Search Results (1,091)

Weed management remains a critical challenge in the U.S. container ornamental production industry, where weeds not only compete with crops for limited resources but also harbor pests and pathogens, thereby diminishing plant quality and marketability. The paper explores the economic impact of weed infestations, herbicide resistance development, and the limited availability of selective herbicides for ornamental crops in the United States. This review synthesizes current chemical weed control tactics, focusing not only on both preemergence and postemergence herbicides commonly used in ornamental nurseries, but also organic alternatives and integrated weed management (IWM) approaches as complementary strategies by evaluating their effectiveness, crop safety, and usage. There is a critical need for research in the areas of alternative chemical options such as insecticides, miticides (e.g., Zerotol and Tetra Curb Max), and organic products for liverwort control in greenhouses. Although essential oils and plant-based extracts show some potential, their effectiveness and practical use remain largely unexplored. Full article

Climate change is generally having a negative impact on forest health by inducing drought stress and favouring the spread of pest species, such as bark beetles. The terrestrial monitoring of bark beetle infestation is very time-consuming, especially in the early stages, and therefore not feasible for extensive areas, emphasising the need for a comprehensive approach based on remote sensing. Although numerous studies have researched the use of optical data for this task, radar data remains comparatively underexplored. Therefore, this study uses the weekly and cloud-free acquisitions of Sentinel-1 in the Bavarian Forest National Park. Time series analysis within a Multi-SAR framework using Random Forest enables the monitoring of moisture content loss and, consequently, the assessment of tree vitality, which is crucial for the detection of stress conditions conducive to bark beetle outbreaks. High accuracies are achieved in predicting future bark beetle infestation (R² of 0.83–0.89). These results demonstrate that forest vitality trends ranging from healthy to bark beetle-affected states can be mapped, supporting early intervention strategies. The standard deviation of 0.44 to 0.76 years indicates that the model deviates on average by half a year, mainly due to the uncertainty in the reference data. This temporal uncertainty is acceptable, as half a year provides a sufficient window to identify stressed forest areas and implement targeted management actions before bark beetle damage occurs. The successful application of this technique to extensive test sites in the state of North Rhine-Westphalia proves its transferability. For the first time, the results clearly demonstrate the expected relationship between radar backscatter expressed in the Kennaugh elements K₀ and K₁ and bark beetle infestation, thereby providing an opportunity for the continuous and cost-effective monitoring of forest health from space. Full article

The potent and selective ‘genetic zipper’ method for insect pest control consists of three essential components: an antisense DNA (the finder), its complementary mature rRNA or pre-rRNA of the pest (the target), and the host’s endogenous DNA-guided rRNase (the degrader). Although this approach has been validated, the spectrum of effective rRNA targets remains insufficiently explored. In this study, we report for the first time the insecticidal efficacy of a novel oligonucleotide insecticide, Eriola-11, which targets the mitochondrial 16S rRNA of the woolly apple aphid Eriosoma lanigerum Hausmann. We hypothesized that the antisense-mediated silencing of mitochondrial rRNA would impair aphid viability and lead to physiological disruptions associated with mitochondrial energy metabolism. Eriola-11 was applied either once or twice (with a 24 h interval) to aphid-infested plants, and aphid mortality was recorded over 14 days. Mitochondrial 16S rRNA expression levels were quantified using molecular assays, and the degradation kinetics of Eriola-11 were assessed in aphid tissue homogenates. Results showed significant insecticidal activity, with 67.55% mortality after a single treatment and 83.35% after two treatments. Treated aphids exhibited the loss of their characteristic white woolly wax covering, and mitochondrial 16S rRNA expression was reduced 0.66-fold relative to the control. Additionally, Eriola-11 was fully degraded by aphid DNases from tissue homogenates within 3 h, highlighting its rapid biodegradability. These findings establish mitochondrial 16S rRNA as a viable target for antisense insecticides and expand the catalogue of potential rRNA-based targets, offering a promising avenue for environmentally sustainable pest control strategies. Full article

