Search Results (236)

Hilly and mountainous areas are important agricultural production regions globally. Their dramatic topography, dense fruit tree planting, and steep slopes severely restrict the application of traditional plant protection machinery. Pest and disease control has long relied on manual spraying, resulting in high labor intensity, low efficiency, and pesticide utilization rates of less than 30%. Plant protection UAVs, with their advantages of flexibility, high efficiency, and precise application, provide a feasible technical approach for plant protection operations in hilly and mountainous areas. However, steep slopes and dense orchard environments place higher demands on key technologies such as drone positioning and navigation, attitude control, trajectory planning, and terrain following. Achieving accurate identification and adaptive following of the undulating fruit tree canopy while maintaining a constant spraying distance to ensure uniform pesticide coverage has become a core technological bottleneck. This paper systematically reviews the key technologies and research progress of plant protection UAVs in hilly and mountainous operations, focusing on the principles, advantages, and limitations of core methods such as multi-sensor fusion positioning, intelligent SLAM navigation, nonlinear attitude control and intelligent control, three-dimensional trajectory planning, and multimodal terrain following. It also discusses the challenges currently faced by these technologies in practical applications. Finally, this paper discusses and envisions the future of plant protection UAVs in achieving intelligent, collaborative, and precise operations on steep slopes and in dense orchards, providing theoretical reference and technical support for promoting the mechanization and intelligentization of mountain agriculture. Full article

To address challenges such as small target size, high density, severe occlusion, complex background interference, and edge device computational constraints, a lightweight model, GAFNet, is proposed based on YOLO11n, optimized for rice pest detection in field environments. To improve feature perception, we propose the Global Attention Fusion and Spatial Pyramid Pooling (GAM-SPP) module, which captures global context and aggregates multi-scale features. Building on this, we introduce the C3-Efficient Feature Selection Attention (C3-EFSA) module, which refines feature representation by combining depthwise separable convolutions (DWConv) with lightweight channel attention to enhance background discrimination. The model’s detection head, Enhanced Ghost Detect (EGDetect), integrates Enhanced Ghost Convolution (EGConv), Squeeze-and-Excitation (SE), and Sigmoid-Weighted Linear Unit (SiLU) activation, which reduces redundancy. Additionally, we propose the Focal-Enhanced Complete-IoU (FECIoU) loss function, incorporating stability and hard-sample weighting for improved localization. Compared to YOLO11n, GAFNet improves Precision, Recall, and mean Average Precision (mAP) by 3.5%, 4.2%, and 1.6%, respectively, while reducing parameters and computation by 5% and 21%. GAFNet can deploy on edge devices, providing farmers with instant pest alerts. Further, GAFNet is evaluated on the AgroPest-12 dataset, demonstrating enhanced generalization and robustness across diverse pest detection scenarios. Overall, GAFNet provides an efficient, reliable, and sustainable solution for early pest detection, precision pesticide application, and eco-friendly pest control, advancing the future of smart agriculture. Full article

Current management strategies for sorghum aphids heavily rely on indiscriminate chemical application, leading to severe environmental consequences and impacting food safety. While precision spraying offers a viable remediation for pesticide overuse, its effectiveness depends on accurately locating and classifying pests. To address the critical challenge of segmenting small, swarming aphids in complex field environments, we propose FESW-UNet, a dual-domain attention network that integrates Fourier-enhanced attention, spatial attention, and wavelet-based downsampling into a UNet backbone. We introduce an efficient multi-scale attention (EMA) module between the encoder and decoder to enhance global context perception, enabling the model to capture more accurate relationships between global and local features in the field. In the feature extraction stage, we embed a simple attention module (SimAM) to target key infestation regions while suppressing background noise, thereby enhancing pixel-level discrimination. Furthermore, we replace conventional downsampling with Haar wavelet downsampling (HWD) to reduce resolution while preserving structural edge details. Finally, a Fourier-enhanced attention module (FEAM) is added to the skip-connection layers. By using complex-valued weights to regulate frequency-domain features, FEAM effectively fuses global low-frequency structures with local high-frequency details, thereby enhancing feature representation diversity. Experiments on the Aphid Cluster Segmentation dataset demonstrate that FESW-UNet outperforms other models, achieving an mIoU of 68.76%, mPA of 78.19%, and mF1 of 79.01%. The model also demonstrated strong adaptability on the AphidSeg-Sorghum dataset, achieving an mIoU of 81.22%, mPA of 87.97%, and mF1 of 88.60%. The proposed method offers an efficient and feasible technical solution for monitoring and controlling sorghum aphids through image segmentation, demonstrating broad application potential. Full article

