Search Results (289)

Rice, as one of the world’s four major staple crops, is frequently threatened by pests and diseases during its growth. With the rapid expansion of agricultural information data, the effective management and utilization of such data have become crucial for the development of agricultural informatization. Named entity recognition technology offers precise support for the early prevention and control of crop pests and diseases. However, entity recognition for rice pests and diseases faces challenges such as structural complexity and prevalent nesting issues. Inspired by biological visual mechanisms, we propose a deep learning model capable of extracting multi-granularity features. Text representations are encoded using BERT, and the model enhances its ability to capture nested boundary information through multi-granularity convolutional neural networks (CNNs). Finally, sequence modeling and labeling are performed using a bidirectional long short-term memory network (BiLSTM) combined with a conditional random field (CRF). Experimental results demonstrate that the proposed model effectively identifies entities related to rice diseases and pests, achieving an F1 score of 91.74% on a self-constructed dataset. Full article

Farmland ecosystems present complex pest–predator co-occurrence patterns, posing significant challenges for image-based multitarget recognition and ecological modeling in sensor-driven computer vision tasks. To address these issues, this study introduces a tree-guided Transformer framework enhanced with a knowledge-augmented co-attention mechanism, enabling effective feature extraction from sensor-acquired images. A hierarchical ecological taxonomy (Phylum–Family Species) guides prompt-driven semantic reasoning, while an ecological knowledge graph enriches visual representations by embedding co-occurrence priors. A multimodal dataset containing 60 pest and predator categories with annotated images and semantic descriptions was constructed for evaluation. Experimental results demonstrate that the proposed method achieves 90.4% precision, 86.7% recall, and 88.5% F1-score in image classification, along with 82.3% hierarchical accuracy. In detection tasks, it attains 91.6% precision and 86.3% mAP@50, with 80.5% co-occurrence accuracy. For hierarchical reasoning and knowledge-enhanced tasks, F1-scores reach 88.5% and 89.7%, respectively. These results highlight the framework’s strong capability in extracting structured, semantically aligned image features under real-world sensor conditions, offering an interpretable and generalizable approach for intelligent agricultural monitoring. Full article

With the rapid advancement of artificial intelligence technology, the widespread application of deep learning in computer vision is driving the transformation of agricultural pest detection and control toward greater intelligence and precision. This paper systematically reviews the evolution of agricultural pest detection and control technologies, with a special focus on the effectiveness of deep-learning-based image recognition methods for pest identification, as well as their integrated applications in drone-based remote sensing, spectral imaging, and Internet of Things sensor systems. Through multimodal data fusion and dynamic prediction, artificial intelligence has significantly improved the response times and accuracy of pest monitoring. On the control side, the development of intelligent prediction and early-warning systems, precision pesticide-application technologies, and smart equipment has advanced the goals of eco-friendly pest management and ecological regulation. However, challenges such as high data-annotation costs, limited model generalization, and constrained computing power on edge devices remain. Moving forward, further exploration of cutting-edge approaches such as self-supervised learning, federated learning, and digital twins will be essential to build more efficient and reliable intelligent control systems, providing robust technical support for sustainable agricultural development. Full article

The greater wax moth Galleria mellonella (Lepidoptera: Galleriinae) represents a ubiquitous apicultural pest that poses significant threats to global beekeeping industries. The larvae damage honeybee colonies by consuming wax combs and tunneling through brood frames, consequently destroying critical hive infrastructure including brood-rearing areas, honey storage cells, and pollen reserves. Larval feeding behavior is critically dependent on chemosensory input for host recognition and food selection. In this study, we conducted a transcriptome analysis of larval heads and bodies in G. mellonella. We identified a total of 25 chemosensory genes: 9 odorant binding proteins (OBPs), 1 chemosensory protein (CSP), 5 odorant receptors (ORs), 4 gustatory receptors (GRs), 4 ionotropic receptors (IRs) and 2 sensory neuron membrane proteins (SNMPs). TPM normalization was employed to assess differential expression patterns of chemosensory genes between heads and bodies. Nine putative chemosensory genes were detected as differentially expressed, suggesting their potential functional roles. Subsequently, we quantified expression dynamics via reverse transcription quantitative PCR in major chemosensory tissues (larval heads, adult male and female antennae), revealing adult antennal-biased expression for most chemosensory genes in G. mellonella. Notably, two novel candidates (GmelOBP22 and GmelSNMP3) exhibited particularly high expression in larval heads, suggesting their crucial functional roles in larval development and survival. These findings enhance our understanding of the chemosensory mechanisms in G. mellonella larvae and establish a critical foundation for future functional investigations into its olfactory mechanisms. Full article

