Search Results (1,446)

The clover cutworm, Anarta trifolii (Lepidoptera: Noctuidae), constitutes a polyphagous pest known for causing sporadic, local outbreaks that significantly damage Beta vulgaris, Gossypium hirsutum, Brassica oleracea and others. Evidence supports the occurrence of seasonal migration in this species, but the determinants of A. trifolii flight performance remain unexplored. Understanding the species’ flight performance is essential for predicting its long-distance dispersal, identifying source and sink populations, and improving regional pest forecasting. We characterized flight performance and its influencing factors via computer-monitored flight mills. Maximum flight performance was achieved in A. trifolii adults at two days, followed by a significant decline with increasing age. At 24 °C and 80% relative humidity (RH), in a 12 h test, males and females aged two days achieved total flight distances of 38.90 ± 1.21 km and 31.70 ± 1.56 km, respectively. In a 24 h test, three-day-old adults reached a maximum flight speed of 19.68 km/h, a sustained flight duration of 17.38 h, a total flight duration of 23.89 h, a sustained flight distance of 69.64 km, and a total flight distance of 96.56 km. The flight performance of A. trifolii was significantly affected by both temperature and RH, with the maximum flight capacity achieved at 18–28 °C and 35–80% RH. Flight performance was significantly enhanced when A. trifolii were fed honey or sucrose. Moreover, the wingbeat frequency of A. trifolii adults varied among age groups, ranging from 31.90 to 57.65 Hz. In females, the wingbeat frequency peaked at 2 days old (46.72 ± 0.25 Hz), whereas in males it peaked at 10 days old (47.18 ± 0.66 Hz). These results advance the fundamental understanding of A. trifolii migration and offer practical applications, including improved pest management strategies, optimized use of chemical insecticides and biological control agents, and enhanced decision-making in integrated pest management programs. Full article

In Greece, the protection of olive orchards against the olive fruit fly (Bactrocera oleae), the most serious pest of olive fruits, is implemented through a national control program. This program is implemented by the Ministry of Rural Development and Food in co-operation with various public and private organizations. A new approach followed for this goal is the use of insecticide spray solutions combined with trophic attractant to attract and kill the olive fruit fly. In the present study, a method for the determination of the major insecticides, lambda-cyhalothrin and Spinosad, in their spraying solutions in combinations with trophic attractants was developed and validated and the monitoring of their residual concentration under various temperature conditions was examined. The reliability of the analytical method was achieved by obtaining acceptable results regarding the core criteria of specificity, linearity (R² ≥ 0.99), accuracy (recoveries ranged from 91.01% to 116.29%), and precision (RSDs ranged from 0.47% to 3%). Furthermore, no significant effect on the stability of lambda-cyhalothrin and spinosad was noted from the various attractants that were added. As it was observed, in all cases the concentration of the insecticide remained stable. On the other hand, the effect of temperature as well as pH seems to be significant, with the degradation rates at 30 °C clearly higher in all cases than those at 20 °C. Therefore, preliminary data have been provided on recording the duration that the formulation remains stable and effective in spray solutions. Full article

The Soybean aphid (Aphis glycines) is a destructive pest that threatens soybeans. In order to develop green and effective control strategies, we propose an EQPAL epidemic model that integrates four developmental stages (1st–2nd stage nymphs, 3rd stage nymphs, 4th stage nymphs, and adults) and a ladybug (Harmonia axyridis) compartment. This model achieves green pest control by artificially releasing a natural enemy of soybean aphids to prey on adult soybean aphids. We analyzed the dynamic behavior of the model and derived the basic reproduction number ${\mathcal{R}}_0$. Using field monitoring data from Changchun City, Jilin Province, China in 2025, the segmented nonlinear least squares method was used for parameter estimation and fitting, resulting in an overall determination coefficient of $R^2=0.8204$. The numerical simulation results showed that the release of ladybugs significantly reduced the density and peak value of soybean aphid adults, and the predation rate $\beta$, predation conversion rate c, and ladybug migration rate $\omega$ were identified as key regulatory parameters. In addition, a cost–benefit analysis was conducted to determine the most cost-effective control measures. Full article