Peanut (Arachis hypogaea L.) is a globally important oilseed and food legume, yet its productivity is persistently constrained by devastating diseases and insect pests that thrive under changing climates. This review aims to provide a comprehensive synthesis of advances in precision breeding and molecular approaches for enhancing disease and pest resistance in peanut. Traditional control measures ranging from crop rotation and cultural practices to chemical protection have delivered only partial and often unsustainable relief. The narrow genetic base of cultivated peanut and its complex allotetraploid genome further hinder the introgression of durable resistance. Recent advances in precision breeding are redefining the possibilities for resilient peanut improvement. Multi-omics platforms genomics, transcriptomics, proteomics, and metabolomics have accelerated the identification of resistance loci, effector-triggered immune components, and molecular cross-talk between pathogen, pest, and host responses. Genome editing tools such as CRISPR-Cas systems now enable the precise modification of susceptibility genes and defense regulators, overcoming barriers of conventional breeding. Integration of these molecular innovations with phenomics, machine learning, and remote sensing has transformed resistance screening from manual assessment to real-time, data-driven prediction. Such AI-assisted breeding pipelines promise enhanced selection accuracy and faster deployment of multi-stress-tolerant cultivars. This review outlines current progress, technological frontiers, and persisting gaps in leveraging precision breeding for disease and pest resistance in peanut, outlining a roadmap toward climate-resilient, sustainable production systems. Full article

Globally, Spodoptera frugiperda is a major threat to many important crops, including maize, rice, and cotton, causing significant economic damage. To control this invasive pest, environmentally friendly pest control techniques, including pheromone detection and identification of potential molecular targets to disrupt S. frugiperda mating communication, are needed. Female moths biosynthesize pheromones and emit them from the pheromone gland, which significantly depends on the intrinsic factors of the moth. Male S. frugiperda have a sophisticated olfactory circuit on their antennae that recognizes pheromone blends via olfactory receptor neurons (ORNs). With its potential to significantly modify the insect genome, CRISPR/Cas9 offers a revolutionary strategy to control this insect pest. The impairing physiological behaviors and disrupting the S. frugiperda volatile-sensing mechanism are the main potential applications of CRISPR/Ca9 explored in this review. Furthermore, the release of mutant S. frugiperda for their long-term persistence must be integral to the adoption of this technology. Looking forward, CRISPR/Cas9-based gene drive systems have the potential to synergistically target pheromone signaling pathways in S. frugiperda by disrupting pheromone receptors and key biosynthesis genes, thereby effectively blocking intraspecific communication and reproductive success. In conclusion, CRISPR/Cas9 provides an environmentally friendly and revolutionary platform for precise, targeted pest management in S. frugiperda. Full article

This study presents a comprehensive analysis of farming-awareness campaigns aimed at enhancing crop pest management through the strategic deployment of infected pests as a biological control mechanism. Additionally, the role of nutrient supplementation is examined within these campaigns to facilitate crop recovery and improve overall agricultural yield. A mathematical model is developed and rigorously analyzed to assess the efficacy of these integrated pest control strategies. The model is investigated with a focus on equilibrium states, stability analysis, and the conditions leading to Hopf bifurcation. Furthermore, optimal control theory is employed to optimize the release of infected pests, ensuring maximum crop yield while maintaining ecological balance. Our study not only underscores the critical influence of nutrient supplementation in augmenting crop productivity but also highlights the risk of excessive nutrient application, which may destabilize the system. These results emphasize the necessity of maintaining an optimal nutrient threshold. By integrating farming-awareness campaigns with precise biological control measures and nutrient management, our study establishes a robust framework for sustainable pest mitigation and agricultural productivity enhancement. The findings suggest that the synergistic application of infected pests and nutrient enrichment not only suppresses pest populations but also enhances crop resilience and productivity. Full article

