixed models and piecewise structural equation models to test for direct and indirect effects of spontaneous flowering plant species on beneficial insects (hoverflies, parasitoid wasps, wild bees and honeybees) and on pest regulation. We also tested for potential negative interactions between honeybees and hoverflies, parasitoid wasps or wild bees. There was a positive and significant effect of insect-pollinated flowers on hoverflies, parasitoid wasps and bees, but small cascading effects on D. plantaginea or C. pomonella density and associated damage. There was no significant relationship between honeybees and hoverflies, parasitoid wasps or wild bees. The reduction of D. plantaginea infestation was partially mediated by hoverfly abundance. Furthermore, we observed effects of spontaneous flowering vegetation on pest regulation and damage reduction independent of hoverfly and parasitoid wasp abundances. These results highlight the importance of conserving floral resources to support biological control of apple pests. Further studies are needed to better understand interactions between spontaneous flowering vegetation and crop management practices to promote sustainable pest regulation strategies.

Phytophagous Insects of Malus in Uzbekistan

The paper presents descriptions of some of the most common apple pests. In Uzbekistan, the apple tree is one of the most important fruit trees. Damage to the apple tree must be controlled, and for this, all types of pests must be studied. Horticultural products from Uzbekistan are in demand on the world market. Currently, the export of food, fruit and vegetable products totaled about 5 billion dollars. Over the past three years, the volume of exported agricultural products has more than tripled. On fruit crops, one of the most dangerous pests are mites. Biological control measures on these crops have not yet been developed to the perfection as against pests belonging to the class of insects. There are studies where effects on the number of spider mites on an apple tree are noticed, where varieties with weak and medium pubescence are especially affected by the mite, and varieties with felt pubescence are slightly damaged.

Temperature but Not Photoperiod Can Predict Development and Survival of an Invasive Apple Pest.

The apple leaf-curling midge, Dasineura mali Kieffer (Diptera: Cecidomyiidae), is an invasive pest of apple, and can contaminate fresh fruit for export, causing biosecurity problems. To provide crucial information for its pest risk analysis, forecast, and management, we investigated the effects of temperatures (5, 10, 15, 20 and 25 °C) and daylengths (10, 11, 12, 13, 14 and 15 h) on its development and survival. The midge eggs failed to hatch at 5 °C and larvae could not complete development at 10 °C. Pupation and emergence rates were significantly higher at 20 °C than at 15 °C and 25 °C. Daylength had no effect on these parameters. The low temperature threshold and thermal requirement to complete development from eggs to adults were 3.7 °C and 627 degree-days, respectively. The midge had a significantly lower thermal requirement for the completion of its lifecycle at 20 °C (614.5 degree-days) than at 15 °C (650.1 degree-days) and 25 °C (634.8 degree-days). The thermal model developed in this study provided accurate predictions of the number of D. mali generations and adult emergence time in each generation in different regions of New Zealand. We suggest that the model could be used to predict population dynamics of this pest in other parts of the world.

Chemical composition and toxicity of Artemisia herba-alba essential oil against Cydia pomonella L. (Lepidoptera:Tortricidae) under laboratory conditions

The codling moth Cydia pomonella L. (Lepidoptera: Tortricidae) is a destructive pest of apples and pears worldwide. Due to the phytotoxic effect of chemical fumigant on postharvest apple, botanical insecticides are an interesting alternative to synthetic chemical insecticides regarding their low environmental impact. Hence, in the present study, we investigated the chemical composition and fumigant toxicity of Artemisia herba-alba essential oil against the fifth-instar codling moth, under laboratory conditions. In total, 49 compounds were identified by gas chromatography/mass spectrometry. The major constituents determined were dextro-camphor (30.60%), 2,6-dimethyl-1,3,6-heptatriene (26.71%), safranal (7.04%), borneol (3.94%) and eucalyptol (3.47%). A. herba-alba essential oil showed strong larvicidal activity, with minimum lethal concentration LC50 values of 13.49 and 04.45 μl L-1 air at 24 h and 48 h, respectively. The LT50 values at the lowest (1.81 μl L-1 air) and highest oil concentrations (18.18 μl L-1 air) were, in turn, 59.35 to 19.52 h. The obtained results indicated that essential oil of A. herba-alba may have a potential to be developed as a botanical fumigant against the fifth-instar codling moth.

Using a novel convolutional neural network for plant pests detection and disease classification.

Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security. An enhanced convolutional neural network (CNN) along with long short-term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre-trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM-CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes. The study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1 score, accuracy, and specificity using LSTM, ELM, and LR classifiers. The presented model attained 99.2% accuracy compared to the cutting-edge models on different classifiers such as LSTM, LR, and ELM, and performed better compared to transfer learning. Pre-trained models, such as VGG19, VGG18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared with other layers. © 2023 Society of Chemical Industry.

Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth Infestation

The demand for high-quality apples remains strong throughout the year, as they are one of the top three most popular fruits globally. However, the apple industry faces challenges in monitoring and managing postharvest losses due to invasive pests during long-term storage. In this study, the effect of codling moth (CM) (Cydia pomonella [Linnaeus, 1758]), one of the most detrimental pests of apples, on the quality of the fruit was investigated under different storage conditions. Specifically, Gala apples were evaluated for their qualities such as firmness, pH, moisture content (MC), and soluble solids content (SSC). Near-infrared hyperspectral imaging (HSI) was implemented to build machine learning models for predicting the quality attributes of this apple during a 20-week storage using partial least squares regression (PLSR) and support vector regression (SVR) methods. Data were pre-processed using Savitzky–Golay smoothing filter and standard normal variate (SNV) followed by removing outliers by Monte Carlo sampling method. Functional analysis of variance (FANOVA) was used to interpret the variance in the spectra with respect to the infestation effect. FANOVA results showed that the effects of infestation on the near infrared (NIR) spectra were significant at p < 0.05. Initial results showed that the quality prediction models for the apples during cold storage at three different temperatures (0 °C, 4 °C, and 10 °C) were very high with a maximum correlation coefficient of prediction (Rp) of 0.92 for SSC, 0.95 for firmness, 0.97 for pH, and 0.91 for MC. Furthermore, the competitive adaptive reweighted sampling (CARS) method was employed to extract effective wavelengths to develop multispectral models for fast real-time prediction of the quality characteristics of apples. Model analysis showed that the multispectral models had better performance than the corresponding full wavelengths HSI models. The results of this study can help in developing non-destructive monitoring and evaluation systems for apple quality under different storage conditions.

Monitoring the Degree of Mosaic Disease in Apple Leaves Using Hyperspectral Images

Mosaic of apple leaves is a major disease that reduces the yield and quality of apples, and monitoring for the disease allows for its timely control. However, few studies have investigated the status of apple pests and diseases, especially mosaic diseases, using hyperspectral imaging technology. Here, hyperspectral images of healthy and infected apple leaves were obtained using a near-ground imaging high spectrometer and the anthocyanin content was measured simultaneously. The spectral differences between the healthy and infected leaves were analyzed. The content of anthocyanin in the leaves was estimated by the optimal model to determine the degree of apple mosaic disease. The leaves exhibited stronger reflectance at a range of 500–560 nm as the degree of disease increased. The correlation between the spectral reflectance processed by the Gaussian1 wavelet transform and anthocyanin was significantly improved compared to the corresponding correlation results with the original spectrum. The VPs-XGBoost anthocyanin estimation model performed the best, which was sufficient to monitor the degree of the disease. The findings provide theoretical support for the quantitative estimation of leaf anthocyanin content by remote sensing to monitor the degree of disease; they lay the foundation for large-scale monitoring of the degree of apple mosaic disease by remote sensing.

APPLE PESTS AND DISEASES

APPLE PESTS AND DISEASES

EfficientDet-4 Deep Neural Network-Based Remote Monitoring of Codling Moth Population for Early Damage Detection in Apple Orchard

Deep neural networks (DNNs) have recently been applied in many areas of agriculture, including pest monitoring. The codling moth is the most damaging apple pest, and the currently available methods for its monitoring are outdated and time-consuming. Therefore, the aim of this study was to develop an automatic monitoring system for codling moth based on DNNs. The system consists of a smart trap and an analytical model. The smart trap enables data processing on-site and does not send the whole image to the user but only the detection results. Therefore, it does not consume much energy and is suitable for rural areas. For model development, a dataset of 430 sticky pad photos of codling moth was collected in three apple orchards. The photos were labelled, resulting in 8142 annotations of codling moths, 5458 of other insects, and 8177 of other objects. The results were statistically evaluated using the confusion matrix, and the developed model showed an accuracy > of 99% in detecting codling moths. This developed system contributes to automatic pest monitoring and sustainable apple production.