Tea cultivation faces significant threats from red spider mite (RSM: Oligonychus coffeae) infestations, which reduce yields and economic viability in major tea-producing regions. Current automated detection methods rely on supervised deep learning models requiring extensive labeled data, limiting scalability for smallholder farmers. This article proposes AIM-Net (AI-based Infestation Mapping Network) by evaluating SwAV (Swapping Assignments between Views), a self-supervised learning framework, for classifying RSM infestation severity (Mild, Moderate, Severe) using a geo-referenced, field-acquired dataset of RSM infested tea-leaves, Cam-RSM. The methodology combines SwAV pre-training on unlabeled data with fine-tuning on labeled subsets, employing multi-crop augmentation and online clustering to learn discriminative features without full supervision. Comparative analysis against a fully supervised ResNet-50 baseline utilized 5-fold cross-validation, assessing accuracy, F1-scores, and computational efficiency. Results demonstrate SwAV’s superiority, achieving 98.7% overall accuracy (vs. 92.1% for ResNet-50) and macro-average F1-scores of 98.3% across classes, with a 62% reduction in labeled data requirements. The model showed particular strength in Mild_RSM-class detection (F1-score: 98.5%) and computational efficiency, enabling deployment on edge devices. Statistical validation confirmed significant improvements (p < 0.001) over baseline approaches. These findings establish self-supervised learning as a transformative tool for precision pest management, offering resource-efficient solutions for early infestation detection while maintaining high accuracy. Full article

Leaf loss caused by pest infestations poses a serious threat to forest health. The leaf loss rate (LLR) refers to the percentage of the overall tree-crown leaf loss per unit area and is an important indicator for evaluating forest health. Therefore, rapid and accurate acquisition of the LLR via remote sensing monitoring is crucial. This study is based on drone hyperspectral and LiDAR data as well as ground survey data, calculating hyperspectral indices (HSI), multispectral indices (MSI), and LiDAR indices (LI). It employs Savitzky–Golay (S–G) smoothing with different window sizes (W) and polynomial orders (P) combined with recursive feature elimination (RFE) to select sensitive features. Using Random Forest Regression (RFR) and Convolutional Neural Network Regression (CNNR) to construct a multidimensional (horizontal and vertical) estimation model for LLR, combined with LiDAR point cloud data, achieved a three-dimensional visualization of the leaf loss rate of trees. The results of the study showed: (1) The optimal combination of HSI and MSI was determined to be W11P3, and the LI was W5P2. (2) The optimal combination of the number of sensitive features extracted by the RFE algorithm was 13 HSI, 16 MSI, and hierarchical LI (2 in layer I, 9 in layer II, and 11 in layer III). (3) In terms of the horizontal estimation of the defoliation rate, the model performance index of the CNNR_HSI model (MPI = 0.9383) was significantly better than that of RFR_MSI (MPI = 0.8817), indicating that the continuous bands of hyperspectral could better monitor the subtle changes of LLR. (4) The I-CNNR_HSI+LI, II-CNNR_HSI+LI, and III-CNNR_HSI+LI vertical estimation models were constructed by combining the CNNR_HSI model with the best accuracy and the LI sensitive to different vertical levels, respectively, and their MPIs reached more than 0.8, indicating that the LLR estimation of different vertical levels had high accuracy. According to the model, the pixel-level LLR of the sample tree was estimated, and the three-dimensional display of the LLR for forest trees under the pest stress of larch caterpillars was generated, providing a high-precision research scheme for LLR estimation under pest stress. Full article

Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a major pest of soybean fields. While high-temperature stress induced by global warming can initially suppress aphid populations, these pests may eventually adapt, leading to more severe infestations and crop damage. Heat shock proteins (HSPs), which are upregulated in response to heat stress to protect aphid development, also confer tolerance to other abiotic stressors, including insecticides. To investigate the role of HSPs in insecticide resistance in A. glycines, we analyzed the expression profiles of three AgHsp90 genes (AgHsp75, AgHsp83, and AgGrp94) following exposure to high temperatures and insecticides. Functional validation was performed using RNA interference (RNAi) to silence AgHsp90 genes. Our results demonstrated that AgHsp90 genes were significantly upregulated under both heat and insecticide stress conditions. Furthermore, after feeding on dsRNA of AgHsp90 genes, mortality rates of A. glycines significantly increased when exposed to imidacloprid and lambda-cyhalothrin. This study provides evidence that AgHsp90 genes play a crucial role in mediating thermal tolerance and insecticide resistance in A. glycines. Full article