Pine wilt disease (PWD) is a devastating biological forest disturbance, making its large-scale and high-precision remote sensing monitoring crucial for epidemic prevention and control. However, the performance of existing deep learning methods in high-resolution imagery is often limited by the confusion of spectral features among disparate ground objects and the complexity of forest boundaries. To address these challenges, this study proposes an innovative, end-to-end deep learning architecture termed MBA-Former. Built upon the robust Swin Transformer V2 backbone, the model systematically integrates two highly adaptable functional modules: (1) a front-end intelligent fusion module designed to adaptively fuse heterogeneous features, and (2) a back-end boundary refinement module that refines segmentation contours via dual-task learning. To train and evaluate the model, fine-grained manual annotations were first performed on Gaofen-2 satellite imagery acquired from multiple typical epidemic areas across northern and southern China. Information-enhanced datasets were constructed by fusing the original spectral bands, typical vegetation indices, and texture features. A comprehensive performance evaluation was then conducted, specifically targeting typical challenging scenarios characterized by complex ground object boundaries. The experimental results demonstrate that the Multi-modal Boundary-Aware Transformer (MBA-Former) significantly outperforms current state-of-the-art models. It achieved a mean Intersection over Union (mIoU) of 81.74%, an IoU of 77.58% for the most critical infected tree category, and a Boundary F1-Score of 78.62%. Compared to the best-performing baseline model, Swin-Unet, these three metrics exhibited notable improvements of 2.88%, 3.55%, and 4.46%, respectively. These findings convincingly demonstrate that MBA-Former provides a highly accurate and robust solution for the large-scale, automated remote sensing monitoring of forest diseases, offering immense value in preventing significant economic losses and preserving forest ecosystem integrity. Full article

As food security gains increasing attention, automated pest monitoring is crucial for agricultural early warning systems. However, in practical light-trap capturing sensors, the extremely small scale of pests and complex background interference, such as unexpected reflection and occlusions, severely undermine the performance of existing models, resulting in frequent missed and false detections. To deal with these challenges, this study proposes EP-YOLO, an enhanced lightweight detection architecture based on YOLOv8n. Specifically, to retain the spatial pixels of micro-targets during downsampling and isolate pest features while eliminating background noise without compromising channel information, the Spatial-to-Depth Convolution (SPD) module and the Efficient Multi-Scale Attention (EMA) module are introduced. We evaluate our model through experiments on Pest24, a dataset consisting of 24 tiny pest categories. The results demonstrate that EP-YOLO achieves a mAP@50 and mAP@50:95 of 70.5% and 47.3%, respectively, improving upon the baseline by 1.1% and 1.9%. Furthermore, EP-YOLO achieves a significant improvement in detecting certain extremely small pests. For example, Rice planthopper and Plutella xylostella show improvements of 8.4% and 3.1%, respectively, compared to the baseline. In conclusion, the physical limitations of detecting tiny pests are successfully overcome by EP-YOLO, providing a robust and deployable design for real-time agricultural monitoring systems. Full article

Plant volatiles are critical mediators of insect–plant interactions, guiding natural enemies to specific habitats and prey. The flower bug, Orius maxidentex Ghauri (Hemiptera: Anthocoridae), is a generalist predator that exhibits a specialized ecological association with the weed Celosia argentea L. (Caryophyllales: Amaranthaceae), utilizing the plant as a primary floral niche in Hainan Island. In this study, the attractiveness of C. argentea floral volatiles to O. maxidentex was confirmed using a Y-tube olfactometer. Solid-phase microextraction (SPME) combined with gas chromatography–mass spectrometry (GC-MS) was utilized to identify six compounds in the floral volatiles: 1,3-diethenylbenzene, trans-cinnamaldehyde, β-bisabolene, methyl salicylate, 3-ethylbenzaldehyde, and nonanal. Electroantennogram (EAG) assays revealed that O. maxidentex antennae showed significant physiological responses to these compounds, and the EAG relative values were positively correlated with concentration gradients. Furthermore, O. maxidentex exhibited significant orientation responses to 1,3-diethenylbenzene, trans-cinnamaldehyde, β-bisabolene, and methyl salicylate, whereas no behavioral response was observed for 3-ethylbenzaldehyde or nonanal. Further tests revealed that β-bisabolene elicited the highest attractiveness, comparable to a synthetic blend formulated to mimic the natural release ratio of the active semiochemicals. These findings reveal the hidden chemical cues mediating the interaction between a predator and its preferred habitat. Understanding this mechanism not only helps explain insect adaptation but also offers new strategies for using these plant volatiles to influence the behavior of this specific predator, potentially enhancing its targeted recruitment in agroecosystems. Full article