Big data and artificial intelligence technologies are driving a paradigm shift in smart farming, yet intelligent decision-making faces critical bottlenecks. At the data level, challenges include fragmentation, high acquisition costs, and inadequate secure sharing; at the model level, issues involve regional heterogeneity, weak adaptability, and insufficient explainability. To address these, this paper systematically reviews global research to establish a theoretical framework spanning the entire production cycle. Regarding data governance, trends favor federated systems with unified metadata and layered storage, utilizing technologies like federated learning for secure lifecycle management. For decision-making, approaches are evolving from experience-based to data-driven intelligence. Pre-harvest planning now integrates mechanistic models and transfer learning for suitability and variety optimization. In-season management leverages deep reinforcement learning (DRL) and model predictive control (MPC) for precise regulation of seedlings, water, fertilizer, and pests. Post-harvest evaluation strategies utilize spatio-temporal deep learning architectures (e.g., Transformers or LSTMs) and intelligent optimization algorithms for yield prediction and machinery scheduling. Finally, a staged development pathway is proposed: prioritizing standardized data governance and foundation models in the short term; advancing federated learning and human–machine collaboration in the mid-term; and achieving real-time, ethical edge AI in the long term. This framework supports the transition toward precise, transparent, and sustainable smart agriculture. Full article

This study aims to develop a novel high-efficiency lure for Tomicus yunnanensis Existing bark beetle attractants often rely on single or fixed-ratio blends of host volatiles and their oxidation products, which struggle to mimic the dynamic release process of insect semiochemicals in nature. To address this, we established a dynamic reaction system based on the catalytic oxidation of α-pinene: ① background control (no catalyst, no heating), ② thermal oxidation system (no catalyst, 40 °C), and ③ catalytic oxidation system (with a titanium–copper modified chabazite-type zeolite catalyst, 40 °C). Behavioral screening using a Y-tube olfactometer revealed a clear gradient in attraction effectiveness among the three systems: catalytic oxidation > thermal oxidation > background control. The products from the catalytic oxidation system at 2 h of reaction showed the highest efficacy, achieving an attraction rate of 61%, which was significantly superior to the α-pinene control. These results indicate that generating dynamically proportioned volatile mixtures through catalytic oxidation can significantly enhance the attraction of T. yunnanensis Further analysis by gas chromatography–mass spectrometry (GC-MS) demonstrated that the catalyst efficiently promoted the directional conversion of α-pinene into key bioactive compounds such as verbenol, myrtenal, and myrtenone, thereby substantially improving behavioral activity. After field validation, this dynamically released attractant could potentially be developed into a real-time field-release lure system for monitoring adult emergence and large-scale trapping, providing a feasible new technological pathway for the precise and sustained management of bark beetle pests. Full article

