Search Results (186)

Due to its abundance, bird cherry–oat aphid is the most important vector in Poland of the complex of viruses causing barley yellow dwarf virus (BYDV). These viruses infect all cereals. During the growing season, cereal plants are exposed to many species of agrophages, which can limit their growth, development and yield. As observed for many years, global warming contributes to changes in the development of many organisms. Aphids (Aphidoidea), which are among the most important pests of agricultural crops, respond very dynamically to these changes. Under favorable conditions, their populations can increase several-fold within a few days. The bird cherry–oat aphid (Rhopalosiphum padi L.) is a dioecious species that undergoes a seasonal host shift during its life cycle. Its primary hosts are trees and shrubs (Prunus padus L.), while secondary hosts include cereals and various grass species. R. padi feeds directly on bird cherry tree, reducing its ornamental value, and on cereals, where it contributes to yields losses. The species can also damage plants indirectly by transmitting harmful viruses. Indirect damage is generally more serious than direct feeding injury. Monitoring aphid flights with a Johnson suction trap (JST) is useful for plant protection, which enables early detection of their presence in the air and then on cereal crops. To provide early detection of R. padi migrations and to study the dynamics of abundance, flights were monitored in 2020–2024 with Johnson suction traps at two localities: Winna Góra (Greater Poland Province) and Sośnicowice (Silesia Province). The aim of the research conducted in 2020–2024 was to study the dynamics of the bird cherry–oat aphid (Rhopalosiphum padi L.) population in relation to meteorological conditions as recorded by a Johnson suction trap. Over five years of research, a total of 129,638 R. padi individuals were captured using a Johnson suction trap at two locations (60,426 in Winna Góra and 69,212 in Sośnicowice). In Winna Góra, the annual counts were as follows: 5766 in 2020, 6498 in 2021, 36,452 in 2022, 5598 in 2023, and 6112 in 2024. In Sośnicowice, the numbers were as follows: 6954 in 2020, 9159 in 2021, 49,120 in 2022, 3855 in 2023, and 124 in 2024. The year 2022 was particularly notable for the exceptionally high abundance of R. padi, especially in the autumn. Monitoring crops for the presence of pests is the basis of integrated plant protection. Climate change, modern cultivation technologies, and increasing restrictions on chemical control are the main factors contributing to the development and spread of aphids. Therefore, measures based on monitoring the level of threat and searching for control solutions are necessary. Full article

Rice is one of the world’s most important staple crops, and outbreaks of insect pests pose a persistent threat to yield stability and food security in major rice-growing regions. Reliable field-scale rice pest detection remains challenging due to limited datasets, heterogeneous imaging conditions, and inconsistent annotations. To address these limitations, we construct RicePest-30, a field-oriented dataset comprising 8848 images and 62,227 annotated instances covering 30 major rice pest species. Images were collected using standardized square-framing protocols to preserve spatial context and visual consistency under diverse illumination and background conditions. Based on RicePest-30, YOLOv11 was adopted as the primary detection framework and optimized through a systematic hyperparameter tuning process. The learning rate was selected via grid search within the range of 0.001–0.01, yielding an optimal value of 0.002. Training was conducted for up to 300 epochs with an early-stopping strategy to prevent overfitting. For fair comparison, YOLOv5s, YOLOv8s, Faster R-CNN, and RetinaNet were trained for the same number of epochs under unified settings, using the Adam optimizer with a learning rate of 0.001. Model performance was evaluated using Precision, Recall, AP@50, mAP@50:95, and counting error metrics. The experimental results indicate that YOLOv11 provides the most balanced performance across precision, localization accuracy, and counting stability. However, all models exhibit degraded performance in small-object scenarios, dense pest distributions, and visually similar categories. Error analyses further reveal that class imbalance and field-scene variability are the primary factors limiting detection robustness. Overall, this study contributes a high-quality, uniformly annotated rice pest dataset and a systematic benchmark of mainstream detection models under realistic field conditions. The findings highlight critical challenges in fine-grained pest recognition and provide a solid foundation for future research on small-object enhancement, adaptive data augmentation, and robust deployment of intelligent pest monitoring systems. Full article

