Search Results (383)

The use of chemical pesticides in agriculture has led to the development of resistant pest populations, posing a challenge to long-term pest management. This review aims to evaluate the scientific literature on the individual and combined use of baculoviruses with conventional chemical and biological insecticides to combat Plutella xylostella, Spodoptera exigua, and Spodoptera frugiperda in broccoli, tomato, and maize crops. Notable findings include that both individual Plutella xylostella nucleopolyhedrovirus (PxNPV) and the combination of Plutella xylostella granulovirus (PxGV) and azadirachtin at a low dose effectively control Plutella xylostella; both combinations of Spodoptera exigua multiple nucleopolyhedrovirus (SeMNPV) with emamectin benzoate and chlorfenapyr reduced resistance in Spodoptera exigua and increased the efficacy of the insecticides; and the combination of Spodoptera frugiperda nucleopolyhedrovirus (SfMNPV) and spinetoram is effective against Spodoptera frugiperda. Integrating baculoviruses into pest management strategies offers a promising approach to mitigate the adverse effects of chemical pesticides, such as resistance development, health risks, and environmental damage. However, there remains a broad spectrum of research opportunities regarding the use of baculoviruses in agriculture. Full article

Efficient detection and localization of maize seedlings in complex field environments is essential for accurate plant segmentation and subsequent three-dimensional morphological reconstruction. To overcome the limited accuracy and high computational cost of existing models, we propose an enhanced architecture named MaizeStar-YOLO. The redesigned backbone integrates a novel C2F_StarsBlock to improve multi-scale feature fusion, while a PKIStage module is introduced to enhance feature representation under challenging field conditions. Evaluations on a diverse dataset of maize seedlings show that our model achieves a mean average precision (mAP) of 92.8%, surpassing the YOLOv8 baseline by 3.6 percentage points, while reducing computational complexity to 3.0 GFLOPs, representing a 63% decrease. This efficient and high-performing framework enables precise plant–background segmentation and robust three-dimensional feature extraction for morphological analysis. Additionally, it supports downstream applications such as pest and disease diagnosis and targeted agricultural interventions. Full article

To solve the challenges of low detection accuracy of maize pests and diseases, complex detection models, and difficulty in deployment on mobile or embedded devices, an improved YOLOv8 algorithm was proposed. Based on the original YOLOv8n, the algorithm replaced the Conv module with the DSConv module in the backbone network, which reduced the backbone network parameters and computational load and improved the detection accuracy at the same time. Additionally, BiFPN was introduced to construct a bidirectional feature pyramid structure, which realized efficient information flow and fusion between different scale features and enhanced the feature fusion ability of the model. At the same time, the Wise-IoU loss function was combined to optimize the training process, which improved the convergence speed and regression accuracy of the loss function. The experimental results showed that the precision, recall, and mAP0.5 of the improved algorithm were improved by 1.4, 1.1, and 1.5%, respectively, compared with YOLOv8n, and the model parameters and computational costs were reduced by 6.6 and 7.3%, respectively. The experimental results demonstrate the effectiveness and superiority of the improved YOLOv8 algorithm, which provides an efficient, accurate, and easy-to-deploy solution for maize leaf pest detection. Full article

Spodoptera frugiperda is a major agricultural pest worldwide, causing significant economic loss to maize crops. Its control largely depends on synthetic pesticides, which contribute to resistance development, harm non-target organisms, and lead to environmental degradation. Essential oils and their main components offer a more sustainable and ecologically sound alternative for the management of S. frugiperda. This study evaluated the effects of selected essential oils and their bioactive compounds on the survival and behavior of S. frugiperda using toxicity and preference assays. Peppermint essential oil and its major constituent, pulegone, significantly reduced the survival of S. frugiperda, with effects similar to those caused by synthetic insecticides. Eucalyptus essential oil and its main compound, 1,8-cineole, also influenced the behavior of S. frugiperda, suggesting potential for use in repellents. These findings highlight the use of essential oils and their main constituents/active constituents as bioinsecticides and their integration into environmentally friendly pest management strategies. Full article

