Search Results (53)

Cnidium monnieri (L.) Cusson (Apiaceae) is a well-known insectary plant in farmlands. Its vegetative and flowering stages can promote the migration of natural enemies. However, the arrival of natural enemies in crop fields often lags behind the establishment of pest populations. As gibberellin can accelerate plant growth and flowering, we investigated whether gibberellin treatment could advance the recruitment of natural enemies by altering the phenology of C. monnieri. Field experiments were conducted in 2021 and 2022 to evaluate the effects of different gibberellin concentrations on plant phenology, growth traits, and natural-enemy abundance. For field validation, C. monnieri strips established in wheat fields were treated with water or 50 mg/L gibberellin. The 50 mg/L treatment advanced the onset of flowering by 21 days and the first detection of natural enemies on C. monnieri by 14 days in 2021 and 20 days in 2022. It also significantly increased natural-enemy abundance on C. monnieri in 2022. In wheat fields, the same treatment resulted in earlier detection and significantly greater abundance of natural enemies in 2022. The earlier detection of natural enemies was temporally consistent with the advancement of flowering. These findings indicate that manipulating the flowering phenology of insectary plants may improve the timing of natural-enemy establishment and strengthen conservation biological control. 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

Reliable stored-grain pest monitoring is essential for smart granaries, yet probe-based field images pose three coupled bottlenecks: tiny and densely distributed pests in complex backgrounds, costly bounding-box annotation, and limited edge-side computing resources. To address these bottlenecks in a targeted manner, this study proposes GrainPest-SSL, an integrated framework comprising a field dataset, a lightweight detector, and a pseudo-label purification-based semi-supervised pipeline. First, to overcome the lack of realistic training data, a GrainPest dataset with 1000 field images and 21,676 annotated pest instances is constructed using multiple self-developed monitoring probes deployed in a large wheat flat granary, capturing systematic pest-monitoring images from different in-bin locations rather than a single fixed imaging point. Second, to improve small-target detection under resource constraints, a YOLOv8n-CAEMA detector is designed with a P2 detection head and tail-inserted Coordinate Attention (CA) and Efficient Multi-scale Attention (EMA), achieving 0.840 mAP@0.5 under full supervision with only 2.932 M parameters. Third, to reduce annotation dependence without adding inference-stage complexity, an offline Teacher–Student strategy with Pseudo-Label Purification Filtering (PPLF) refines pseudo-labels using confidence, size, and aspect-ratio priors; under the 30% labeled setting, GrainPest-SSL improves mAP@0.5 from 0.738 to 0.799 and mAP@0.5:0.95 from 0.322 to 0.369 on average over three random seeds. Comparisons with representative agricultural pest detectors and semi-supervised object detection (SSOD) methods further confirm the balanced accuracy–efficiency performance of GrainPest-SSL under label-limited conditions. The deployed Student detector further achieves 13.6 FPS in FP16 mode on a Jetson Orin Nano Dev Kit under the 10 W power mode, supporting scheduled pest inspection, early infestation screening, and intelligent warning in smart granary monitoring systems. Full article

The outbreak of pests seriously affects the yield and quality of wheat crops. The accurate recognition and detection play an essential role in the early warning of crop pests. While some limitations, like insufficient dataset, imbalanced samples of pests with dense distribution, and dense distribution and tinyof crop pests, pose significant challenges to the precise detection. Thus, in this work, we first spent two years collecting real-world wheat pest images with four types of pests, including three grain aphids, and one mite species, to obtain a high-quality crop pest dataset for network optimization. Secondly, to alleviate insufficiency of samples of pests with dense distribution, we have developed a cut-up data augmentation strategy that separates dense pest targets from complex backgrounds. Furthermore, to address the challenge of pest detection with tiny body size and dense distribution, we introduce the Multiple Kernel Attention Network (MKA-Net), which further integrates the multi-scale features of pests to improve detection accuracy. Our method achieved the best detection precision, with AP50 reaching its peak at 67.1%, which is a significant improvement of nearly 6.9 points in wheat pest detection compared with the baseline. In summary, our proposed method can assist in the prevention and control of wheat pests and promote the progress of intelligent agriculture. 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

