Search Results (115)

Stink bugs are widespread agricultural pests that damage crops and reduce yield. Their impact is influenced by host plant selection and interactions with natural enemies, particularly egg parasitoids. Understanding these relationships is crucial for improving biological control strategies. This paper investigates the seasonal host plant use and parasitism of Halyomorpha halys, Palomena prasina, and Pentatoma rufipes in South Tyrol, Italy. Over two years, we conducted field surveys at 27 sites, recording stink bug presence across 85 plant species and analyzing egg parasitism rates. Results show that stink bugs exhibit distinct host plant preferences, with H. halys utilizing the broadest range of host plants while P. prasina and P. rufipes showed stronger affinities for specific families such as Sapindaceae and Rosaceae. Parasitism rates varied across species and plant families: Trissolcus japonicus predominantly parasitized H. halys while T. cultratus and two Telenomus species targeted P. rufipes and P. prasina, respectively. Spatial–temporal features and host plant associations significantly influenced species distributions and parasitoid occurrence. These findings emphasize the role of plant–insect interactions in shaping pest and parasitoid dynamics. Integrating plant diversity into pest management strategies could enhance parasitoid effectiveness and reduce stink bug populations, contributing to more sustainable agricultural practices. Full article

We studied the characteristics of adult flat grain beetles Cryptolestes pusillus Schönherr in trap counts and their relationship with insect densities using electronic probe traps in experimental bins, which contained approximately 1.1 tons of paddy (Changlixiang) with moisture contents of 10.7% and 14.0% and insect densities of 0.1, 1.0, and 5.0 adults/kg. Inside each bin, we vertically installed three layers of electronic probe traps. We installed five traps at the center and half-radius of each layer of the bin. We undertook measurements of daily trap counts, grain temperature, and intergranular relative humidity at each trap location for up to 10 days, and 1.0 kg of paddy was collected from each trap location. At each of the introduced insect densities, we detected beetles using electronic probe traps. When insect density was 0.1 adults/kg, we could not detect the existence of insects in 1.0 kg samples. It was found that the trap counts were spatially nonuniformly distributed, and there was a weak correlation between different locations. There was a regularity in the temporal distribution of trap counts, which was significantly influenced by the paddy moisture content. Except for the insect density, the moisture content, grain temperature, and intergranular relative humidity at the trap location significantly affected trap counts. The distribution pattern of beetles in paddy bulks was found and aggregated by analyzing trap counts. There was a correlation between trap counts and insect densities in grain bulks, but this correlation varied significantly across different locations. Full article

Agricultural pest detection is critical for crop protection and food security, yet existing methods suffer from low computational efficiency and poor generalization due to imbalanced data distribution, minimal inter-class variations among pest categories, and significant intra-class differences. To address the high computational complexity and inadequate feature representation in traditional convolutional networks, this study proposes MA-YOLO, an agricultural pest detection model based on multi-scale fusion and attention mechanisms. The SDConv module reduces computational costs through depthwise separable convolution and dynamic group convolution while enhancing local feature extraction. The LDSPF module captures multi-scale information via parallel dilated convolutions with spatial attention mechanisms and dual residual connections. The ASCC module improves feature discriminability by establishing an adaptive triple-weight system for global, channel, and spatial semantic responses. The MDF module balances efficiency and multi-scale feature extraction using multi-branch depthwise separable convolution and soft attention-based dynamic weighting. Experimental results demonstrate detection accuracies of 65.4% and 73.9% on the IP102 and Pest24 datasets, respectively, representing improvements of 2% and 1.8% over the original YOLOv11s network. These results establish MA-YOLO as an effective solution for automated agricultural pest monitoring with applications in precision agriculture and crop protection systems. Full article