Rice (Oryza sativa L.) production in the Philippines remains highly vulnerable to recurrent outbreaks of the Rice Black Bug (RBB; Scotinophara coarctata F.) and White Stemborer (WSB; Scirpophaga innotata W.), two of the most destructive pests in Southeast Asian rice ecosystems. Classical economic threshold levels (ETLs) are difficult to estimate in smallholder settings due to the lack of cost–loss data, often leading to either delayed or excessive pesticide application. To address this, the present study developed an adaptive outbreak-forecasting framework that integrates the Number–Size (N–S) fractal model with machine learning (ML) classifiers to define and predict pest regime transitions. Seven years (2018–2024) of light-trap surveillance data from the Philippine Rice Research Institute–Midsayap Experimental Station were combined with daily climate variables from the NASA POWER database, including air temperature, humidity, precipitation, wind, soil moisture, and lunar phase. The N–S fractal model identified natural breakpoints in the log–log cumulative frequency of pest counts, yielding early-warning and severe-outbreak thresholds of 134 and 250 individuals for WSB and 575 and 11,383 individuals for RBB, respectively. Eight ML algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Balanced Bagging, LightGBM, XGBoost, and CatBoost were trained on variance-inflation-filtered climatic and temporal predictors. Among these, CatBoost achieved the highest predictive performance for WSB at the 94.3rd percentile (accuracy = 0.932, F1 = 0.545, ROC–AUC = 0.957), while Logistic Regression performed best for RBB at the 75.1st percentile (F1 = 0.520, ROC–AUC = 0.716). SHAP (SHapley Additive exPlanations) analysis revealed that outbreak probability increases under warm nighttime temperatures, high surface soil moisture, moderate humidity, and calm wind conditions, with lunar phase exerting additional modulation of nocturnal pest activity. The integrated fractal–ML approach thus provides a statistically defensible and ecologically interpretable basis for adaptive pest surveillance. It offers an early-warning system that supports data-driven integrated pest management (IPM), reduces unnecessary pesticide use, and strengthens climate resilience in Philippine rice ecosystems. Full article

Postharvest losses from potato tuber moth severely constrain seed quality in Andean smallholder systems. This study evaluated four locally available repellent plants—Ambrosia peruviana, Eucalyptus globulus, Artemisia absinthium, and Minthostachys mollis—applied as dried leaves layered within seed bags of INIA 302 ‘Amarilis’ under farmer-like storage at two highland sites in Cajamarca, Peru (Huaytorco, 3350 m; Samaday, 2750 m), over 187 days. Within each site, a Completely Randomized Design with three bag-level replicates per treatment was used, and damage was assessed after 187 days as incidence of attacked tubers, internal damage severity and live larval counts. Endpoint data were analyzed separately by site using Kruskal–Wallis tests followed by Dunn’s post hoc test with Šidák correction (α = 0.05). Across both sites, all botanicals significantly reduced damage severity and live larval counts relative to the untreated control. At the warmer, lower site, A. absinthium and M. verticillata achieved large effect sizes, with severity and larval numbers reduced by roughly 80–90% compared with the control, while at the cooler, higher site, larvae were not detected in any botanical treatment. These findings indicate that simple layering of dried leaves from locally available plants, particularly wormwood and muña, can substantially mitigate S. tangolias damage in highland seed potato stores and represents a promising, low-cost complement to integrated pest management, although multi-season and dose-response studies are still needed to confirm and refine this approach. Full article

Tephritid flies employ visual and chemical cues to locate and assess suitable habitats, food sources, mating sites, and ovipositional resources. Bactrocera dorsalis and Zeugodacus tau are economically significant pests that infest a wide range of fruits and fleshy vegetables. Understanding their visual sensitivity is crucial for developing effective and ecologically friendly management strategies. Thus, in this study, we compare the responses of these two species to various visual stimuli across laboratory, greenhouse, and closed-orchard environments. Our experiments reveal that, across the tested physiological states, both species exhibit a preference for certain wavelengths in the laboratory, particularly 520 nm and 560 nm. In the greenhouse, green and yellow models captured significantly more females of both species. Z. tau females showed greater sensitivity to the yellow model than B. dorsalis females. Additionally, Z. tau showed a higher affinity for both spherical and cylindrical shapes, while B. dorsalis flies were only attracted to the spherical model. In a closed orchard area, traps modified according to the best-performing combination used in the laboratory and greenhouse (shape and light) increased the capture counts of both species over time, with Z. tau exhibiting greater visual attraction sensitivity than B. dorsalis. These findings provide a theoretical and scientific foundation for improving trapping techniques targeting these two species. Full article