Reliable detection of small agricultural targets is fundamental to precision crop protection, phenotyping, yield estimation, and robotic intervention. Typical examples include detecting aphids such as Aphis gossypii, whiteflies such as Bemisia tabaci, planthoppers such as Nilaparvata lugens, and other tiny pests on sticky traps or crop canopies for early warning, identifying crop-like weed seedlings for site-specific herbicide spraying, locating early disease lesions for targeted treatment, and detecting young fruits, flowers, or wheat heads for yield estimation and robotic manipulation. Agricultural small-object detection differs from generic small-object detection because target visibility is jointly determined by pixel area, physical size, imaging distance, ground sampling distance, canopy structure, biological similarity, and task-specific intervention requirements. Existing reviews have summarized agricultural object detection or general small-object detection, but they rarely connect agricultural failure modes with detector-level mechanisms and reproducible evaluation practices. This review addresses this gap through a mechanism-oriented synthesis of agricultural small-object detection. First, we revisit the limitations of the COCO-style 32²-pixel threshold and propose an agricultural scale-reporting framework that combines pixel area, physical scale, relative image occupancy, and acquisition geometry. Second, we organize recent methods according to the mechanisms by which they address detail loss, scale shift, occlusion, dense distributions, foreground–background confusion, localization uncertainty, and edge-deployment constraints. Third, we summarize public datasets, quantitative evaluation metrics, reporting checklists, and real-device deployment evidence to support fair and field-oriented comparison. Finally, we identify future directions in multimodal sensing, foundation-model adaptation, label-efficient learning, and hardware-aware optimization. By linking agricultural scene characteristics, detector mechanisms, and evaluation requirements, this review aims to provide a more actionable framework for developing robust small-object detection systems in precision agriculture. Full article

The grain aphid (Sitobion avenae) is a major pest of winter wheat, causing significant yield losses through direct feeding and as a vector of barley yellow dwarf virus (BYDV). Populations can increase rapidly under moderate temperatures and low rainfall, potentially leading to severe infestations if not effectively monitored and managed. This study develops and validates a UAV-based RGB imaging methodology, which relies on deep learning for accurate detection and assessment of Sitobion avenae in wheat crops. The RGB images are preliminarily filtered using “histogram equalization”, which allows for highlighting the infested areas. An experimental study was conducted under the specific climatic conditions of Southern Dobruja, Bulgaria, to quantify Sitobion avenae infestations. Three neural network architectures were used (DeepLabv3, U-Net, and PSPNet) in combination with three backbone models: ResNet34, ResNet50, and ResNet101. The optimal combination was determined to be the U-Net + ResNet101 model, which achieved an average F1 score of 0.982 and a Cohen’s Kappa coefficient of 0.966. The results demonstrate that UAV-based detection allows precise mapping of infested areas, enabling targeted insecticide applications and effective pest management while substantially reducing chemical inputs. These findings indicate that the proposed framework provides a reliable and scalable tool for precision pest monitoring and control in winter wheat. Full article

The rice weevil (Sitophilus oryzae) is a major pest in stored wheat, and traditional detection methods face challenges in identifying its hidden life stages within kernels. This study develops a nondestructive method to detect S. oryzae (Sitophilus oryzae) infestation in wheat kernels using hyperspectral imaging, spectral preprocessing, feature extraction, and classification modeling. Hyperspectral data were collected from wheat kernels at different infestation stages (1, 11, 21, and 25 days (d)) and from healthy kernels. Spectral quality was optimized using SG smoothing, multiplicative scatter correction (MSC), and standard normal variate transformation (SNV). Feature extraction algorithms, including Competitive Adaptive Re-weighting Algorithm (CARS), Successive Projection Algorithm (SPA), and Iterative Retention of Information Variables (IRIV), were used to reduce data dimensionality, while classification models like Decision Tree (DT), K-nearest neighbors (KNN), and Support Vector Machine (SVM) were applied. The results show that MSC preprocessing provides the best performance among the models. After feature band selection, the MSC-CARS-SVM model achieved the highest accuracy for the 1 day and 25 d samples (95.48% and 96.61%, respectively). For the 11 d and 21 d samples, the MSC-IRIV-SPA-SVM model achieved the best performance with accuracies of 94.35% and 94.92%, respectively. This study demonstrates that MSC effectively reduces spectral noise and improves classification performance. After feature selection, the model shows significant improvements in both accuracy and stability. The study confirms the feasibility of using hyperspectral technology to identify healthy and S. oryzae-infested wheat kernels, providing theoretical support for early, nondestructive pest detection. Full article

