Search Results (81)

Effective control of the rosy apple aphid, Dysaphis plantaginea, is crucial for maintaining apple orchard productivity. Understanding plant-mediated bottom-up and parasitoid-driven top-down interactions is critical for developing optimized pest management strategies. We investigated how host plant diversity and interactions between two parasitoids, Aphidius matricariae and Aphidius ervi, shape aphid suppression across seedlings from ten apple cultivars. Mummy counts, parasitism rate, emergence rate, and sex ratio measurements were used to assess the parasitoid preference and performance. Results revealed significant interactions between host plant identity and parasitoid performance. Mixed parasitoid releases outperformed single-species treatments. Alone, A. ervi achieved significantly higher parasitism rates than A. matricariae. Host plant effects were pronounced; Cripps Pink enhanced A. matricariae performance, while Golden Delicious and Red Delicious improved A. ervi metrics. Emergence rates and sex ratios varied by plant origins, with Elstar enhancing A. ervi and Granny Smith enhancing A. matricariae. Choice tests indicated cultivar-specific parasitoid preferences, and heatmap analysis revealed apparent competition among aphids mediated by parasitoids, with Braeburn and Gala acting as key parasitoid sources. Study findings indicate that managing apple cultivar diversity and exploiting complementary parasitoid interactions can improve D. plantaginea biocontrol in orchards. Full article

Accurate monitoring of orchard pests is vital for green and efficient apple production. Yet images captured by intelligent pest-monitoring lamps often contain small targets, weak boundaries, and crowded scenes, which hamper detection accuracy. We present MDAS-YOLO, a lightweight detection framework tailored for smart pest monitoring in apple orchards. At the input stage, we adopt the LIME++ enhancement to mitigate low illumination and non-uniform lighting, improving image quality at the source. On the model side, we integrate three structural innovations: (1) a C3k2-MESA-DSM module in the backbone to explicitly strengthen contours and fine textures via multi-scale edge enhancement and dual-domain feature selection; (2) an AP-BiFPN in the neck to achieve adaptive cross-scale fusion through learnable weighting and differentiated pooling; and (3) a SimAM block before the detection head to perform zero-parameter, pixel-level saliency re-calibration, suppressing background redundancy without extra computation. On a self-built apple-orchard pest dataset, MDAS-YOLO attains 95.68% mAP, outperforming YOLOv11n by 6.97 percentage points while maintaining a superior trade-off among accuracy, model size, and inference speed. Overall, the proposed synergistic pipeline—input enhancement, early edge fidelity, mid-level adaptive fusion, and end-stage lightweight re-calibration—effectively addresses small-scale, weak-boundary, and densely distributed pests, providing a promising and regionally validated approach for intelligent pest monitoring and sustainable orchard management, and offering methodological insights for future multi-regional pest monitoring research. Full article

The European Union’s goal to reduce pesticide risk, exemplified by restrictions on insecticides like neonicotinoids, necessitates a shift from single-substance risk assessment to a holistic evaluation of pest control strategies. To address this, a novel decision-support grid was developed that integrates 13 environmental, biodiversity, and human health risk indicators for multiple active substances across an entire crop season into a single Final Scenario Score (FSS), ranging from 0 to 1 (where 1 is the risk of the reference scenario). This framework was applied to three case studies in Belgium—sugar beet, apple, and pear cultivation—where neonicotinoid-based reference scenarios were compared with chemical and/or organic alternatives under low (best-case) and high (worst-case) pest pressure conditions. The results highlight the complexity of finding viable alternatives, with an FSS below 0.75 as the justification threshold. In sugar beet, only the best-case chemical alternative (FSS = 0.71) met the threshold, while worst-case chemical alternatives failed due to increased risk. For apple and pear, organic alternatives consistently showed low-risk scores (FSS 0.27–0.61) but faced important efficacy gaps against key insect. Chemical alternatives in orchards were justifiable in low-pressure scenarios (FSS 0.64–0.73) but failed under high pest pressure (FSS 0.91–0.93). This novel decision-support grid proves to be a valuable tool for guiding sustainable pest control strategies for regulators and field advisors. Full article