With the large-scale invasion of alien species, forest ecosystems are facing severe challenges, and the health of trees is increasingly threatened. Accurately detecting and counting trees affected by such invasive species has become a critical issue in forest conservation and resource management. Traditional detection methods usually rely only on the information of a single modality of an image, lack linguistic or semantic guidance, and often can only model a specific diseased tree situation during training, making it difficult to achieve effective differentiation and generalization of multiple diseased tree types, which limits their practicality. To address the above challenges, we propose an end-to-end multimodal diseased tree detection model. In the visual encoder of the model, we introduce rotational positional encoding to enhance the model’s ability to perceive detailed structures of trees in images. This design enables more accurate extraction of features related to diseased trees, especially when processing images with complex environments. At the same time, we further introduce a cross-attention mechanism between image and text modalities, so that the model can realize the deep fusion of visual and verbal information, thus improving the detection accuracy based on understanding and recognizing the semantics of the disease. Additionally, this method possesses strong generalization capabilities, enabling effective recognition based on textual descriptions even when samples are not available. Our model achieves optimal results on the Larch Casebearer dataset and the Pests and Diseases Tree dataset, verifying the effectiveness and generalizability of the method. Full article

Bactrocera dorsalis (Hendel, 1912) is a highly destructive pest affecting fruits and vegetables, making population monitoring essential for farmers to implement timely control measures. In recent years, although automatic monitoring systems for B. dorsalis have been introduced, challenges such as limited accuracy, difficulty in accurately identifying the target pest using infrared interruption sensors alone, and high labor requirements persist. This study presents an automatic monitoring system consisting of intelligent bait equipment (IBE), an advanced detection model based on YOLOv8, and an online monitoring platform. The developed IBE is equipped with cameras, attractant-based lures, and an automatic removal mechanism for B. dorsalis. Field tests demonstrated the IBE exhibited an attractiveness to B. dorsalis comparable to conventional traps, achieved a near-perfect cleaning efficiency (~100%), and maintained a reliable wireless transmission system. The YOLOv8l-based automatic pest detection model outperformed other YOLOv8 variants (n, s, m, x), achieving the highest precision (95.17%), recall (94.15%) and F1 score (94.66%), underscoring its effectiveness in pest detection. Further analysis of the impact of B. dorsalis density on YOLOv8l’s detection performance revealed a decline in accuracy as density increased; however, even at high densities, the model maintained a strong F1 score of 93.36%, demonstrating robustness. Finally, the automatic pest detection model was integrated into ‘YunShanPu’, an online platform for real-time pest monitoring. The proposed method has demonstrated promising performance in the automatic identification and counting of B. dorsalis and has potential for monitoring B. dorsalis populations continuously, providing early warning and forecasting for integrated pest management. Full article

Upland cotton (Gossypium hirsutum L.) is a major global commodity crop whose production is threatened by the reniform nematode (Rotylenchulus reniformis Linford and Oliveira), a plant-parasitic pest that causes substantial yield losses. Host-plant resistance offers a sustainable management strategy, but currently available resistant cotton cultivars provide only partial protection and often require supplemental control methods. In this study, Clustered Regularly Interspaced Palindromic Repeats (CRISPR)–CRISPR-associated 9 (Cas9) gene editing was used to generate targeted knockouts of Mildew Resistance Locus O (GhiMLO3) in cotton and assess its role in resistance to R. reniformis. Four independent knockout lines (A1, D3, E1, and P3) were developed, confirmed by sequencing, and evaluated for nematode resistance under controlled greenhouse conditions. Nematode reproduction was significantly reduced on lines D3 and E1, with lower egg counts and fewer vermiform life stages compared with the control genotypes, Coker 312 (WT), Delta Pearl, and Jin668. The edited lines also showed characteristic mesophyll cell-death phenotypes, suggesting potential pleiotropic effects associated with MLO-mediated resistance. Sequence analysis confirmed multiple homozygous and heterozygous mutations in MLO3 alleles from both the A and D subgenomes, with D3 and E1 lines displaying the strongest resistance profiles. These findings demonstrate that MLO3 gene editing is a promising approach for improving R. reniformis resistance in cotton. Full article