Cereal leaf beetle (CLB, Oulema melanopus L.) is an important pest that damages cereals. Insecticide use against CLB could be reduced with targeted treatments. Our aims were to develop a methodology to map CLB damage on cereal fields using remote sensing. We investigated the suitability of four vegetation indices (VIs: the Visible Atmospherically Resistance Index (VARI), the Green Chromatic Coordinate (GCC), the Green Leaf Index (GLI), and the Normalized Green–Red Difference Index (NGRDI)) derived from RGB images (drone (UAV) imagery). Study sites were located in different regions of Hungary in 2024. Images were taken at different phenological stages of cereals. Suitability of VIs was analyzed with ANOVA and MANOVA. Machine learning models were developed to classify damaged field sections with random forest (RF) and Light Gradient Boosting Machine (LightGBM) algorithms. Results show that VARI, GCC, GLI, and NGRDI contain complementary features for early detection of CLB damage. Difference in sample points’ VI from field median is advantageous for the LGBM algorithm (F1_damaged = 0.64–0.72), while the best RF models were obtained with more features (F1_damaged = 0.66). Random test data splits had optimistic results (overall accuracy: RF = 0.63–0.80, LightGBM = 0.63–0.79) compared to spatially controlled test splits (overall accuracy: RF = 0.53–0.70, LightGBM = 0.53–0.62). Full article

The spectral characteristics of harmful insect pests in wheat fields were characterised using hyperspectral imaging for the first time. The analysis of spectral profiles revealed that reflectance is determined by the structure of the insect’s chitin and the colouration of its body surface. Insects with lighter or more vivid colours (white, yellow, or green) showed higher reflectance values compared to those with predominantly dark pigmentation. Reflectance was also influenced by the presence of wings, surface roughness, and the age of the insect. Each species exhibited distinct spectral patterns that allowed for differentiation not only from other insect species but also from the plant background. A classification model using PLS-DA was developed and demonstrated high accuracy in identifying 12 pest species, confirming the strong potential of hyperspectral imaging for species-level classification. The results validate the PLS-DA method for differentiating insects based on spectral characteristics and underscore the reliability of this approach for automated monitoring systems to detect phytophagous pests in crop fields. This technology could reduce insecticide use by 30–40% through targeted application. The research has both scientific and economic significance, laying the groundwork for integrating machine learning and computer vision into agricultural monitoring. It supports the advancement of precision farming and contributes to improved global food security. Full article

The cereal leaf beetle (CLB; Oulema melanopus L., Coleoptera: Chrysomelidae) is a serious pest of wheat, capable of causing yield losses of up to 40% through photosynthetic impairment. Early detection and severity assessment are essential for effective and sustainable pest management. This study evaluates the potential of hyperspectral remote sensing (RS) combined with machine learning (ML) for non-invasive detection of CLB-induced stress in winter wheat. Spectral reflectance was measured using a full-range spectroradiometer (350–2500 nm) from flag leaves categorized into four damage levels (healthy, slightly, moderately, and severely damaged). Three input datasets were used for ML classification: full spectral reflectance, a set of 13 vegetation indices (VIs), and outputs of dimensionality reduction technique. CLB stress increased reflectance in the visible range (400–700 nm) and reduced it in the near-infrared (700–1400 nm), consistent with chlorophyll degradation and mesophyll damage. Several VIs, including RIGreen, NDVI750, GNDVI, and NDVI, correlated strongly with damage severity (τ = 0.78–0.81). Among the six ML models tested, Support Vector Machine (SVM) achieved the highest classification accuracy of 90.0% (precision = 0.90, recall = 0.90, F1 = 0.90) across the four severity classes, and achieved 91.9% accuracy at the early-detection threshold. As far as the currently available literature indicates, this study provides one of the earliest quantitative assessments of CLB damage severity based on full-spectrum leaf-level hyperspectral reflectance integrated with ML classification. These findings were obtained under controlled, leaf-level measurement conditions and therefore represent a proof-of-concept; future validation using UAV and satellite platforms is needed to assess performance under operational field variability. Overall, our findings highlight the potential of hyperspectral RS and ML for precision pest monitoring, supporting threshold-based decision-making and more sustainable insecticide use. Full article

To address prevalent challenges in field-based wheat pest recognition—namely, viewpoint perturbations, sample scarcity, and heterogeneous data distributions—a pest identification framework named CropCLR-Wheat is proposed, which integrates self-supervised contrastive learning with an attention-enhanced mechanism. By incorporating a viewpoint-invariant feature encoder and a diffusion-based feature filtering module, the model significantly enhances pest damage localization and feature consistency, enabling high-accuracy recognition under limited-sample conditions. In 5-shot classification tasks, CropCLR-Wheat achieves a precision of 89.4%, a recall of 87.1%, and an accuracy of 88.2%; these metrics further improve to 92.3%, 90.5%, and 91.2%, respectively, under the 10-shot setting. In the semantic segmentation of wheat pest damage regions, the model attains a mean intersection over union (mIoU) of 82.7%, with precision and recall reaching 85.2% and 82.4%, respectively, markedly outperforming advanced models such as SegFormer and Mask R-CNN. In robustness evaluation under viewpoint disturbances, a prediction consistency rate of 88.7%, a confidence variation of only 7.8%, and a prediction consistency score (PCS) of 0.914 are recorded, indicating strong stability and adaptability. Deployment results further demonstrate the framework’s practical viability: on the Jetson Nano device, an inference latency of 84 ms, a frame rate of 11.9 FPS, and an accuracy of 88.2% are achieved. These results confirm the efficiency of the proposed approach in edge computing environments. By balancing generalization performance with deployability, the proposed method provides robust support for intelligent agricultural terminal systems and holds substantial potential for wide-scale application. Full article