As a core resource of traditional Chinese medicine (TCM), medicinal plants are conventionally monitored and assessed using high-cost, low-efficiency methods. Remote sensing offers an efficient technical alternative for large-scale and dynamic evaluation. This study systematically reviewed the literature from 2005 to 2025, summarized remote sensing platforms, sensors, and data analytical methods, and specifically analyzed their applications in medicinal plant resource investigation, planting monitoring, stress monitoring, and TCM quality assessment. These studies mainly focus on resource surveys and quality analysis, targeting root and rhizome herbs. Integrated satellite-, UAV-, and ground-based remote sensing enables distribution mapping, growth retrieval, stress monitoring, and non-destructive quality evaluation in medicinal plants, achieving overall accuracies ranging from 80% to 100%. Currently, remote sensing applications in medicinal plants are evolving toward space–air–ground integration, multi-source data fusion, artificial intelligence empowerment, and multi-omics integration. However, they are constrained by complex wild habitats, difficulties in monitoring root herbs, spectral confusion, and limited model generalization. Future efforts should focus on establishing an integrated monitoring network, developing full-chain quality inversion models for geo-authentic herbs, building climate-adaptive cultivation systems, creating early pest–disease warning technologies, and deepening the integration of remote sensing and multi-omics to support the sustainable utilization and high-quality development of medicinal plant resources. Full article

The rice–wheat rotation is a predominant cropping pattern in China, frequently challenged by pests such as aphids in wheat, and Chilo suppressalis and Cnaphalocrocis medinalis in rice. This study investigates the potential of two common landscape plants, Photinia × fraseri and Pittosporum tobira, as functional plants for conserving natural enemies across crop cycles. Arthropod communities were systematically monitored using Malaise traps during the wheat, wheat–rice transition, and rice seasons from 2023 to 2024. Results revealed that both species successfully conserved a diverse natural enemy community, though their structural differentiation was strongly driven by seasonal variation, as confirmed by Heatmap and principal component analysis (PCA) (P. × fraseri: PC1 = 46.3%, PC2 = 23%; P. tobira: PC1 = 40.2%, PC2 = 25%). During the wheat season, both plants synergistically supported rich functional guilds, including predatory guilds (e.g., Episyrphus balteatus, Gnathonarium dentatum, and Harmonia axyridis) and parasitic guilds (e.g., Microplitis tuberculifer and Cotesia spp.). Notably, during the critical wheat-to-rice transition, these shrubs functioned as “habitat anchors,” where P. × fraseri demonstrated superior retention capacity for functional groups like Aphidius gifuensis, mitigating post-harvest habitat fragmentation. During the rice season, distinct functional complementarity emerged: P. × fraseri appeared to function as a habitat-type plant, potentially providing stable shelter for predatory groups (e.g., spiders and lady beetles), while P. tobira appeared to act as a resource-type plant, potentially attracting a significant rebound of parasitoids (e.g., Xanthopimpla flavolineata) in August. This mid-summer rebound on P. tobira was primarily attributed to its dense evergreen foliage providing a microclimatic refuge, rather than an active flowering resource. Analysis of shared dominant taxa (H. axyridis, Cotesia spp., and E. balteatus) showed highly significant seasonal fluctuations, with peak conservation during the wheat season. This study confirms that P. × fraseri and P. tobira have cross-cycle potential as a “natural enemy bank” in rice–wheat rotation agricultural systems. Their synergistic effects—integrating stable structural shelter with seasonal nutritional subsidies—support the conservation of diverse natural enemy communities throughout the annual crop cycle and significantly enhance the sustained pest control capacity of farmland ecosystems, identifying them as exemplary functional plants for ecological engineering in rice–wheat landscapes and providing a foundation for future studies on biological control efficacy. Full article

