Search Results (727)

Reliable identification of pest developmental stages provides a foundation for investigating aphid development and adaptive mechanisms and informs the selection of timely interventions in integrated pest management. In this study, eight morphological indicators of Uroleucon gobonis were distinguishing under a stereomicroscope. Additionally, six candidate genes identified from transcriptomic data were selected to characterize their expression profiles across five developmental instars. These results showed that the body length enabled distinguishing of the aphids from the first instar to the fourth instar, while antennal length and cauda length facilitated distinction between the second–fourth instar nymphs; cornicle length allowed separation of the third instar and subsequent instars. In contrast, body width permitted discrimination primarily between the first and second instars; head width, foreleg length, and hindleg length exhibited substantial overlap across instars, limiting their utility for instar discrimination. Elevated expression of DN1031, DN1019, and DN1093 was a prominent feature of first instar nymphs. DN1098 was persistently increased from the first to the third instar, which facilitated discrimination among these three developmental stages. The fourth instar was characterized by the concurrent down-regulation of DN1098 and DN1031 relative to the third instar, whereas adults exhibited a distinct expression peak of DN136 compared to other developmental stages. This study establishes an integrated instar-identification system for U. gobonis, combining rapid morphological screening with molecular characterization. This framework provides a valuable methodological basis for investigating developmental plasticity and supports the development of targeted pest management strategies. Full article

The stable production of crops such as corn, wheat, soybeans, and canola is increasingly threatened by widespread pest infestations. Conventional manual pest surveys are hampered by low operational efficiency, subjective assessment bias, and delayed feedback, thereby impeding their ability to satisfy the demands of precision agriculture. To address these challenges, we proposes an intelligent pest detection framework based on EfficientNet and Feature Pyramid Network (FPN) for fast and accurate field pest identification. EfficientNet is adopted as the lightweight attention-embedded backbone to extract hierarchical features, and multi-scale detection plus hierarchical FPN fusion are integrated to improve recognition performance for tiny, inconspicuous pests. The experimental results on 37 common pest species in field crops showed that the proposed model achieves a mean average precision at Intersection-over-Union (IoU) threshold 0.5 (mAP@0.5) of 98.89%, 1.57% average recognition error rate, and with an average inference time of merely 0.048 s per image, balancing outstanding detection accuracy and real-time performance. Furthermore, this approach delivers a lightweight, reliable, and automated monitoring solution for field pest surveillance, thereby facilitating data-driven, precise pest management and advancing the practice of sustainable, green precision agriculture. Full article

Ips hauseri Reitter is the most important wood-boring pest of natural Tianshan spruce (Picea schrenkiana) forests and is listed as an A2 quarantine pest by the European and Mediterranean Plant Protection Organization (EPPO). Because the beetle is small and cryptic and its immature stages cannot be reliably identified by morphology, a rapid molecular identification method is urgently needed. Here, we targeted the mitochondrial cytochrome c oxidase subunit I (COI) gene, designed a set of I. hauseri-specific LAMP primers, and established a visually interpretable LAMP assay using a WarmStart colorimetric system. The optimal reaction conditions were 63.8 °C for 35 min. The assay amplified only I. hauseri, with no cross-reaction with the related species Pityogenes spessivtsevi, Ips typographus, Ips subelongatus, and Hylurgus ligniperda; its limit of detection was 100 pg of genomic DNA; and it amplified efficiently from adults, larvae, pupae, and a single egg. Positive results could be scored directly by eye from the red-to-yellow colour change in the reaction, requiring no precision instruments at any step. Using a non-destructive alkaline crude-DNA extraction (HotSHOT), the assay also amplified I. hauseri directly from single adults and larvae, confirming its compatibility with a rapid, field-simple sample preparation. The LAMP assay was rapid, specific within the tested panel, and simple. However, broader validation with additional sympatric non-target species and geographically distinct populations of I. hauseri is needed before routine field or quarantine application. Full article