Intelligent pest detection has become a critical challenge in precision agriculture. Addressing the challenge of distinguishing between aphids, thrips, whiteflies, beet armyworms, spodopetra frugiperda, and spider mites during strawberry growth, this study establishes a small-sample multi-pest dataset for strawberries through field photography, open-source sharing, and web scraping. This study introduces a channel–space parallel attention mechanism (PCSA) into the UNet architecture. This improved UNet model accentuates pest color and morphology through channel-based attention and emphasizes spatial localization with coordinate-based attention, allowing for the comprehensive integration of global and local pixel information. Subsequently, comparative analysis of several color spaces identified HSV as optimal for pest recognition, with the “UNet + PCSA + HSV” approach achieving state-of-the-art results (IoU, 84.8%; recall, 89.9%; precision, 91.8%). Full article

Silicon (Si) is gaining recognition as a sustainable alternative to reduce insecticide use in the management of the Asian citrus psyllid and huanglongbing (HLB). This study aimed to evaluate the effects of two silicon sources and three application methods on Diaphorina citri incidence, soil chemical properties, and foliar nutrient uptake in a Tahiti lime orchard. Using a randomized block design, treatments were applied six times over three months. Foliar and combined applications of diatomaceous earth reduced vegetative flushing and decreased natural psyllid incidence by up to 75% in the first 30 days. While silicon did not affect oviposition in induced infestations, it disrupted the nymph-to-adult transition. Silicon also improved soil conditions, increasing pH, organic matter, and the availability of phosphorus, calcium, and magnesium. In leaf tissue, higher levels of nitrogen, phosphorus, potassium, iron, and silicon (0.28–0.50%) were observed. Fruit quality improved with silicon, showing greater fresh weight (134 g) and juice content (44.7%) compared to the control (95.33 g and 28.5%). The results suggest that silicon’s effectiveness depends more on its availability and application method than its source. Incorporating silicon, especially diatomaceous earth, into fertilization programs supports pest control, enhances soil and plant nutrition, and improves fruit quality. Full article

Chemosensory systems are fundamental for insects to regulate behaviors such as prey detection, oviposition, and pollination. Despite their importance, the molecular mechanisms underlying chemosensation remain poorly understood in many insect groups. Hoverflies (Syrphidae), whose larvae are efficient aphid predators and adults act as pollinators, represent a functionally important but understudied lineage. Building on the genome of Eupeodes corollae that we recently published, we selected this dominant and widespread species as a representative model and performed a genome-wide identification and analysis of odorant-binding proteins (OBPs) to provide a molecular foundation for understanding chemosensory recognition mechanisms. Accordingly, a total of 47 OBPs were identified and classified into Classic, Minus-C, and Plus-C subfamilies, with conserved motifs and structural features observed within each group. Next, phylogenetic analysis revealed that several EcorOBPs are homologous to functionally characterized OBPs in other Diptera, suggesting conserved evolutionary roles. Moreover, chromosomal mapping showed that Minus-C EcorOBPs cluster on chromosome 2, and Ka/Ks analysis indicated strong purifying selection, reflecting evolutionary stability. In addition, synteny analysis demonstrated that E. corollae shares more collinear OBP gene pairs with predatory hoverflies (Episyrphus balteatus and Scaeva pyrastri) than with the saprophagous species Eristalis tenax, consistent with ecological divergence. Finally, transcriptomic profiling revealed tissue-specific expression patterns, including antennal-biased EcorOBP1 linked to olfaction and reproductive tissue-biased EcorOBP11 linked to reproduction, highlighting candidate genes for functional studies. Together, these findings provide a comprehensive characterization of OBPs in E. corollae and offer molecular insights into chemosensory mechanisms that support both pest control and pollination services. Full article