Aimed at the practical challenge that pest occurrence in fruit and vegetable horticultural production exhibits strong environmental dependency, pronounced stage characteristics, and high sensitivity to control decision-making, a multimodal pest recognition and occurrence risk joint modeling method is proposed to address the limitation that conventional intelligent plant protection systems focus primarily on pest identification while lacking risk discrimination capability. Within a unified network framework, pest visual information and environmental temporal data are integrated through the construction of an environment-guided representation learning mechanism, a recognition–risk joint optimization strategy, and a risk-aware decision representation modeling structure. In this manner, pest category recognition and occurrence risk evaluation are conducted simultaneously, thereby providing direct decision support for precision prevention and control in fruit and vegetable production. Systematic experimental evaluation is conducted based on multi-crop and multi-year field data collected from Wuyuan County, Bayannur City, Inner Mongolia. Overall comparative results demonstrate that an identification accuracy of $0.947$, a precision of $0.936$, and a recall of $0.924$ are achieved on the test set, all of which significantly outperform mainstream visual detection models such as YOLOv8, DETR, and Mask R-CNN. In terms of detection performance, mAP@50 and mAP@75 reach $0.962$ and $0.821$, respectively, indicating stable localization and discrimination capability under complex backgrounds and dense small-target conditions. For the occurrence risk discrimination task, a risk accuracy of $0.887$ is obtained, representing an improvement of approximately $4.5$ percentage points compared with the simple multimodal feature concatenation method. Cross-crop, cross-site, and cross-year generalization experiments further show that risk accuracy remains above $0.84$ with stable recognition performance under significant distribution shifts. Ablation studies verify the synergistic contributions of the proposed core modules to overall performance improvement. The results indicate that the proposed framework enables the transition from single recognition to risk-driven plant protection decision-making, providing a technically viable pathway for pest diagnosis and control strategy optimization in fruit and vegetable horticulture. Full article

Growing interest in environmentally compatible stored-product pest control has highlighted diatomaceous earth (DE) and 1,8-cineole as promising agents, both alone and in combination. Their different modes of action, together with the limitations associated with higher-dose single applications, support evaluating their combined use at lower doses. This study was conducted to compare the effects of DE and 1,8-cineole, applied alone and in combination, on the larval, pupal, and adult stages of Tenebrio molitor. Five different concentrations were tested for each substance (DE at 25, 50, 100, 250, and 500 ppm, and 1,8-cineole at 2.5, 5, 10, 15, and 20 ppm), and four DE + 1,8-cineole combinations were evaluated within the same experimental system. Mortality was monitored over time, LC₅₀ values were calculated by probit analysis, and larval output observed after adult treatments was also evaluated. The findings indicated that the biological response was associated with developmental stage. The lowest LC₅₀ for DE was recorded in larvae at 86.11 ppm on day 3, whereas for 1,8-cineole the lowest LC₅₀ was recorded in adults at 94.83 ppm on day 3. Combined treatments generally tended to produce faster and stronger mortality; in particular, the DE250 + CIN20 treatment reached 100% mortality in larvae and adults and 93.33% mortality in pupae by day 7. In addition, larval output decreased in the single-treatment groups, the proportion of dead larvae among the observed larvae increased to 96–100%, and no larval output was detected in the combination groups. Combinations of DE and 1,8-cineole tended to produce more pronounced mortality responses than the single treatments, particularly in the larval and adult stages. The present findings indicate that combining DE with 1,8-cineole may provide a promising stage-specific strategy for improving the control of T. molitor under laboratory conditions. Full article

Varroa destructor (Bee mite) and Deformed Wing Virus are primary causes of honeybee colony collapse. This study developed an automated AI-based vision inspection system for detecting bee mites and deformed bees using the YOLO algorithm. The system integrates an RGB camera, a beecomb rotation motor, and an image transmission module to enable automated dual-sided image acquisition of the beecomb. The image characteristics of normal bees, bee mites, and deformed bees were analyzed, and YOLO-based object detection models were developed to classify them. Six YOLO models—based on YOLOv8 and YOLOv11 architectures across three model sizes (nano, small, and large)—were evaluated on 405 test images (6441 objects). The proposed system reduced the inspection time from 240 s required for manual method to 20 s per beecomb, achieving 12-fold efficiency improvement. Comparative analysis showed model-task specialization: YOLOv8l excelled in detecting small bee mites (F1: 92.5%, mAP[0.5]: 92.1%), while YOLOv11s achieved the highest performance for morphologically diverse deformed bees (F1: 95.1%). Error analysis indicated that detection performance was influenced by morphological characteristics. Deformed bee detection errors correlated with overlap in wing-to-body ratio: DB Type II exhibited 18.6% miss rate, while DB Type III achieved perfect detection. In bee mite detection, a sensitivity–specificity trade-off was observed: YOLOv11l had the lowest false negatives (2.5%) but highest false positives, while YOLOv8l demonstrated superior discrimination. These results demonstrate the practical potential of the proposed system for field deployment in apiaries, supporting early pest diagnosis and improved colony health management. The model-task specialization framework provides guidance for architecture selection based on object characteristics. Future work will focus on multi-location validation and real-time monitoring integration. Full article