Leucoptera malifoliella (Lepidoptera: Lyonetiidae) is a quarantine pest of apple and other Rosaceae fruit trees whose range is expanding into new territories. Its minute size and morphological overlap with closely related Lyonetiidae make routine identification unreliable, especially for larvae and damaged specimens. We compared COI sequences from six common small Lepidoptera species found in orchards and designed the species-specific primer pair SXW-F/SXW-R. The resulting polymerase chain reaction (PCR) assay amplifies an ~500 base pairs (bp) fragment exclusively from L. malifoliella; no product was detected in any of five non-target species. The reaction tolerates annealing temperatures of 50–58 °C and consistently detects the target across all life stages (first- to third-instar larvae, pupae, adults) and all adult tissues tested (antennae, head-thorax, abdomen, wings, legs). Detection sensitivity reaches 0.03 ng/μL—approximately one-thousandth of the DNA content of a single adult. This is the first species-specific COI (SS-COI) method reported for L. malifoliella. It furnishes a rapid, specific, and sensitive diagnostic tool for quarantine inspection, field monitoring, and integrated pest management (IPM) programs. Full article

Driven by the human demand for tea beverages, tea production has expanded worldwide. Tea production in China, the world’s largest tea producer, is constrained by Dendrothrips minowai and Matsumurasca (Matsumurasca) onukii. Various management measures for controlling these pests have been developed, but their implementation requires knowledge of the pest distribution, which is currently insufficient. Therefore, precise management of these pests is a major challenge. Using optimized MaxEnt models for the distributions of the two pests across the current and future timeframes, we predicted the overlap of their suitable habitats. The central and southern provinces of China were identified as the primary suitable habitats of both pests at the current time. The suitable habitats will diverge in the future, with D. minowai habitats declining by 29.70–61.90% and M. onukii habitats increasing by 8.05–43.62%. These results demonstrate species-specific responses to climate change. Despite a decrease in overlap areas, the current and future overlap areas consistently coincide with some major tea-growing areas such as Guizhou, Yunnan, Hunan, and Fujian. The predicted overlap areas can aid the identification of priority areas, optimization of resource allocation, and dynamic adjustment of management measures, improving the precision and efficiency of managing the two pests. Full article

The red palm weevil, Rhynchophorus ferrugineus (Olivier), is among the most destructive invasive pests of date palms worldwide. In this study, mitochondrial cytochrome c oxidase subunit I (COI) and nuclear internal transcribed spacer (ITS) markers were analyzed in parallel to comparatively assess genetic diversity and haplotype variation in R. ferrugineus populations from Qassim Province, Saudi Arabia. Sequencing success rates reached 96.5% and 93.0% for COI and ITS, respectively. COI sequences exhibited very low nucleotide divergence among Qassim specimens (0.0–0.0077), indicating a highly conserved mitochondrial background and close phylogenetic similarity (p-distance = 0.0–0.0078) with an Egyptian reference haplotype (GU581319) and the reference R. ferrugineus mitochondrion (KT428893). In contrast, ITS analyses revealed substantially greater nuclear variation, identifying multiple haplotype groups with divergence levels of 10–19%. Haplotype diversity was higher in ITS (Hd = 0.876 ± 0.041) than in COI (Hd = 0.663 ± 0.068), while nucleotide diversity in ITS (π = 0.0387 ± 0.0013) was 36.9-fold greater than in COI (π = 0.00105 ± 0.00025; Z = 27.66, p < 0.001). Phylogenetic reconstruction showed greater population structuring in ITS than in COI. Integrated mitochondrial and nuclear markers improve genetic resolution and species identification in R. ferrugineus. Full article

Mangrove forests along China’s coastline serve as vital ecological barriers and blue carbon reservoirs. However, pests and diseases have become the primary biotic threats driving stand decline and diminished carbon sequestration capacity. This review synthesizes current knowledge on the major insect pests and plant diseases affecting Chinese coastal mangroves, focusing on their species profiles, characteristic damage symptoms, occurrence dynamics, and integrated control strategies. Fungal pathogens predominate among the diseases, with outbreaks most common during periods of high temperatures and humidity or low temperatures combined with high humidity; these often interact synergistically with insect pests. The dominant insect pests comprise leaf-feeding Lepidoptera, sap-sucking Hemiptera, and wood-boring Coleoptera, which spread through diverse pathways and can rapidly produce extensive “scorched” damage across mangrove stands during epidemic events. Control efforts follow the principle of “prevention first and integrated management,” incorporating cultural practices, chemical interventions, biological control agents, physical trapping methods, and rigorous quarantine-monitoring protocols. When applied in concert, these measures effectively limit damage to acceptably low levels. Recent studies identify pest–disease interactions and climate change as the foremost challenges in current management. Future priorities should include advancing molecular identification techniques, breeding disease-resistant varieties, and developing environmentally friendly biopesticides to establish precision ecological control systems. Such advances will deliver robust scientific support for mangrove conservation and the achievement of China’s dual-carbon goals. Full article