Field-scale pest monitoring requires accurate pest recognition and classification techniques. However, there are two main challenges in practical pest detection tasks. First, both intra-species morphological variation across developmental stages and inter-species size differences create challenges for models adapting to multi-scale features. Second, biological camouflage reduces target-background contrast, increasing the difficulty of model recognition. To address these issues, this paper proposes an improved pest detection model, IP-YOLOv8, based on YOLOv8s. First, a multi-scale feature fusion architecture is introduced, establishing a cross-layer feature interaction mechanism that effectively integrates shallow detailed features and deep semantic features, significantly enhancing the model’s multi-scale representation ability. Second, a dynamic detection head is designed to address the diverse morphology of pests. This head adapts the receptive field through a dynamic sampling mechanism, allowing the model to accurately capture pest features of varying scales and shapes. Finally, to tackle the issue of camouflage background confusion, an edge feature fusion module is proposed to enhance target contour information, thereby addressing the blurring of edge features caused by camouflage. Experimental results demonstrate that IP-YOLOv8 outperforms YOLOv8s on the IP102 dataset, achieving improvements of 2.2% in mAP50, 1.3% in mAP50:95, 3.1% in precision, and 1.5% in recall. This method effectively adapts to complex field pest detection tasks, providing strong technical support for precision agriculture. 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

A multimodal interactive system, HortiVQA-PP, is proposed for horticultural scenarios, with the aim of achieving precise identification of pests and their natural predators, modeling ecological co-occurrence relationships, and providing intelligent question-answering services tailored to agricultural users. The system integrates three core modules: semantic segmentation, pest–predator co-occurrence detection, and knowledge-enhanced visual question answering. A multimodal dataset comprising 30 pest categories and 10 predator categories has been constructed, encompassing annotated images and corresponding question–answer pairs. In the semantic segmentation task, HortiVQA-PP outperformed existing models across all five evaluation metrics, achieving a precision of 89.6%, recall of 85.2%, F1-score of 87.3%, $mAP@50$ of 82.4%, and IoU of 75.1%, representing an average improvement of approximately 4.1% over the Segment Anything model. For the pest–predator co-occurrence matching task, the model attained a multi-label accuracy of 83.5%, a reduced Hamming Loss of 0.063, and a macro-F1 score of 79.4%, significantly surpassing methods such as ASL and ML-GCN, thereby demonstrating robust structural modeling capability. In the visual question answering task, the incorporation of a horticulture-specific knowledge graph enhanced the model’s reasoning ability. The system achieved 48.7% in BLEU-4, 54.8% in ROUGE-L, 43.3% in METEOR, 36.9% in exact match (EM), and a GPT expert score of 4.5, outperforming mainstream models including BLIP-2, Flamingo, and MiniGPT-4 across all metrics. Experimental results indicate that HortiVQA-PP exhibits strong recognition and interaction capabilities in complex pest scenarios, offering a high-precision, interpretable, and widely applicable artificial intelligence solution for digital horticulture. Full article

Adoxophyes orana (Lepidoptera: Tortricidae) is a significant polyphagous leafroller that damages trees and shrubs in Rosaceae and other families. However, the molecular mechanisms by which this pest recognizes sex pheromones and host plant volatiles remain largely unknown. Tissue expression profiles indicated that two general odorant-binding proteins (AoraGOBP1 and AoraGOBP2) were more abundant in the antennae and wings of both sexes, with AoraGOBP1 being rich in the female head and abdomen. Temporal expression profiles showed that AoraGOBP1 was expressed at the highest level in 5 day-nmated adults, while AoraGOBP2 exhibited high expression in 5 day-unmated, 7 day-unmated, and mated female adults. Fluorescence competitive binding assays of heterologous expressed AoraGOBPs demonstrated that AoraGOBP2 strongly bound to the primary sex pheromone Z9-14:Ac, and two minor sex pheromones Z9-14:OH and Z11-14:OH, whereas AoraGOBP1 only showed a high binding affinity to Z9-14:Ac. What is more, AoraGOBP1 exhibited a broader binding spectrum for host plant volatiles than AoraGOBP2. Molecular dockings, molecular dynamic simulations, and per-residue binding free decompositions indicated that the van der Waals interaction was the predominant contributor to the binding free energy. Electrostatic interactions between aldehydes, or alcohols and AoraGOBPs stabilized the conformational structures. Phe12 from AoraGOBP1, and Phe13 from AoraGOBP2 were identified as the most important residues that contributed to bind free energy. Our findings provide a comprehensive insight into the molecular mechanisms of olfactory recognition in A. orana, facilitating the development of chemical ecology-based approaches for the control. Full article