The worldwide agriculture industry is facing increasing problems due to rapid population increase and increasingly unfavorable weather patterns. In order to reach the projected food production targets, which are essential for guaranteeing global food security, innovative and sustainable agricultural methods must be adopted. Conventional approaches, including traditional breeding procedures, often cannot handle the complex and simultaneous effects of biotic pressures such as pest infestations, disease attacks, and nutritional imbalances, as well as abiotic stresses including heat, salt, drought, and heavy metal toxicity. Applying phytohormonal approaches, particularly those involving hormonal crosstalk, presents a viable way to increase crop resilience in this context. Abscisic acid (ABA), gibberellins (GAs), auxin, cytokinins, salicylic acid (SA), jasmonic acid (JA), ethylene, and GA are among the plant hormones that control plant stress responses. In order to precisely respond to a range of environmental stimuli, these hormones allow plants to control gene expression, signal transduction, and physiological adaptation through intricate networks of antagonistic and constructive interactions. This review focuses on how the principal hormonal signaling pathways (in particular, ABA-ET, ABA-JA, JA-SA, and ABA-auxin) intricately interact and how they affect the plant stress response. For example, ABA-driven drought tolerance controls immunological responses and stomatal behavior through antagonistic interactions with ET and SA, while using SnRK2 kinases to activate genes that react to stress. Similarly, the transcription factor MYC2 is an essential node in ABA–JA crosstalk and mediates the integration of defense and drought signals. Plants’ complex hormonal crosstalk networks are an example of a precisely calibrated regulatory system that strikes a balance between growth and abiotic stress adaptation. ABA, JA, SA, ethylene, auxin, cytokinin, GA, and BR are examples of central nodes that interact dynamically and context-specifically to modify signal transduction, rewire gene expression, and change physiological outcomes. To engineer stress-resilient crops in the face of shifting environmental challenges, a systems-level view of these pathways is provided by a combination of enrichment analyses and STRING-based interaction mapping. These hormonal interactions are directly related to the United Nations Sustainable Development Goals (SDGs), particularly SDGs 2 (Zero Hunger), 12 (Responsible Consumption and Production), and 13 (Climate Action). This review emphasizes the potential of biotechnologies to use hormone signaling to improve agricultural performance and sustainability by uncovering the molecular foundations of hormonal crosstalk. Increasing our understanding of these pathways presents a strategic opportunity to increase crop resilience, reduce environmental degradation, and secure food systems in the face of increasing climate unpredictability. Full article

The brown planthopper (BPH), Nilaparvata lugens, is the most destructive insect pest of rice. BPH infestations severely threaten rice yield worldwide. The gustatory receptor NlGr23 plays a critical role in mediating the repulsive reaction to oxalic acid of the BPH. We integrated transcriptomic and proteomic analyses to determine the metabolic and behavioral consequences of NlGr23 silencing. The RNAi-mediated knockdown of NlGr23 increased body weight and honeydew production, indicating enhanced feeding activity. The results of multiomics profiling revealed disrupted lipid homeostasis, identifying 187 differentially expressed genes and 150 differentially expressed proteins. These genes were enriched in pathways including glycerophospholipid metabolism, fatty acid biosynthesis, and AMPK signaling. The results of biochemical assays showed that NlGr23 silencing elevated triacylglycerol levels by 68.83%, and reduced glycerol and free fatty acid levels, suggesting impaired lipolysis. The NlGr23 loss-of-function mutation mechanistically activates the AMPK pathway, suppresses lipid breakdown, and promotes energy storage. This study established NlGr23 as a key regulator linking chemosensation to metabolic reprogramming, providing new insights into gustatory receptor-mediated energy homeostasis in the BPH. Full article

Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes a novel biotic condition screening (BCS) model for the detection of corn leaf diseases and pests, called BCS_YOLO, based on the You Only Look Once version 11n (YOLOv11n). The proposed model enables accurate detection and classification of various corn leaf pathologies and pest infestations under challenging agricultural field conditions. It achieves this thanks to three key newly designed modules—a Self-Perception Coordinated Global Attention (SPCGA) module, a High/Low-Frequency Feature Enhancement (HLFFE) module, and a Local Attention Enhancement (LAE) module. The SPCGA module improves the model’s ability to perceive fine-grained targets by fusing multiple attention mechanisms. The HLFFE module adopts a frequency domain separation strategy to strengthen edge delineation and structural detail representation in affected areas. The LAE module effectively improves the model’s discrimination ability between targets and backgrounds through local importance calculation and intensity adjustment mechanisms. Conducted experiments show that BCS_YOLO achieves 78.4%, 73.7%, 76.0%, and 82.0% in $precision$, recall, $F1score$, and $mAP@50$, respectively, representing corresponding improvements of 3.0%, 3.3%, 3.2%, and 4.6% compared to the baseline model (YOLOv11n), while also outperforming the mainstream object detection models. In summary, the proposed BCS_YOLO model provides a practical and scalable solution for efficient detection of corn leaf diseases and pests in complex smart-agriculture scenarios, demonstrating significant theoretical and application value. Full article