The production of apples plays a crucial role in global agriculture. In 2023, the world production of these fruits amounted to nearly 150 million tonnes, cultivated on 6.6 million ha. Today’s horticulture faces the difficult challenge of maintaining high productivity while simultaneously reducing negative environmental impact. Traditional methods based on chemical pesticides encounter increasing problems, such as biodiversity loss, toxic residues in food, development of pest resistance, and disrupted balance of ecosystems. Integrated Pest Management (IPM) responds to these challenges by combining biological and agrotechnical methods with selective use of chemicals. Biopesticides are a crucial component of IPM, and they include antagonist microorganisms, substances of natural origin, and other biological methods of control, which represent effective alternatives to conventional measures. Their development is driven by consumer requirements concerning food safety, as well as by the need to protect the environment. The aim of this article is to highlight current problems in apple production, describe microorganisms and natural substances used as biopesticides used for the protection of apple orchards, as well as present the characteristics of modern technologies used for biocontrol in apple orchards. Full article

Venturia inaequalis, the cause of apple scab, readily develops resistance to fungicides with specific modes of action. Knowledge of the spatial and temporal pattern of resistance development is therefore relevant to fruit producers and their consultants. In the Lower Elbe region of Northern Germany, a two-year survey based on a conidial germination test was conducted, examining fungicide resistance in 35 orchards under Integrated Pest Management (IPM), 16 orchards of susceptible cultivars as well as a further 12 orchards of scab-resistant (Vf) cultivars under organic management, and 34 abandoned or unmanaged sites. No evidence of resistance to SDHI compounds (fluopyram, fluxapyroxad) was found after >5 yr of their regular use. Resistance to anilinopyrimidines (cyprodinil, pyrimethanil) had disappeared 15 yr after its widespread occurrence. Isolates from a few IPM orchards showed a reduced sensitivity to dodine. Double resistance to the MBC compound thiophanate-methyl and the QoI trifloxystrobin was rare in V. inaequalis strains that had achieved breakage of Vf-resistance, but very common (>50%) on scab-susceptible cultivars in IPM, organic and abandoned orchards in the ‘Altes Land’ core area of the Lower Elbe region, and in IPM orchards in the periphery. We conclude that resistance to QoI and MBC fungicides is persistent even decades after their last use, and that the core area harbours a uniform population adapted to intensive crop protection, whereas isolated orchards in the periphery are colonised by discrete populations of V. inaequalis. Full article

The red-belted clearwing moth Synanthedon myophaeformis is a deleterious pest of apple orchards, wherein the larvae bore tree bark, resulting in reduced fitness and ultimately death. The main control strategies of this pest still rely on the use of pesticides, while alternative agronomic methods for its control coexist, with the application of the main pheromone (Z,Z)-3,13-octadecadien-1-yl acetate. Until now, the molecular bases of the chemosensory systems of the red-belted clearwing moth have been less explored. With the aim to identify novel ligands that may interfere with the behaviour of S. myophaeformis, in this study, we have isolated and functionally characterised some key odorant receptors (ORs) of this moth by selecting paralogues from two main subgroups of the Lepidopteran pheromone receptor (PR) clade: the OR3 subgroup (OR3.1 to OR3.4) and the OR22 subgroup (OR22.1 to OR22.4). We generated transgenic D. melanogaster expressing SmyoORs in ab3A neurons, which we approached by single sensillum recording (SSR). Among these ORs, we deorphanized SmyoOR3.4 to ligands that we have previously identified for orthologues of the codling moth Cydia pomonella, including the pear ester ethyl-(E,Z)-2,4-decadienoate, its methyl ester analogue methyl-(E,Z)-2,4-decadienote, and the unsaturated aldehyde (Z)-6-undecenal. With this approach, we also identified a wide pattern of activation of SmyoOR22.4 to several apple-emitted ligands. Despite the fact that combining SSR with gas chromatography (GC-SSR) did not unveil the activation of the SmyoORs to compounds present in the headspace from apples, GC-SSR unveiled the enhancement of the SmyoOR3.4 spiking at nanogram doses of both pear ester, methyl ester, and (Z)-6-undecenal. For the first time, this study deorphanized ORs from the red-belted clearwing moth and identified ligands as possible semiochemicals to add to the ongoing strategies for the control of this pest. Full article