Grain crops are regarded as fundamental to China’s agricultural production and food security. Effective control of nocturnal phototactic pests is essential for ensuring crop yields and achieving sustainable agricultural development. However, traditional solar insecticidal lamps often suffer from low energy utilization efficiency, dynamic switching control schemes, and poor adaptability in multi-pest coexistence scenarios. A multi-period intelligent switching control optimization scheme based on integrating a multi-pest phototactic rhythm is proposed, focusing on Cnaphalocrocis medinalis and Chilo suppressalis in rice fields. By considering the phototactic behavioral rhythm, energy consumption patterns, and residual energy levels, the proposed scheme dynamically optimizes the switching cycles of solar insecticidal lamps to maximize pest control effectiveness and energy efficiency. The rhythm modeling approach and dynamic adjustment mechanisms are employed to accurately align insecticidal working hours with varying pest activity patterns, thereby improving the pest control effectiveness of IoT-based solar insecticidal lamps. Simulation experiments demonstrate that, compared to traditional switching control schemes, the dynamic switching control scheme improves the average insecticidal rate by 17.7%, increases the effective insecticidal energy efficiency value by approximately 66.1%, and enhances the energy utilization rate by about 38.5%. The proposed dynamic switching control and intelligent energy management scheme not only improves the precision of pest control and energy utilization but also promotes the more efficient application of networked solar insecticidal lamps in smart agriculture. This work provides theoretical support and practical reference for intelligent pest control in complex agricultural environments, promoting the precision and sustainability of pest management practices. Full article

Brassica juncea (L.) Coss. var. foliosa Bailey contains high glucosinolate (GSL) levels that define its flavor and defense properties. However, the regulatory mechanisms controlling GSL biosynthesis in Brassica crops remain unclear. Here, four MYB29 homologs were identified in allotetraploid Brassica juncea. These BjuMYB29 proteins localize to the nucleus and possess transcriptional activation activity. Evolutionary analysis suggests polyploidization-driven expansion of MYB genes contributed to GLS diversification in Brassica species. Expression profiling showed distinct spatiotemporal and herbivory-responsive patterns among BjuMYB29 homologs. Heterologous expression of BjuA03.MYB29 and BjuA10.MYB29 in Arabidopsis enhanced insect resistance via GSL accumulation. Although both homologs upregulate aliphatic GSL biosynthetic genes, they differentially regulate indolic GSLs, with BjuA03.MYB29 suppressing and BjuA10.MYB29 enhancing their accumulation, potentially through differential control of CYP79B2. These results reveal subfunctionalization among MYB29 homologs in GSL regulation. This functional diversification of MYB29 homologs offers novel targets for precision breeding of Brassica crops with customized GSL profiles to optimize pest resistance and nutritional quality. 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

The Spodoptera frugiperda (fall armyworm), a globally significant agricultural pest, poses severe threats to crop production. Accurate identification of larval instar stages is crucial for implementing precise control measures and reducing pesticide use. However, traditional identification methods suffer from low efficiency and heavy reliance on expert knowledge, while existing deep learning models still face challenges such as insufficient feature extraction and high computational complexity in fine-grained instar classification. To address these issues, this study proposes a novel network model, termed Multi-Scale Improved Self-Attention ResNet (MSA-ResNet), which integrates large convolutional kernels (LCK), atrous spatial pyramid pooling (ASPP), and an improved self-attention (ISA) mechanism into the ResNet50 backbone. These enhancements enable the model to more effectively capture and discriminate subtle morphological details of larvae. Experiments conducted on a self-constructed dataset comprising 24,179 images across six instar stages demonstrate that MSA-ResNet achieves an accuracy of 96.81% on the test set, significantly outperforming mainstream models such as ResNet50, VGG16, and MobileNetV3. In particular, the precision for the first instar increased by 12.94%, while the recall rates for the second and fourth instars improved by 16% and 8.97%, respectively. Ablation studies further validate the effectiveness of each module and the optimal embedding strategy. This research presents a high-precision and efficient intelligent solution for larval instar identification of S. frugiperda, offering a transferable reference for fine-grained image recognition tasks in agricultural pest management. Full article