The impact of insect pests on the yield and quality of rice is extremely significant, and accurate detection of insect pests is of crucial significance to safeguard rice production. However, traditional manual inspection methods are inefficient and subjective, while existing machine learning-based approaches still suffer from limited generalization and suboptimal accuracy. To address these challenges, this study proposes an improved rice pest detection model, MTD-YOLO, based on the YOLOv8 framework. First, the original backbone is replaced with MobileNetV3, which leverages optimized depthwise separable convolutions and the Hard-Swish activation function through neural architecture search, effectively reducing parameters while maintaining multiscale feature extraction capabilities. Second, a Cross Stage Partial module with Triplet Attention (C2f-T) module incorporating Triplet Attention is introduced to enhance the model’s focus on infested regions via a channel-patial dual-attention mechanism. In addition, a Dynamic Head (DyHead) is introduced to adaptively focus on pest morphological features using the scale–space–task triple-attention mechanism. The experiments were conducted using two datasets, Rice Pest1 and Rice Pest2. On Rice Pest1, the model achieved a precision of 92.5%, recall of 90.1%, mAP@0.5 of 90.0%, and mAP@[0.5:0.95] of 67.8%. On Rice Pest2, these metrics improved to 95.6%, 92.8%, 96.6%, and 82.5%, respectively. The experimental results demonstrate the high accuracy and efficiency of the model in the rice pest detection task, providing strong support for practical applications. Full article

Tomato (Solanum lycopersicum L.) is one of the most preferred hosts of polyphagous stink bugs (Hemiptera: Pentatomidae) and leaf-footed bugs (Hemiptera: Coreidae). These hemipterans can infest tomato fruits at all stages of fruit ripening. However, it is unclear whether there is any feeding preference for these true bugs among different ripening stages of tomato (green, breaker, pink, and red stages). Feeding and behavioral assays were performed to determine the feeding preference and damage potential of two common stink bugs—the brown marmorated stink bug (Halyomorpha halys (Stål)) and the southern green stink bug (Nezara viridula L.)—and a leaf-footed bug (Leptoglossus zonatus (Dallas)) among the various ripening stages of tomato. The results indicated that green is the most preferred ripening stage for N. viridula and L. zonatus, while pink tomatoes were found to be a more preferred feeding site for H. halys. Fully ripe red tomatoes were found to be the least preferred feeding site for all three insects. The findings of this study will be useful for developing fruit damage symptom-based monitoring programs and establishing economic threshold levels for these pests in tomatoes, as well as informing harvesting regimes. Full article

Aphis gossypii is a significant global pest that impacts numerous agricultural crops and vegetables, causing direct damage to food plants and indirect damage through the transmission of phytopathogenic viruses, primarily begomoviruses. In Panama, particularly in the Azuero region, viral infections transmitted by this aphid can affect a substantial share of tomato crops cultivated for industrial use. A traditional alternative to synthetic pesticides involves exploring plant extracts with insecticidal properties derived from wild plants found in our tropical forests, which can be easily prepared and applied by farmers. In this context, the present research aimed to evaluate the insecticidal activity of ethanolic extracts from the stems and leaves of Sphagneticola trilobata on both nymphs and adults of A. gossypii. Mortality was assessed at 24, 48, and 72 h after applying three doses of each extract (25, 50, and 100 µg/L). A standard phytochemical analysis to determine insecticidal activity revealed that both extracts exhibited significant efficacy at the highest concentration tested; however, the leaf extract demonstrated greater effectiveness at lower concentrations. A comprehensive metabolomic study indicated that the active compounds are diterpenes derived from the pimarenyl cation. These compounds have been extensively documented for their insecticidal potential against various insect species, suggesting that ethanolic extracts from this plant could serve as viable candidates for agricultural insecticides to combat aphid infestations. Full article