Evaluating cultivar susceptibility to biotic stressors in apple orchards is essential for selecting genotypes adapted to local conditions and for designing effective plant protection strategies. This study conducted a comparative assessment of five apple cultivars (‘Florina’, ‘Jonathan’, ‘Golden Delicious’, ‘Pinova’, and ‘Idared’) in response to major fungal diseases (Venturia inaequalis, Podosphaera leucotricha, and Monilinia spp.) and insect pests (Eriosoma lanigerum, Quadraspidiotus perniciosus, Anthonomus pomorum, Aphis spp., and Cydia pomonella). The cultivars were monitored over a five-year period in six orchards located in Central Transylvania, Romania. Significant differences in phytosanitary behavior were recorded among cultivars and locations. ‘Florina’ consistently showed the highest tolerance to pathogens and pests across all sites and years, while ‘Jonathan’ and ‘Golden Delicious’ proved highly susceptible, particularly to apple scab, powdery mildew, aphids, and codling moth. Pest incidence was strongly influenced by temperature, while disease occurrence was more closely linked to precipitation patterns. Heritability analysis indicated that genetic factors played a substantial role in shaping cultivar responses to most biotic stressors. The integrated approach to cultivar–location–pathogen and pest interactions offers practical insights for optimizing orchard protection strategies under variable ecological conditions. Full article

Apple detection in natural environments is crucial for advancing agricultural automation. However, orchards often employ bagging techniques to protect apples from pests and improve quality, which introduces significant detection challenges due to the varied appearance and occlusion of apples caused by bags. Additionally, the complex and variable natural backgrounds further complicate the detection process. To address these multifaceted challenges, this study introduces AAB-YOLO, a lightweight apple detection model based on an improved YOLOv11 framework. AAB-YOLO incorporates ADown modules to reduce model complexity, the C3k2_ContextGuided module for enhanced understanding of complex scenes, and the Detect_SEAM module for improved handling of occluded apples. Furthermore, the Inner_EIoU loss function is employed to boost detection accuracy and efficiency. The experimental results demonstrate significant improvements: mAP@50 increases from 0.917 to 0.921, precision rises from 0.948 to 0.951, and recall improves by 1.04%, while the model’s parameter count and computational complexity are reduced by 37.7% and 38.1%, respectively. By achieving lightweight performance while maintaining high accuracy, AAB-YOLO effectively meets the real-time apple detection needs in natural environments, overcoming the challenges posed by orchard bagging techniques and complex backgrounds. Full article

The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study reviewed deep learning combined with computer vision for monitoring apple tree growth and fruit production processes in the past seven years. Three types of deep learning models were used for real-time target recognition tasks: detection models including You Only Look Once (YOLO) and faster region-based convolutional network (Faster R-CNN); classification models including Alex network (AlexNet) and residual network (ResNet); segmentation models including segmentation network (SegNet), and mask regional convolutional neural network (Mask R-CNN). These models have been successfully applied to detect pests and diseases (located on leaves, fruits, and trunks), organ growth (including fruits, apple blossoms, and branches), yield, and post-harvest fruit defects. This study introduced deep learning and computer vision methods, outlined in the current research on these methods for apple tree growth and fruit production. The advantages and disadvantages of deep learning were discussed, and the difficulties faced and future trends were summarized. It is believed that this research is important for the construction of smart apple orchards. Full article