Soil disinfection is of great significance in reducing soil pests and weeds, overcoming the problem of crop continuous cropping obstacles, and ensuring the quality and safety of agricultural products. Soil flame disinfection technology, as a supplementary soil disinfection method that can be incorporated into an integrated plant protection system, has attracted much attention in recent years due to its characteristics of low resistance, greenness, environmental friendliness, and high efficiency. However, soil flame disinfection can also have a certain impact on soil organic matter and microbial communities, which is a core challenge that limits the promotion of flame disinfection technology. Clarifying the mechanism and temperature distribution of flame disinfection, accurately controlling flame disinfection parameters, can not only kill harmful organisms in soil, but also minimize damage to soil organic matter and microbial communities is the current research focus. This paper presents a comprehensive summary and discussion of the research progress regarding the mechanism of soil flame disinfection technology, the distribution of temperature fields, and the precise control technology for disinfection machines. It thoroughly elaborates on the efficacy of flame in eliminating typical soil-borne diseases and pests, the destructive impact of flame on soil organic matter and beneficial microbial communities, as well as the current status of research and development on soil flame disinfection devices. Additionally, it explores the pressing technical challenges that remain to be addressed. The article then discusses the future market prospects of soil flame disinfection equipment, focusing on key technological breakthroughs and opportunities, providing theoretical support for the next research, optimization and promotion of soil flame disinfection technology. Full article

Precise monitoring of rubber plantations is critical for effective management and ecological assessments, enabling optimal resource allocation, disease detection, and mitigation of environmental impacts. This study integrated multi-source remote sensing data—including Landsat 8, Sentinel-1/2, GaoFen-1 (GF-1) optical and SAR imagery, and DEM data of Hainan Island. The rubber plantation areas from 2021 to 2025 were extracted from the Google Earth Engine (GEE) platform by employing a multi-step threshold segmentation method, which utilized the Otsu algorithm to automatically determine optimal thresholds for distinguishing rubber plantations from other land covers. The overall accuracy of the extracted rubber plantations in this study was above 90%; the Kappa coefficient was greater than 0.85; and the F1-score surpassed 0.93. The resulting distribution maps reveal that rubber plantations on Hainan Island are predominantly concentrated in the northwestern and northern regions. The rubber plantation area of Hainan Island remained relatively stable from 2021 to 2023. During 2023–2024, the rubber plantation area experienced a decline. This reduction was particularly pronounced in 2024, when the area decreased by nearly 150 km² compared to the previous year. However, in 2025, this downward trend reversed sharply with an increase of approximately 300 km². These findings provide a critical scientific basis for sustainable rubber production, supporting informed decision-making in irrigation, pest control, and yield optimization. Furthermore, they offer valuable insights for strategic planning to balance economic returns with ecological conservation, thereby ensuring the long-term viability of the industry. Full article

In Saudi Arabia, coffee (Coffea arabica L.) has been grown for centuries on the mountain terraces of the southwestern regions. Jazan region accounts for about 80% of the total production. The acreage allocated to coffee is comparatively small but it is expanding rapidly thanks to a strong government-supported drive to increase local coffee production. Despite the initial success, the effort is hampered by the limited water supply available for irrigating the new plantings and the increased incidence of pests and diseases. The magnitude of these natural handicaps appears to have increased as of late, apparently due to climate change (CC). This review examines strategies to mitigate the consequences of CC on the coffee sector through the implementation of precision agriculture (PA) techniques, with the focus on addressing the challenges posed by biotic and abiotic stresses. The impact of CC is both direct by rendering present growing regions unsuitable and indirect by amplifying the severity of biotic and abiotic tree stressors. Precision agriculture (PA) techniques can play a key role in tackling these challenges through data-driven tools like sensors, GIS, remote sensing, machine learning and smart equipment. By monitoring soil, climate, and crop conditions, PA enables targeted irrigation, fertilization, and pest control thus improving efficiency and sustainability. This approach reduces costs, conserves resources, and minimizes environmental impact, making PA essential for building climate-resilient and sustainable coffee production systems. The review synthesizes insights from case studies, research papers, and other scientific literature concerned with precision farming practices and their effectiveness in alleviating biotic and abiotic pressures on coffee trees. Additionally, it evaluates technological advances, identifies existing knowledge gaps, and suggests areas for future research. Ultimately, this study seeks to contribute to enhancing the resilience of coffee farming in Saudi Arabia amidst ongoing CC challenges by educating farmers about the potential of PA technologies. Full article