Tomicus yunnanensis Kirkendall and Faccoli, a native bark beetle species and key pest of Pinus yunnanensis Franch. in southwestern China, relies on host-derived volatile organic compounds (VOCs) for host selection. To unravel these mechanisms, we collected VOCs from P. yunnanensis trunks across four infestation stages (healthy, early-infested, weakened, near-dead) using dynamic headspace sampling. Chemical profiling via gas chromatography–mass spectrometry (GC-MS) identified 51 terpenoids, with α-pinene as the most abundant component. VOC profiles differed markedly between healthy and early-infested trees, while gradual shifts in compound diversity and abundance occurred from the weakened to near-dead stages. Bioactive compounds were screened using gas chromatography–electroantennographic detection (GC-EAD) and a Y-tube olfactometer. Electrophysiological responses in T. yunnanensis were triggered by α-pinene, β-pinene, 3-carene, 2-thujene, and 4-allylanisole. Behavioral tests revealed that α-pinene, 3-carene, and 2-thujene acted as attractants, whereas β-pinene and 4-allylanisole functioned as repellents. These results indicate that infestation-induced VOC dynamics guide beetle behavior, with attractants likely promoting host colonization during early infestation and repellents signaling deteriorating host suitability in later stages. By mapping these chemical interactions, our study identifies potential plant-derived semiochemicals for targeted pest management. Integrating these compounds with pheromones could enhance the monitoring and control strategies for T. yunnanensis, offering ecologically sustainable solutions for pine ecosystems. Full article

The sorghum aphid (Melanaphis sacchari (Zehntner, 1897)), a globally destructive pest, severely compromises sorghum yield and quality. This study compared aphid-resistant (HX133) and aphid-susceptible (HX37) sorghum (Sorghum bicolor (L.) Moench) cultivars, revealing that HX133 significantly suppressed aphid proliferation through repellent and antibiotic effects, while aphid populations increased continuously in HX37. Transcriptome analysis identified 2802 differentially expressed genes (DEGs, 45.9% upregulated) in HX133 at 24 h post-infestation, in contrast with only 732 DEGs (21% upregulated) in HX37. Pathway enrichment highlighted shikimate-mediated phenylpropanoid/flavonoid biosynthesis and glutathione metabolism as central to HX133’s defense response, alongside photosynthesis-related pathways common to both cultivars. qRT-PCR validation confirmed activation of the shikimate pathway in HX133, driving the synthesis of dhurrin—a cyanogenic glycoside critical for aphid resistance—and other tyrosine-derived metabolites (e.g., benzyl isoquinoline alkaloids, tocopherol). These findings demonstrate that HX133 employs multi-layered metabolic regulation, particularly dhurrin accumulation, to counteract aphid infestation, whereas susceptible cultivars exhibit limited defense induction. This work provides molecular targets for enhancing aphid resistance in sorghum breeding programs. Full article

The spotted wing drosophila, Drosophila suzukii is an injurious polyphagous pest threatening worldwide soft fruit production. Its high adaptability to new colonized environments, short life cycle, and wide host range are supporting its rapid spread. The most common techniques to reduce its significant economic damage are based on multiple insecticides applications per season, even prior to the harvest, which reduces agroecosystem biodiversity and affects human and animal health. Environmental concerns and regulatory restrictions on insecticide use are driving the need for studies on alternative biological control strategies. This study aimed to assess the effect of T. drosphilae in controlling D. suzukii infestations and its interaction with P. vindemiae, a secondary parasitoid naturally present in Apulia (South Italy). Field experiments were carried out in organic cherry orchards in Gioia del Colle (Bari, Italy) to test the efficacy and adaptability of T. drosphilae following weekly releases of artificially reared individuals. Additionally, the interaction between P. vindemiae and T. drosphilae was studied under laboratory conditions. Results from field experiments showed that D. suzukii populations were significantly lower when both parasitoids were present. However, T. drosophilae was less prone to adaptation, so its presence and parasitism were limited to the post-release period. Laboratory experiments, instead, confirmed the high reduction of D. suzukii populations when both parasitoids are present. However, the co-existence of the two parasitoids resulted in a reduced parasitism rate and offspring production, notably for T. drosophilae. This competitive disadvantage may explain its poor establishment in field conditions. These findings suggest that the field release of the two natural enemies should be carried out with reference to their natural population abundance to not generate competition effects. Full article