The codling moth Cydia pomonella (L.) represents a substantial threat to the apple tree industry, with its cellular content being agronomically vital as it serves as the final immunological and toxicological barrier of the pest. Key hemocyte types identified in the hemolymph include plasmatocytes, granulocytes, spherulocytes, and oenocytoids. Hemolymph samples were in vitro suspended in various salt buffers (physiological saline, phosphate saline buffer (PBS) and Galleria mellonella anticoagulant buffer) to determine the most suitable one for agricultural monitoring purposes. The pH influenced the total hemocyte counts and the type of cells that adhered to the slides. PBS (pH 6.5) was found to be optimal for such studies due to its high levels of cellular attachment, cell viability, absence of melanization, and cellular degeneration effects. The supplementation of 5% CaCl₂ to PBS did not enhance the functional utility of the buffer. The in vivo bacterial challenge of larval hemolymph with 4 × 10⁸ sp/mL Bacillus subtilis provided complete clearance from the microbial invader within 30 min. Hemocytes released antimicrobial lysozyme as part of their innate immune responses. Hemocytic examination of larvae as an agricultural practice is strongly recommended for baseline insecticide resistance avoidance and predictive efficiency of integrated pest management in the apple farm. Full article

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

Hemp (Cannabis sativa L.) cultivation in the United States has expanded rapidly over the past decade. Due to federal and state regulations, only a limited number of studies have examined the chemical options available for controlling pests on C. sativa. In the U.S., two of the most important species of arthropod pests affecting C. sativa are the beet leafhopper Circulifer tenellus Baker (Hemiptera: Cicadellidae) and the two-spotted spider mite Tetranychus urticae Koch (Acari: Tetranychidae). This study evaluated the effects of four biopesticides, Chromobacterium subtsugae, Burkholderia spp., Chenopodium ambrosioides, and azadirachtin, under greenhouse conditions against C. tenellus adults and nymphs and T. urticae adults. Biopesticides were applied to foliage using a calibrated hand sprayer. To evaluate the biopesticides’ potency, C. tenellus adults, nymphs, and mites were released 1 h after treatment; to evaluate the residual efficacy, they were released 7 days after treatment (DAT). In both experiments, C. tenellus adults, nymphs, and mites were counted 1, 3, and 7 days after release. Our results indicate that Burkholderia spp. exhibited the highest efficacy against C. tenellus adults at 7 DAT, whereas C. ambrosioides and azadirachtin caused the greatest nymphal mortality at 1 and 3 DAT, respectively. Our results show that Burkholderia spp. had the greatest potency against C. tenellus adults 7 DAT, while C. ambrosioides and azadirachtin highly affect the mortality of nymphs at 1 and 3 DAT, respectively. Treatments with C. subtsugae and C. ambrosioides showed high potency against T. urticae. Finally, C. subtsugae showed the lowest residual effect against the mite pest. The data presented in this article will add to the arsenal of information to improve the current management strategies used against these two hemp pests. Full article

Helleborus orientalis L. is a valuable winter-flowering and understory landscape plant, but its application and breeding are hindered by poor heat tolerance and the lack of a robust germplasm evaluation system. In this study, 51 Helleborus L. hybrid individuals obtained through manual open pollination were evaluated using coefficient of variation (CV), Shannon–Weaver diversity index (H′), correlation analysis, principal component analysis (PCA), and cluster analysis to assess genetic diversity and ornamental value based on 17 phenotypic traits. The results showed rich phenotypic diversity among the hybrids. Quantitative traits showed CV ranging from 9.48% to 37.99% and H′ between 0.77 and 1.51, with flower count and leaf length being the most variable. Qualitative traits had H′ values from 0.52 to 1.55, with sepal color showing the highest diversity. Significant correlations were detected among heat tolerance, pest resistance, leaf and petiole length, as well as plant and flower form. PCA extracted six principal components accounting for 74.50% of cumulative variance. Cluster analysis classified the 51 germplasms into five groups. Using the AHP model, a comprehensive evaluation system was established, and 13 elite individuals were selected for variety rights application and characterization. This study provides a reference for establishing DUS test guidelines and advancing breeding and utilization of Helleborus L. Full article