Bactrocera oleae (Rossi) (Diptera: Tephritidae) is a key pest of olive groves. Adult monitoring is carried out by means of attractant traps of different shapes, which give relevant information for pest control such as the presence of adult flies in the field and their trend, female maturity and sex ratio. However, it is still not entirely clear whether a given density is sufficient for providing a reliable representation of flies in an olive grove. To investigate this question, an experiment was planned, consisting of arranging a high-density network of unbaited sticky panels (UTs) between panels baited with ammonium carbonate (BTs) deployed at a density of 2 traps/ha. The experiment was carried out in Greece, Italy, Lebanon, Spain and Tunisia. The percentage of BT over UT catches varied significantly among the different countries, with BTs ranging from 82% of catches in Italy to 27% in Greece. The Pearson correlation between BTs and UTs was significant under high captures but not significant at low densities. The index of aggregation showed an inverse relationship with baited catches. The distributions of males and females were nearly always positively spatially associated. According to the field data, BTs at the density of 2/ha provide a realistic estimate of the population in the field in the cases of established populations. However, in the periods without population establishment, a denser monitoring trap network is likely required to obtain a reliable estimation of the field population. Full article

This study employs an accounting approach to quantitatively assess Chengdu’s ecological value, focusing on agriculture, forestry, animal husbandry, fisheries, climate regulation, water conservation, water quality purification, and air quality improvement. The value of each indicator is calculated and visualized using ArcGIS 10.8, with predictions made for four future time intervals. The analysis reveals the spatial distribution patterns of ecological value across Chengdu. The results indicate the following: (1) From 2015 to 2019, Chengdu’s ecological value indicators demonstrated a positive growth trend, with notable increases in recreation services (CNY 178.5 billion), agriculture, forestry, animal husbandry, and fisheries (CNY 32.88 billion), and water conservation (CNY 9.26 billion). Values exhibited a general decrease from the city center outward. (2) Water quality purification, air quality improvement, and pest control values exhibited slight declines in 2015, 2017, and 2019 compared to 2015. (3) Ecological values demonstrate spatial diversity, with lower values in central areas for categories such as soil conservation and a “high-low-high” pattern for water conservation. Recreation services exhibit a “high in the center, low around the edges” pattern. (4) The gray prediction model forecasts that by 2027, the values for agriculture, forestry, animal husbandry and fisheries, water conservation, and soil conservation will double relative to 2015. Climate regulation and air quality improvement values are predicted to triple, while water quality purification exhibits minimal change. Pest control is expected to decline to 67% of its 2015 value, while the value of recreation services will increase to 12 times its 2015 value. The results of this study reveal the evolutionary characteristics of the ecological value volume index in Chengdu, fill a gap in the field of ecological value volume measurement and prediction in the region, and provide scientific support for understanding the evolutionary trajectory of Chengdu’s ecological environment. Full article

Anoplophora horsfieldii Hope, a potential pest of the Cerambycidae family, is widely distributed throughout China, where it can cause damage to various living tree species. It has emerged as a critical invasive organism threatening China’s agricultural and forestry production as well as ecological security. This study comprehensively analyzed the key environmental factors influencing the geographical distribution of A. horsfieldii and its spatiotemporal dynamics by integrating multi-source environmental data and employing ecological niche modeling. Model validation demonstrated high reliability and accuracy of our predictions, with an area under the receiver operating characteristic curve (AUC) value of 0.933, Kappa coefficient of 0.704, and true skill statistic (TSS) reaching 0.960. Our analysis identified four dominant environmental factors governing the distribution of A. horsfieldii: mean diurnal range (Bio2), temperature annual range (Bio7), precipitation of driest quarter (Bio17), and precipitation of coldest quarter (Bio19). Under current climatic conditions, the total potential suitable distribution area for A. horsfieldii was estimated at 212.394 × 10⁴ km², primarily located in central, southern, eastern, southwestern, and northwestern China. Future projections under three climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) suggest significant reductions in highly and moderately suitable habitats, while low-suitability areas may expand into central, eastern, and southwestern regions, with Chongqing, Henan, and Anhui potentially becoming new suitable habitats. Concurrently, the centroid coordinates of suitable habitats exhibited a directional shift toward Guangdong Province, with the overall distribution pattern demonstrating a spatial transition characterized by movement from inland to coastal areas and from higher to lower latitudes. This study provides scientific theoretical support for forestry authorities in controlling the spread of A. horsfieldii, while establishing a solid foundation for future ecological conservation and biosecurity strategies. The findings offer both theoretical insights and practical guidance for pest management and ecosystem protection. Full article