Efficient pest control in orchards is crucial for preserving crop quality and maximizing yield. A key factor in optimizing automated variable-rate spraying is accurate tree canopy size estimation, which helps reduce pesticide overuse while minimizing environmental and health risks. This study evaluates the performance of two advanced convolutional neural networks, PP-LiteSeg and fully convolutional networks (FCNs), for segmenting tree canopies of varying sizes—small, medium, and large—using short-term dense-connection networks (STDC1 and STDC2) as backbones. A dataset of 305 field-collected images was used for model training and evaluation. The results show that FCNs with STDC backbones outperform PP-LiteSeg, delivering superior semantic segmentation accuracy and background classification. The STDC1-based model excels in precision variable-rate spraying, achieving an Intersection-over-Union of up to 0.75, Recall of 0.85, and Precision of approximately 0.85. Meanwhile, the STDC2-based model demonstrates greater optimization stability and faster convergence, making it more suitable for resource-constrained environments. Notably, the STDC2-based model significantly enhances canopy-background differentiation, achieving a background classification Recall of 0.9942. In contrast, PP-LiteSeg struggles with small canopy detection, leading to reduced segmentation accuracy. These findings highlight the potential of FCNs with STDC backbones for automated apple tree canopy recognition, advancing precision agriculture and promoting sustainable pesticide application through improved variable-rate spraying strategies. Full article

The Manchurian fruit moth, Grapholita inopinata (Heinrich) (Lepidoptera: Tortricidae), is an important pest of fruit crops, particularly apples (Malus spp., Rosaceae), and is classified as a quarantine pest in many European countries and other world regions. Until recently, this species was known only in Northeastern China, Japan, and Russia (from Eastern Siberia and the Far East). To determine the westernmost distribution of G. inopinata and assess its abundance, we conducted nine-year pheromone monitoring across 13 administrative regions of Russia from 2014 to 2018 and 2021 to 2024. A total of 1866 traps were deployed, capturing 31,962 G. inopinata specimens in 1811 traps. The species was newly detected in eight regions—seven in Asian Russia and one in European Russia (Perm Krai). These findings doubled the moth’s known range on the Asian continent and extended its western boundary to 56° E in European Russia. Between 2021 and 2024, G. inopinata was generally found at low densities across the surveyed regions (≤10 males per trap per week), with the exception of Perm Krai, Omsk, and Novosibirsk Oblasts, where moderate abundance (up to 38 males per trap per week) was recorded. In contrast, from 2014 to 2018, moderate to high population densities (up to 94 males per trap per week), including mass occurrences (over 100 males per trap per week), were observed in Krasnoyarsk Krai, with an absolute peak capture of 303 males in one trap in June 2017. Notably, in 2015–2017, male flight activity in southern Krasnoyarsk Krai exhibited two distinct peaks: one in mid-to-late June and another from late July to mid-August, indicating the development of two generations. This is the first-ever record of a bivoltine seasonal cycle for G. inopinata in Siberia. These findings are critical for improving pest risk assessments and developing early detection strategies, supporting more effective monitoring and management approaches of this orchard pest. Full article

The widespread use of pesticides has long been a cornerstone of modern agriculture, but their overuse has led to several challenges, including increased production costs, food safety risks, and environmental damage. Green pest control technologies (GPCTs) have emerged as a promising alternative to traditional chemical methods, although their widespread adoption is still in progress, and their environmental and economic impacts require further examination. This study aims to evaluate the adoption of GPCT in apple orchards by employing a rigorous framework to measure pesticide intensity per unit, assess the impact of GPCT on pesticide reduction, and analyze the associated environmental effects in large-scale apple farming systems in China. Based on survey data collected from apple farmers across key production regions in China, we apply an Endogenous Treatment Effect Regression (ETR) model to evaluate the effects of these technologies on pesticide usage and concentration. This model allows for more accurate estimates of the treatment effects by addressing selection bias and accounting for both observable and unobservable factors. Our results show that the adoption of GPCT leads to a significant reduction in pesticide use intensity. Notably, the reductions are more pronounced among specific groups of farmers, particularly those who are less risk-averse and those with larger or more fragmented landholdings. These findings underscore the dual ecological and economic benefits of GPCT, providing strong support for policy initiatives that promote sustainable agricultural practices and encourage land consolidation. Full article