The brown marmorated stink bug (Halyomorpha halys) is a major invasive pest threatening fruit production across Europe. This study integrates spatiotemporal geostatistical modelling with degree-day and photoperiod analyses to characterise its seasonal dynamics in Emilia-Romagna (Italy) from 2020 to 2022. Weekly pheromone trap data were used to quantify developmental succession among Small nymphs (early instars, N1–N3), Large nymphs (late instars, N4–N5), and Adults. Time-series and cross-correlation analyses confirmed consistent developmental delays across years, with Small preceding Large by approximately two weeks and Adults emerging after an additional two to three weeks. However, global inter-stage correlations were moderate (r ≈ 0.4–0.5), indicating substantial spatial heterogeneity among monitoring sites and suggesting that regional averages do not fully capture local population dynamics. To address this variability, a three-dimensional spatiotemporal geostatistical model (space × time) was implemented using Direct Sequential Simulation. The model successfully reproduced seasonal population waves and interannual differences in onset and persistence. The identification of persistent hotspots and stage-specific temporal windows is biologically relevant because it highlights where and when H. halys populations are most likely to increase. As such, from an IPM perspective, these outputs can support earlier monitoring, more precise timing of management interventions, and spatial prioritization of control efforts. These findings demonstrate that combining stage-specific temporal analysis with spatially explicit modelling improves forecasting accuracy and supports more precise timing of biological and chemical interventions. The proposed framework provides a scalable tool for climate-responsive integrated pest management in fruit-growing systems. Full article

The two-spotted spider mite, Tetranychus urticae Koch, is a major agricultural pest with a rapid propensity for developing acaricide resistance. Bifenazate targets mitochondrial cytochrome b (CYTB). While the G126S mutation is associated with resistance, its independent role remains unclear, as it often occurs with other SNPs. This study explores the molecular basis of bifenazate resistance in a Russian laboratory strain derived from a St. Petersburg greenhouse population. Disruptive selection with increasing bifenazate concentrations generated resistant and susceptible isofemale lines. AlphaFold2 structural modeling of CYTB indicated that G126S causes a steric clash, leading to conformational destabilization, whereas other reported mutations primarily affect the ligand-binding pocket. Oxford Nanopore sequencing revealed a low initial frequency of the G126S allele (<1%; 226/35,895 reads) in the unselected population. After one year of stepwise selection (0.00005–0.031% a.i.), the mutant allele frequency surged to 90% (7272/8056 reads). No other resistance-associated mutations were found in the analyzed cytb fragment. We report the first identification of the G126S mutation in a Russian T. urticae population and demonstrate rapid fixation under bifenazate selection. Within this genetic background, G126S alone appears sufficient to confer high-level resistance, emphasizing the population-specific nature of resistance evolution and the critical need for local monitoring. Full article

Agricultural research helps us to reduce the risks associated with climate variability, pests and diseases, market fluctuations, and biennial bearing. However, genotype characterization is often conducted independently of economic performance and yield stability, limiting the identification of genotypes that combine high yield, stability, and economic return potential. This study characterized the genotype × harvest season interaction and gross revenue of Coffea canephora clones evaluated in Rondônia, Brazil, over five harvest seasons (2021–2025). Twenty-eight genotypes were evaluated in a randomized complete block design with four replications and five plants per plot, and yield stability was assessed using the centroid method. Cumulative yield over five harvest seasons totaled 306.22 bags ha⁻¹, with a mean of 61.24 bags ha⁻¹ per harvest season. The genotype × harvest season interaction revealed distinct temporal yield patterns. Genotypes closest to the ideotype of maximum yield and stability included BAG19, BRS1216, GJ8, GJ25, AS2, and BAG24. Gross revenue simulations based on contrasting historical coffee price scenarios indicated the economic superiority of these clones. Differences in yield potential contributed more to gross revenue variation than biennial bearing. Relative to the overall mean of the 28 genotypes, these clones achieved an 85% increase in mean yield and a 61.2% increase in mean gross revenue. Full article