To identify the species of Collembola that harm Morchella and to screen for pesticides that are effective in controlling these pests with minimal inhibition of mycelial growth, a five-point sampling method was used to investigate the population of Collembola and its damaging effects on Morchella and to analyze its spatial distribution in the soil. The indoor control efficacy of ten insecticides was determined using the mushroom disc immersion method and the pesticide film method. The most effective insecticides were then selected for field testing. The effect of the best-performing field pesticides on the mycelial growth of Morchella was measured using the Petri dish mycelial growth rate method, and pesticide residues were detected using chromatography. The survey revealed that in three Morchella greenhouses, the average Collembola population was 220,333 individuals/m³. The spatial distribution of Collembola was uniform. The collected Collembola specimens were identified as Oligaphorura ursi from the family Onychiuridae. Through the lab and field screening of pesticides, it was found that 40% phoxim EC, 1.8% abamectin EC, 2.5% lambda-cyhalothrin EW, and 4.5% beta-cypermethrin EC had the best efficacy. Meanwhile, residues of these four pesticides were not detected. Mycelial growth inhibition experiments showed that 2.5% lambda-cyhalothrin EW, 1.8% abamectin EC, and 4.5% beta-cypermethrin EC exhibit low inhibition of mycelial growth and can be used as control pesticides for Collembola on Morchella, providing a technical reference for the green pesticide control of Collembola on Morchella in the study region. Full article

This paper presents a simulation study investigating the possible dispersal of the red palm weevil, a highly destructive pest affecting various palm species, across the island of Corfu, Greece. The simulation incorporates ecological modeling and geographical data to analyze the dynamics and the spread of red palm weevil populations over time and space. Key findings indicate that factors such as tree density and spatial distribution significantly influence infestation rates, with densely populated areas being more susceptible to rapid spreading. The study underscores the importance of early detection and targeted interventions to control red palm weevil populations and to mitigate their impact on affected regions. This research contributes to the development of effective pest management strategies that could potentially be adapted to address similar invasive species challenges in other agricultural contexts. Full article

The citrus industry plays a pivotal role in modern agriculture. With the expansion of citrus plantations, the intelligent detection and prevention of diseases and pests have become essential for advancing smart agriculture. Traditional citrus leaf disease identification methods primarily rely on manual observation, which is often time-consuming, labor-intensive, and prone to inaccuracies due to inherent asymmetries in disease manifestations. This work introduces CBACA-YOLOv5, an enhanced YOLOv5s-based detection algorithm designed to effectively capture the symmetric and asymmetric features of common citrus leaf diseases. Specifically, the model integrates the convolutional block attention module (CBAM), which symmetrically enhances feature extraction across spatial and channel dimensions, significantly improving the detection of small and occluded targets. Additionally, we incorporate coordinate attention (CA) mechanisms into the YOLOv5s C3 module, explicitly addressing asymmetrical spatial distributions of disease features. The CARAFE upsampling module further optimizes feature fusion symmetry, enhancing the extraction efficiency and accelerating the network convergence. Experimental findings demonstrate that CBACA-YOLOv5 achieves an accuracy of 96.1% and a mean average precision (mAP) of 92.1%, and improvements of 0.6% and 2.3%, respectively, over the baseline model. The proposed CBACA-YOLOv5 model exhibits considerable robustness and reliability in detecting citrus leaf diseases under diverse and asymmetrical field conditions, thus holding substantial promise for practical integration into intelligent agricultural systems. Full article