Wireworms are often referred as a hardly manageable group of pests due to their unstable lifestyle and uneven distribution in soils. The current strategy of wireworm control involves the heavy use of chemical pesticides. To find an effective and eco-friendly biological control agent against wireworms, evaluation of bacterial properties and insecticidal effects of six Bacillus thuringiensis (Bt) strains against Agriotes lineatus was performed under laboratory conditions. The presence of cry11, cyt2 and krsA gene was detected in Bt strain BHC 2.4, while the same strain had the ability to produce siderophores, protease, amylase and cellulase. Single inoculums of Bt strains (BHC 2.4; BHC 4.5; BHC 4.7; 1.5; 4.3; 6.1) showed mortality against Agriotes lineatus larvae in the range of 6.67–72.22%. However, the compatible Bt dual cultures showed significantly higher efficiency in comparison with the single inoculums, with the highest efficiency of 79.63% recorded for Bt strain BHC 2.4 + Bt strain 1.5. The efficiency of applied Bt strains might be associated with the presence of genes coding for antibiotics and toxins. Therefore, the use of selected Bt strains applied in a form of compatible mixes could offer a sustainable solution for wireworm management in wheat. Full article

Stored wheat pests threaten food quality and economic returns, yet existing detection methods struggle with small-object detection, complex scenarios, and efficiency–accuracy trade-offs, largely due to the lack of high-quality datasets. To address these challenges, this study constructed MPest3 dataset for stored wheat pests and proposed an enhanced detection model, FCA-YOLO, based on YOLOv8. This multi-scale fusion architecture, combining pyramid feature extraction with adaptive spatial weighting, improves the detection of small pests through hierarchical feature integration. Experimental results demonstrate that FCA-YOLO enhances multi-scale feature extraction and spatial fusion, achieving a 2.06% increase in mAP, a 4.51% improvement in accuracy, and reducing the pre- and postprocessing time for each image. Compared to Faster-rcnn, FCA-YOLO achieves a better balance between accuracy and computational efficiency, providing a robust and efficient solution for intelligent pest monitoring in grain storage applications. Full article

Dinotefuran and clothianidin belong to the third generation of nicotinic insecticides and are widely used in crop pest control. It is necessary to detect their residues in food. The time-resolved fluorescent microspheres lateral flow immunoassay (TRFMs-LFIA) has the advantages of high sensitivity, short duration, and simple operation and is suitable for rapid field testing. In this study, two haptens (FCA-1, FCA-2) were synthesized in three steps and conjugated to the carrier proteins to obtain artificial antigens, which were subsequently used for monoclonal antibody preparation. A TRFMs-LFIA based on monoclonal antibodies was established to detect dinotefuran and clothianidin residues in food. The limit of detection (LOD) for dinotefuran was 0.045 ng/mL, with an IC₅₀ of 0.61 ng/mL and a linear range (IC₂₀~IC₈₀) of 0.12~3.11 ng/mL. The LOD for clothianidin was 0.11 ng/mL, with an IC₅₀ of 0.94 ng/mL and a linear range (IC₂₀~IC₈₀) of 0.24~3.65 ng/mL. Cross-reactivity rates with seven tested structural analogs were less than 1.5%. The pretreatment method was optimized for wheat, cucumber, and cabbage samples, which was time-saving (20 min) and easy to operate. The average recovery rates ranged from 88.0% to 114.8%, with the corresponding coefficients of variation appearing (CV) between 1.9% and 13.5%. The results of actual wheat, cucumber, and cabbage samples detected by the established TRFMs-LFIA were consistent with those of Ultra-Performance Liquid Chromatography coupled with Tandem Mass Spectrometry (UPLC-MS/MS). These results demonstrate that the established TRFMs-LFIA is sensitive, accurate, rapid, and suitable for real sample detection. Full article