Wheat aphids are the primary pests in wheat-producing areas, posing a serious threat to stable, high wheat yields and regional food security. To detect and count wheat aphids under different complex conditions, this study designed an improved model and developed a mini program. Firstly, we constructed a dual-source dataset containing 542 images collected from indoor and field environments, including 117 indoor images and 425 field images. Secondly, we proposed Aphid Identification and Detection YOLO (AID-YOLO), an enhanced YOLO11n-based method for close-range wheat aphid detection and image-level counting. Specifically, the original downsampling structure was replaced with the ADown module to improve feature extraction efficiency while reducing redundant computation, an IEMA multi-scale attention mechanism integrating IRMB and EMA was introduced to strengthen feature learning under complex background interference, and the dynamic upsampling operator DySample was adopted to enhance cross-scale feature fusion. Finally, AID-YOLO achieved a 19.0% reduction in parameter count (2.09 M vs. 2.58 M) and a 19.0% decrease in computational cost (5.1 vs. 6.3 GFLOPs). Across three random seeds, AID-YOLO achieved an average mAP50 of 95.3 ± 0.10%, compared with 93.0 ± 0.39% for the YOLO11n baseline on the combined indoor–field evaluation set. These results suggest that AID-YOLO achieves a favorable balance between detection accuracy and model lightweighting under the tested indoor and field conditions, providing a useful technical reference for intelligent wheat aphid monitoring. Full article

Agricultural pest infestations cause substantial global crop losses. Morphological similarities across species and structural variations across developmental stages render accurate identification a persistently expert-dependent and time-consuming process. Recent deep learning approaches have advanced automated pest classification; however, most efforts have concentrated on architectural design, while optimization strategies have received comparatively little attention. This study proposes a novel optimization framework, hereafter referred to as Adaptive Fractional Gradient Descent (AFGD), that integrates the Grünwald–Letnikov (GL) fractional derivative into the backpropagation process of deep convolutional neural networks. Unlike standard gradient descent, the proposed method maintains a weighted history of past gradients. It dynamically adjusts the fractional order α via Bayesian optimization at regular training intervals, enabling the model to adaptively balance exploiting gradient memory against exploring new gradients throughout training. Experiments conducted on the IP102 benchmark dataset using DenseNet121, ResNet101, and EfficientNetB0 backbones demonstrated consistent accuracy improvements over standard gradient descent across all configurations. In the untrained setting, absolute test accuracy improved by 20.73, 11.51, and 11.01 percentage points for DenseNet121, ResNet101, and EfficientNetB0, although the absolute accuracy levels in this configuration remain modest. Under ImageNet pre-training, the proposed method yielded absolute gains of 6.69, 7.39, and 3.76 percentage points over the corresponding standard gradient baselines, with the highest absolute test accuracy of 70.81% recorded for DenseNet121. These findings indicate that fractional-order gradient control is a promising, architecturally complementary optimization strategy for robust pest classification, with broader implications for deep learning applications in precision agriculture. Full article

Commensalism, a frequent type of interaction among freshwater invertebrates, remains poorly studied. Some turbellarians (Platyhelminthes: Temnocephalidae) are specialized obligate commensals of crustaceans, mollusks, insects, and turtles. In the Neotropics, Temnocephala iheringi inhabits the mantle cavity of snails (Mollusca: Gastropoda) from Pantanal (Brazil) to the Pampean region of Argentina, where several species serve as hosts. This study aimed to molecularly characterize several populations of T. iheringi and to analyze their genetic and morphological variability across different host species and geographic areas. Using the mitochondrial COI marker, we assessed populations associated with five of its seven known host species through phylogenetic reconstructions, species delimitation approaches, and haplotype network analyses. Combined with morphological data, results support COI as an effective identification tool for Temnocephalidae. Several genetic lineages were identified and were largely congruent with collection localities. However, specimens associated with hosts displaying high dispersal capabilities (i.e., Pomacea canaliculata and P. maculata) showed low mitochondrial genetic differentiation and minimal phylogenetic structure across large distances, which may be consistent with recent dispersal and/or ongoing connectivity among populations. These findings provide new insights into the evolutionary dynamics of this specific temnocephalid–snail association. Given that some hosts are highly invasive and even considered pests in several countries, the data and genetic sequences generated in this study may prove valuable for future research on symbiont diversity and dispersal. Full article