Pest infestations remain a critical threat to global agriculture, significantly compromising crop yield and quality. While accurate pest detection forms the foundation of precision pest management, current approaches face two primary challenges: (1) the scarcity of comprehensive multi-scale, multi-category pest datasets and (2) performance limitations in detection models caused by substantial target scale variations and high inter-class morphological similarity. To address these issues, we present three key contributions: First, we introduce Insect25—a novel agricultural pest detection dataset containing 25 distinct pest categories, comprising 18,349 high-resolution images. This dataset specifically addresses scale diversity through multi-resolution acquisition protocols, significantly enriching feature distribution for robust model training. Second, we propose GC-Faster RCNN, an enhanced detection framework integrating a hybrid attention mechanism that synergistically combines channel-wise correlations and spatial dependencies. This dual attention design enables more discriminative feature extraction, which is particularly effective for distinguishing morphologically similar pest species. Third, we implement an optimized training strategy featuring a cosine annealing scheduler with linear warm-up, accelerating model convergence while maintaining training stability. Experiments have shown that compared with the original Faster RCNN model, GC-Faster RCNN has improved the average accuracy mAP0.5 on the Insect25 dataset by 4.5 percentage points, and mAP0.75 by 20.4 percentage points, mAP0.5:0.95 increased by 20.8 percentage points, and the recall rate increased by 16.6 percentage points. In addition, experiments have also shown that the GC-Faster RCNN detection method can reduce interference from multiple scales and high similarity between categories, improving detection performance. Full article

Chrysodeixis includens (Walker, 1858) and Rachiplusia nu (Guenée, 1852) are important defoliating pests belonging to the Plusiinae subfamily in the Western hemisphere. C. includens is a major lepidopteran pest of soybean in the Americas, whereas, until 2021, R. nu was more restricted to the temperate regions of South America. Recently, reports of R. nu feeding on Cry1Ac soybean and occurring in tropical regions of Brazil have raised questions regarding the distribution of this species. The morphological similarity of the larvae from the two species makes it difficult to correctly identify the species in the field, which may lead to an underestimation of R. nu in Brazilian territory. This study aimed to address these questions by using a molecular approach to identify Plusiinae caterpillars throughout three seasons in non-Bt, Cry1Ac, and Cry1Ac × Cry1F soybean fields. Here, we carried out a comprehensive spatial sampling of the primary soybean-producing regions in Brazil. The results showed that R. nu has been the main Plusiinae occurring in soybean over the last three years, and it was present in all sampled regions. For Cry1Ac and Cry1Ac × Cry1F soybeans, up to 99% of the samples collected in 2023/24 were identified as R. nu. Non-Bt soybeans had higher variations in the proportion of the two species among the regions and across seasons, indicating that populations of C. includens and R. nu are co-occurring throughout the country. This is, to our knowledge, the most robust report assessing the distribution of C. includens and R. nu in Brazil using a molecular tool. This study provides clarification of R. nu occurrence and highlights the importance of pest monitoring from an integrated pest management perspective. Full article

Invasive species pose a growing threat to global biodiversity, agricultural productivity, and ecosystem health, as climate change worsens their spread. This study focused on modelling the current and projected distribution of the Japanese beetle (Popillia japonica Newman), an invasive pest with potentially devastating impacts on crops and natural vegetation across Europe. Using the MaxEnt species distribution model, we integrated beetle occurrence data with bioclimatic variables, analyzing current and future climate scenarios based on Shared Socio-economic Pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5) for near-term (2021–2040) and mid-term (2041–2060) periods. By reclassifying the model results, we identified European regions with negligible, low, medium, and high exposure to this invasive pest under climate change pathways. The results identified regions in central Europe covering an area of 83,807 km² that are currently at medium to high risk of Japanese beetle infestation. Future projections suggest northward expansion with suitable areas potentially increasing to 120,436 km² in the worst-case scenario, particularly in northern Italy, southern Germany, the Western Balkans, and parts of France. These spatially explicit findings can inform targeted monitoring, early detection, and management strategies to mitigate the economic and ecological threats posed by the Japanese beetle. Integrating species distribution modelling with climate change scenarios is imperative for science-based policies to tackle the growing challenge of biological invasions. This research provides a framework for assessing invasion risks at the European scale and guiding adaptive responses in agricultural and natural systems. Full article