Durian is a major tropical crop in Southeast Asia, but its yield and quality are severely impacted by a range of pests and diseases. Manual inspection remains the dominant detection method but suffers from high labor intensity, low accuracy, and difficulty in scaling. To address these challenges, this paper proposes EN-YOLO, a novel enhanced YOLO-based deep learning model that integrates the EfficientNet backbone and multimodal attention mechanisms for precise detection of durian pests and diseases. The model removes redundant feature layers and introduces a large-span residual edge to preserve key spatial information. Furthermore, a multimodal input strategy—incorporating RGB, near-infrared and thermal imaging—is used to enhance robustness under variable lighting and occlusion. Experimental results on real orchard datasets demonstrate that EN-YOLO outperforms YOLOv8 (You Only Look Once version 8), YOLOv5-EB (You Only Look Once version 5—Efficient Backbone), and Fieldsentinel-YOLO in detection accuracy, generalization, and small-object recognition. It achieves a 95.3% counting accuracy and shows superior performance in ablation and cross-scene tests. The proposed system also supports real-time drone deployment and integrates an expert knowledge base for intelligent decision support. This work provides an efficient, interpretable, and scalable solution for automated pest and disease management in smart agriculture. Full article

Goji berry is widely consumed worldwide and holds substantial market value, yet its cultivation faces significant threats from the goji berry psyllid (Bactericera gobica). Chemosensory-related genes play critical roles in regulating insect behaviors, which makes them key molecular targets for the development of environmentally friendly pest control strategies. However, chemosensory genes in B. gobica have not been previously identified or characterized. In this study, we sequenced transcriptomes from the antennae and body tissues of male and female B. gobica and annotated genes associated with chemosensory functions. We identified 15 odorant-binding proteins (OBPs), 18 chemosensory proteins (CSPs), 3 sensory neuron membrane proteins (SNMPs), 26 odorant receptors (ORs), 8 gustatory receptors (GRs), and 32 ionotropic receptors (IRs). Transcriptome data and a quantitative real-time PCR confirmed the tissue-specific expression patterns of these genes, with several genes, including three BgobOBPs, eight BgobCSPs, one BgobOR, two BgobGRs, and two BgobIR, highly expressed in the antennae, suggesting their role in olfactory recognition. BgobGR1 was most highly expressed among GRs, indicating its important role in gustatory perception. We also identified gene BgobGR5 with differential expression patterns between females and males. Our study represents the first characterization of chemosensory genes in a Bactericera species. Full article

Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae) is one of the most destructive pests of stored grains worldwide. Sublethal concentrations of insecticides are known to influence insect behavior, potentially disrupting critical processes such as mating. This study investigated the effects of λ-cyhalothrin at the lethal concentration (LC) values LC₁₀ and LC₃₀ and lateralization on the mating behavior patterns of S. zeamais males. Results showed that the exposure to sublethal concentrations of λ-cyhalothrin significantly altered the copulation success rate and key time-related parameters, including mate recognition and copulation duration, while the lateralization caused significant differences in mating time-related parameters within each tested group (control, LC_10, and LC₃₀). Additionally, the λ-cyhalothrin-treated groups showed prolonged mate recognition times and required more mounting attempts to achieve mating. These findings highlight the potential of sublethal insecticide applications to control S. zeamais populations by impairing reproduction. Full article

In the context of global food security and sustainable agricultural development, the efficient recognition and precise management of agricultural insect pests and their predators have become critical challenges in the domain of smart agriculture. To address the limitations of traditional models that overly rely on single-modal inputs and suffer from poor recognition stability under complex field conditions, a multimodal recognition framework has been proposed. This framework integrates RGB imagery, thermal infrared imaging, and environmental sensor data. A cross-modal attention mechanism, environment-guided modality weighting strategy, and decoupled recognition heads are incorporated to enhance the model’s robustness against small targets, intermodal variations, and environmental disturbances. Evaluated on a high-complexity multimodal field dataset, the proposed model significantly outperforms mainstream methods across four key metrics, precision, recall, F1-score, and mAP@50, achieving 91.5% precision, 89.2% recall, 90.3% F1-score, and 88.0% mAP@50. These results represent an improvement of over 6% compared to representative models such as YOLOv8 and DETR. Additional ablation studies confirm the critical contributions of key modules, particularly under challenging scenarios such as low light, strong reflections, and sensor data noise. Moreover, deployment tests conducted on the Jetson Xavier edge device demonstrate the feasibility of real-world application, with the model achieving a 25.7 FPS inference speed and a compact size of 48.3 MB, thus balancing accuracy and lightweight design. This study provides an efficient, intelligent, and scalable AI solution for pest surveillance and biological control, contributing to precision pest management in agricultural ecosystems. Full article