In order to investigate the application potential of lightweight agricultural robots utilizing carbon fiber-reinforced polymer (CFRP) as the primary structural material, this study developed a dedicated rubber-tracked chassis tailored for peanut pest and disease monitoring robots. The chassis design is anchored to the widely applied “single ridge with double rows” cultivation pattern in peanut production and incorporates a real-time track tension monitoring mechanism integrated with pressure sensors. The overall structural configuration of the chassis fully conforms to the standard ridge parameters of mechanized peanut planting while fully considering the intrinsic material properties of CFRP. Additionally, a sprocketless drive wheel structure is specifically adopted to realize higher-precision motion control performance. A mathematical model was constructed to quantitatively characterize the tension correlation between the tight side and slack side of the rubber track, as well as the variation law of initial tension influenced by multiple factors including the total mass of the robot platform. With the curb weight of the robot platform set at 45 kg, the theoretical initial tension is calculated to be 24.5 N (equivalent to approximately 2.5 kg, taking the gravitational acceleration g = 9.8 m/s²). The prototype shows potential for maintaining consistent tension, though a mechanical weakness was identified and will be addressed in future work. Performance validation tests show that the chassis maintains stable operation with no sprocket slippage during field visual inspection. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article

Anomala corpulenta (Motschulsky, 1854) (Coleoptera: Scarabaeidae), an important agricultural and forestry pest, is a beetle widely distributed in many countries, inflicting damage on numerous crops. Given the limited selectivity of commonly used light trapping devices for insects and their potential adverse effects on the ecological environment, there is a pressing need for innovative light control methods. This study investigates the effects of continuous exposure to yellow light on the behavioral activities of A. corpulenta adults, which are nocturnal. The experimental setup comprised a light experimental group (exposed continuously to yellow light at wavelengths of 565–585 nm and intensities of 30–40 lx at night) and a control group (kept in a dark room). Observations were made on emergence, mating, feeding, and mortality. Results showed that continuous exposure to yellow light significantly alters the emergence rhythm of A. corpulenta, leading to delays and dispersions in peak emergence, with emergence occurring during the light period. The emergence rates varied significantly from the control group during specific periods, and the overall emergence rate was notably affected, with female insects exhibiting greater sensitivity. Furthermore, food consumption and the number of mating pairs were significantly lower compared to the control group. Continuous exposure to yellow light also influenced the longevity of A. corpulenta; in the mixed test group, female insects had a lifespan of 20 days, while males lived for 18 days. In the sexually isolated test group, both sexes died within 16 days, with the survival rates of the experimental group being lower than those of the control group on certain days. This study concludes that continuous exposure to yellow light significantly modifies the emergence rhythm of A. corpulenta, while reducing the emergence rate, total food intake, and the number of mating pairs. Notably, in the mixed-sex test group, the survival probability of females in the experimental group was significantly lower than that of the control group. These findings provide a theoretical foundation for the light control of A. corpulenta and contribute to the field of insect visual ecology. Full article

Cats are among the most beloved and affectionate companion animals to humans. Historically, they have been utilised to manage pests or offer comfort and companionship, a practice that continues today. Due to human malpractice, unowned free-roaming cats (as stray pets or feral cats) are now considered amongst the 100 worst invasive species, and are responsible for the decline and even the disappearance of many wild species worldwide. Free-roaming cats maintain their hunting instincts, causing problems for native species, which is recognised as a major issue in island biodiversity. Despite their impact, limited studies have been conducted to understand the spatial activity of free-roaming cats in the Mediterranean when they are away from their caregivers (owners who feed and care for their cats while allowing unrestricted outdoor roaming). To investigate this, we used GPS tracking collars to monitor 15 free-roaming cats on the island of Cyprus, during spring–autumn 2022. The monitored cats were active in a spectrum of different habitats, from forests and farmland to shrublands and the suburbs. We monitored cats for 5.6 days, on average, to investigate their home range sizes (KDE 95%; median: males = 55,678 m²; females = 11,377 m²), daily distance travelled (median: males = 1233 m; females = 538 m), and daily/nocturnal activity, and the factors that influence these patterns. The animals’ sex, shelter availability, and the type of coverage in an area show statistically significant differences in relation to their home range, while activity peaked during the afternoon hours, a finding that is also statistically confirmed. Although the sample size of the study is relatively small, the influence of environmental and anthropogenic factors on the home range of free-roaming cats in Cyprus is revealed. These findings offer quantitative evidence and can contribute to wildlife conservation and free-roaming cat management. Full article