Cotton aphids are the primary pests that adversely affect cotton growth, and they also transmit a variety of viral diseases, seriously threatening cotton yield and quality. Although the traditional remote sensing method with a single data source improves the monitoring efficiency to a certain extent, it has limitations with regard to reflecting the complex distribution characteristics of aphid pests and accurate identification. Accordingly, there is a pressing need for efficient and high-precision UAV remote sensing technology for effective identification and localization. To address the above problems, this study began by presenting a fusion of two kinds of images, namely panchromatic and multispectral images, using Gram–Schmidt image fusion technique to extract multiple vegetation indices and analyze their correlation with aphid damage indices. After fusing the panchromatic and multispectral images, the correlation between vegetation indices and aphid infestation degree was significantly improved, which could more accurately reflect the spatial distribution characteristics of aphid infestation. Subsequently, these machine learning techniques were applied for modeling and evaluation of the performance of multispectral and fused image data. The results of the validation revealed that the GBDT (Gradient-Boosting Decision Tree) model for GLI, RVI, DVI, and SAVI vegetation indices based on the fused data performed the best, with an estimation accuracy of R² of 0.88 and an RMSE of 0.0918, which was obviously better than that of the other five models, and that the monitoring method of combining fusion of panchromatic and multispectral imagery with the accuracy and efficiency of the GBDT model were noticeably higher than those of single multispectral imaging. The fused panchromatic and multispectral images combined with the GBDT model significantly outperformed the single multispectral image in terms of precision and efficiency. In conclusion, this study demonstrated the effectiveness of image fusion combined with GBDT modeling in cotton aphid pest monitoring. Full article

The peach fruit fly (Bactrocera zonata), a significant polyphagous pest, poses a considerable threat to fruit crops across its expanding range. Although climate change significantly impacts pest populations, its effects on B. zonata remain understudied. This research examined B. zonata population dynamics across two distinct Egyptian ecological zones (Sohag and Ismailia Governorates) from 2013–2023 using pheromone traps and climate data. Results revealed significant spatial and temporal variations in abundance patterns. Both regions displayed a unimodal distribution, with Sohag exhibiting a distinct peak during September to November, whereas Ismailia showed a broader peak period spanning from August to December. Temperature significantly influenced population levels while precipitation showed no significant effect. Similarly, our results indicated increasing population trends in both regions despite no significant long-term temperature changes. These findings suggest that factors beyond temperature alone, such as host fruit availability, regional environmental variations, and potentially evolving agricultural practices, drive B. zonata population growth, highlighting the need for comprehensive, climate-responsive pest management strategies that account for regional variations. Full article

Cotton aphids, Aphis gossypii, are an important pest worldwide and have evolved mutualistic relationships with the invasive fire ant Solenopsis invicta. Their body color varies from pale yellow to dark green, with an increase in body size and fecundity. The body color composition in a cotton aphid colony can be influenced by biotic interactions with mutualistic ants and predatory ladybugs. However, since the distribution of nutrients varies across host plant organs, there may exist special effects of biotic interactions on the body color composition of the aphids on different plant parts. In the present study, we found that, under constant laboratory conditions, the proportions of dark green morphs varied among the cotton aphids distributed on different parts of a cotton seedling, with significantly higher proportions on the stems, petioles, and sprouts (SPSs) than on leaves. The presence of mutualistic fire ants significantly increased the proportion of dark green morphs in the cotton aphid colony, but with a reduction in aphid body size, compared to the untended individuals. In contrast, the introduction of a predatory seven-spotted ladybug, Coccinella septempunctata, dramatically decreased the proportion of dark green morphs on SPSs, but not on leaves, leading to a reduction in the proportion of the whole colony. These results illustrate a spatial variation in the proportions of dark green morphs on host plants in cotton aphids, which may be an adaptive strategy used by the aphids to gain benefits and/or minimize costs in the interactions with mutualistic ants and predatory ladybugs. Full article