The use of rain shelters is a promising agronomic practice to protect crops from rainfall, reducing the need for fungicides to control certain pathogens that take advantage of leaf wetness. However, the combined condition of absence of rain and reduced fungicide schedule can affect the fungal populations, possibly favoring biocontrol agents and/or other pathogens. In this study, the effects this practice on epiphytic and endophytic fungal communities associated with barks, leaves, flowers, and fruits of two apple cultivars (Fuji and Golden Delicious) were evaluated across two seasons. Apple plants were grown under two conditions in a commercial-like orchard: (1) covered by rain shelters with reduced fungicide schedule and (2) uncovered with standard integrated pest management (IPM) schedule. The use of rain shelters combined with reduced fungicide applications affects the overall fungal community structure and their abundance of specific taxa. Leaf epiphytes were the most impacted community, and fungal communities also differed between the two apple cultivars. The use of rain shelters helped reduce fungicide input in the orchard, but it increased the abundance of potential pathogens compared to the IPM in open field conditions, such as powdery mildew and apple scab. Understanding how the plant microbiome responds to new practices that help in reducing fungicides can help developing strategies that avoid the build-up of potentially new pathogens. Full article

Halyomorpha halys (Stål) (Hemiptera: Pentatomidae) is an invasive pest causing significant damage to tree crops. Our study examined the impact of newly designed aggregation pheromone-baited ‘mini–sailboat’ (MSB) traps for controlling H. halys and its effect on the spatial distribution of fruit damage. Four replicates of four traps, with a total of 16 MSB traps, were placed along a 1.3 km border of apple orchards, concentrating the traps on one side of the orchards. A fruit damage assessment for incidence and severity was conducted at increasing distances from the orchard border where the traps were placed, encompassing 107 assessment points. Our study showed that deploying MSB traps along the orchard border significantly increased fruit damage within the first 45 m compared to control plots without traps. However, beyond the first 45 m from the border, there was a significant reduction in damage incidence. In the treated plots, 50% of the damage occurred within 26 m of the traps, while in control plots, within 85 m. Shifting the fruit damage pattern means restricting the pests lingering in a narrow strip near the MSB traps, which paves the way for improved techniques to restructure the crop perimeter. Full article

The apple jewel beetle (AJB), Agrilus mali Matsumura (Coleoptera: Buprestidae), is a dangerous pest of commercial apple orchards across China, the largest apple production country in the world, and has recently become invasive in the Xinjiang Uygur Autonomous Region (XUAR) of northwestern China, where wild apple forests also occur. This pest poses a serious threat to apple production and wild apple forests throughout the world. Global warming is expected to change the geographical distribution of A. mali in China, but the extent of this is unknown. Based on empirical data from 1951 to 2000, a MaxEnt model was used to forecast the ecological distribution of A. mali under three different climate scenarios projected in the fifth report of the Intergovernmental Panel on Climate Change. The results showed that the most important variables were the maximum temperature of November, precipitation in January, and minimum temperatures in April. Under all climate scenarios, the forecasted suitable regions for A. mali in China will expand northward in the 2050s and 2070s. The forecasted highly suitable regions will be 1.11–1.34 times larger than they are currently, and their central distributions will be 61.57–167.59 km further north. These findings suggest that the range and damage caused by A. mali in China will increase dramatically in the future. Monitoring and management measures should be implemented urgently to protect both the commercial apple industry and wild apple resources. Full article