Stomoxys calcitrans (Linnaeus, 1758) is a hematophagous fly species of veterinary importance, known for its negative effects on animal health and productivity. The stress caused by their painful bites results in losses in milk and meat production. Despite its impact, data on its ecology and activity in Algeria are lacking. Such knowledge is needed to evaluate its potential effects on livestock production and rural health, and to support surveillance, outbreak prediction, and control strategies. This study aimed to investigate the monthly and daily dynamics of S. calcitrans in livestock farms in the Batna region and evaluate the influence of climatic factors on its abundance. From July 2022 to July 2023, Vavoua traps were placed monthly from 7 a.m. to 6 p.m. on four farms in the Batna region, representing different livestock types. Captured flies were identified, sexed, and counted every two hours. Climatic data were collected both in situ and from NASA POWER datasets. Fly abundance was analyzed using non-parametric statistics, Spearman’s correlation, and multiple regression analysis. A total of 1244 S. calcitrans were captured, mainly from cattle farms. Activity occurred from August to December, with a peak in September. Males were more abundant and exhibited a bimodal activity in September. Fly abundance was positively correlated with temperature and precipitation and negatively correlated with wind speed and humidity. This study presents the first ecological data on S. calcitrans in northeastern Algeria, highlighting its seasonal dynamics and the climatic drivers that influence it. The results highlight the species’ preference for cattle and indicate that temperature and rainfall are key factors influencing its abundance. These findings lay the groundwork for targeted control strategies against this neglected pest in Algeria. Full article

Real-time insect monitoring is essential for sustainable agriculture and biodiversity conservation. The traditional method of attracting insects to colored glue traps and manually counting the catch is time-intensive and requires specialized taxonomic expertise. Moreover, these traps are often lethal to pests and beneficial insects alike, raising both ecological and ethical concerns. Camera-based trap designs have recently emerged to lower the amount of manual labor involved in determining insect species, yet they are still deadly to the catch. This study presents the design and evaluation of a non-lethal acoustic monitoring system capable of detecting and classifying insect species based on their sound signatures. A first prototype was developed with a focus on low self-noise and suitability for autonomous field deployment. The system was initially validated through laboratory experiments, and subsequently tested in six rapeseed fields over a 25-day period. More than 3400 h of acoustic data were successfully collected without system failures. Key findings highlight the importance of carefully selecting each component to minimize self-noise, as insect sounds are extremely low in amplitude. The results also underscore the need for efficient data and energy management strategies in long-term field deployments. This paper aims to share the development process, design decisions, technical challenges, and practical lessons learned over the course of building our IoT sensor system. By outlining what worked, what did not, and what should be improved, this work contributes to the advancement of non-invasive insect monitoring technologies. Full article

Spodoptera frugiperda is a major transboundary migratory pest under global alert by the Food and Agriculture Organization (FAO) of the United Nations. The accurate identification and counting of trapped adults in the field are key technologies for achieving quantitative monitoring and precision pest control. However, precise recognition is challenged by issues such as scale loss and the presence of mixed insect species in trapping images. To address this, we constructed a field image dataset of trapped Spodoptera frugiperda adults and proposed an improved YOLOv5s-based detection method. The dataset was collected over a two-year sex pheromone monitoring campaign in eastern–central Yunnan, China, comprising 9550 labeled insects across six categories, and was split into training, validation, and test sets in an 8:1:1 ratio. In this study, YOLOv7, YOLOv8, Mask R-CNN, and DETR were selected as comparative baselines to evaluate the recognition of images containing Spodoptera frugiperda adults and other insect species. However, the complex backgrounds introduced by field trap photography adversely affected classification performance, resulting in a relatively modest average accuracy. Considering the additional requirement for model lightweighting, we further enhanced the YOLOv5s architecture by integrating Mosaic data augmentation and an adaptive anchor box strategy. Additionally, three attention mechanisms—SENet, CBAM, and Coordinate Attention (CA)—were embedded into the backbone to build a multidimensional attention comparison framework, demonstrating CBAM’s superiority under complex backgrounds. Ultimately, the CBAM-YOLOv5 model achieved 97.8% mAP@0.5 for Spodoptera frugiperda identification, with recognition accuracy for other insect species no less than 72.4%. Based on the optimized model, we developed an intelligent recognition system capable of image acquisition, identification, and counting, offering a high-precision algorithmic solution for smart trapping devices. Full article