Solar insecticidal lamps are important physical control devices for green pest management, but faults in their photovoltaic power supply units can reduce trapping efficiency and shorten service life. To improve fault identification under complex agricultural environments, this study proposes a signal-image-level multimodal fusion network (SIL-MMFN) for detecting and classifying photovoltaic panel operating states in solar insecticidal lamps. The method combines time-series measurements with short-time Fourier transform (STFT)-based time–frequency images. A convolutional image branch extracts spatial features from time–frequency representations, whereas a bidirectional GRU branch with attention models temporal dependencies in the original signals. In addition, physics-informed features based on the illumination–current residual and output power are introduced to enhance discriminative fault information. Field data collected from four agricultural deployment nodes were used to classify normal, open-circuit, and mismatch states. Experimental results show that the proposed method achieved an accuracy of 97.5%, precision of 96.7%, recall of 97.8%, and macro-F1 score of 97.3%, outperforming single-modality and representative comparison models. The results indicate that multimodal fusion helps reduce confusion between open-circuit and mismatch faults and provides a potential approach for operating-state monitoring and maintenance of agricultural photovoltaic equipment. In this study, fault diagnosis refers to the detection and classification of photovoltaic panel operating states, including normal, open-circuit, and mismatch conditions. Full article

Globally, Trichogramma Westwood (Hymenoptera: Trichogrammatidae) is known as the most effective biological control agent due to its ability to parasitize insect pest eggs. However, identifying an appropriate host is vital for Trichogramma to prosper. Therefore, this study delves into the complex role of semiochemicals in shaping the host-seeking behavior of Trichogramma parasitoids, with a particular focus on their responses to both plant-derived and host-derived cues. The mechanism of semiochemical reception in Trichogramma wasps relies on a highly specialized, sensitive olfactory and gustatory system to locate host eggs and mates. Semiochemicals, which mediate ecological interactions, have been identified as pivotal in influencing the parasitic efficiency of Trichogramma species. Trichogramma’s host-seeking behavior is influenced not solely by ovipositional cues but also by the intrinsic physical attributes of Lepidopteran hosts, such as the scales on the wings and abdomen, which emit semiochemicals capable of eliciting positive chemotactic responses, thereby guiding parasitoids toward optimal sites for oviposition. Furthermore, the interplay between insect-derived and plant-derived chemical cues exhibits a synergistic effect, collectively enhancing the chemotactic attraction of Trichogramma, thereby fine-tuning its host-seeking behavior with greater precision and specificity. This study further underscores Trichogramma’s innate behavioral ability to discriminate between host eggs of varying developmental stages, facilitating the precise identification and selection of the most suitable host for parasitization. Age and experience both make Trichogramma more selective of hosts, but younger parasitoids may take a broader approach to host selection due to their greater life expectancy. Furthermore, the removal of these cues affects their host localization and learning abilities. Associative learning enables Trichogramma to exhibit flexible behaviors, providing them with a selective advantage; allows them to explore various hosts; and reduces environmental uncertainty. Plant structure, host density, and host age are the key factors that significantly influence the foraging and parasitism of Trichogramma. The searching speed of this parasitoid is significantly influenced by temperature. Heat stress increases VOC emissions in plants such as potato via stomatal opening, reducing herbivore attraction and enhancing parasitoid recruitment. Furthermore, air pollution, including CO₂, O₃, and NOx, impairs parasitoid efficiency by disrupting volatile-mediated host location and reducing biological control performance. Trichogramma wasps are generally effective biological control agents, but their success depends on the species used, target pest, crop, release density, and field conditions. Overall, species such as T. ostriniae, T. japonicum, and T. leucaniae show the strongest performance in several crops by increasing parasitism, reducing pest damage, and improving yield. This study highlights the successful integration of semiochemical cues in pest management programs and the effective utilization of Trichogramma in conjunction with entomopathogenic bacteria to control Lepidopteran pests. This approach contributes to the development of more effective pest management strategies, thereby promoting agricultural sustainability. Full article