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive pest that poses serious threats and causes significant losses to the production of maize in China. This study evaluated the feeding and oviposition preferences of S. frugiperda when reared on four maize cultivars—sweet, waxy, common, and silage—across three consecutive generations. It also compared population adaptability among these cultivars and analyzed population parameters between the F1 and F3 generations. The findings revealed that all four F1 generation populations showed a preference for feeding and oviposition on sweet maize. However, over time, S. frugiperda exhibited a stronger preference, in terms of feeding and oviposition behaviors, for the natal host plant across three consecutive generations of rearing. The fall armyworm completed its life cycle and oviposited on all four maize varieties over three generations. The sweet cultivar population had the highest intrinsic rate of increase, finite rate of increase, net reproductive rate, larval survival rate, pupation rate, eclosion rate, fecundity, and pupal weight, while the silage cultivar population had the shortest larval stage, pre-adult stage, and adult lifespan and the pupal weight and the fecundity were the lowest. Overall, the population fitness was the highest on the sweet cultivar, and the lowest on the silage cultivar. Compared with F1, the F3 generation of the FAW had a significantly shorter developmental duration in four maize cultivars. Except for the waxy maize cultivars, the fecundity of the other three cultivars did not differ significantly between F1 and F3. This study provides fundamental information on the trend of fall armyworm population changes in maize fields and serves as a reference for rational maize cultivar planting decisions. Full article

The efficacy of two selected Metarhizium rileyi Mexican isolates (T9-21 and L8-22) against Spodoptera frugiperda was evaluated under greenhouse conditions. To this end, a suspension (1 × 10⁸ conidia/mL) of these isolates was sprayed on maize plants previously infested with six second-instar larvae. No significant differences were observed between the survival curves of the T9-21 and L8-22 isolates. Cadaver sporulation was significantly higher, and the lethal time was significantly lower with the T9-21 isolate compared with those of the L8-22 isolate (97% and 8 days vs. 70% and 10 days, respectively). Based on these results, a small-scale field trial on maize was performed to evaluate the degree of pest control achieved by the T9-21 isolate and compare it with the insecticide spinetoram, applied at a rate of 1 × 10¹³ conidia/ha and 75 mL/ha, respectively. No significant differences were observed in the proportion of larval mortality between the T9-21 isolate (0.49) and spinetoram (0.72). However, spinetoram significantly reduced natural enemies and phytophagous insect populations compared with the fungus and the control. In conclusion, M. rileyi T9-21 isolate could be a promising alternative for the control of S. frugiperda larvae. Full article

Integrating organic soil amendments such as vermicompost (VC) and vermicompost tea (VCT) in agriculture has received increasing attention as a sustainable strategy to improve soil fertility, enhance plant growth, and suppress pest infestations. This study aimed to evaluate the effects of varying concentrations of VCT (10%, 20%, and 40%), alone and in combination with VC (2.47 ton/ha), on the development and yield of corn (Zea mays), and suppression of fall armyworm (FAW, Spodoptera frugiperda) infestation. The experiment was conducted in seven raised beds with seven treatments: V0 (control), VCT10, VCT20, VCT40, VC1 + VCT10, VC1 + VCT20, and VC1 + VCT40. Six weekly applications of VCT were applied starting at the V2 stage, and soil and plant nutrient contents were determined post-harvest. Additionally, relative chlorophyll content, height, cob yield, dry biomass, and FAW infestations were assessed. Results show that both VC and VCT significantly enhanced soil nutrient content compared to the control treatment (V0). VCT20 and VC1 + VCT10 improved plant N, K, and micronutrient uptake. Corn treated with VCT10 and VC1 + VCT10 had the highest biomass (6.52 and 6.57 tons/ha, respectively), while VCT20 produced the highest cob yield (6.0 tons/ha), which was more than eight times that of V0. SPAD values and corn height were significantly high across all treatments, with VCT20 achieving the highest SPAD readings while the control achieved the lowest. For FAW infestation, the control treatment experienced moderate infestation. At the same time, there was complete suppression in VCT20 and VCT40 treatments and a reduction in VC + VCT treatments, likely due to the bioactive compounds and beneficial microbes in VC and VCT that strengthened plant immunity. The results suggest that VCT20 is a cost-effective, eco-friendly amendment for improving corn performance and FAW resistance. This study contributes to sustainable agriculture by demonstrating how organic amendments can enhance crop resilience while supporting environmentally friendly farming practices. Full article

Agriculture in India supports millions of livelihoods and is a major force behind economic expansion. Challenges in modern agriculture depend on environmental factors (such as soil quality and climate variability) and biotic factors (such as pests and diseases). These challenges can be addressed by advancements in technology (such as sensors, internet of things, communication, etc.) and data-driven approaches (such as machine learning (ML) and deep learning (DL)), which can help with crop yield and sustainability in agriculture. This study introduces an innovative deep neural network (DNN) approach for identifying leaf diseases in paddy crops at an early stage. The proposed neural network is a hybrid DL model comprising feature extraction, channel attention, inception with residual, and classification blocks. Channel attention and inception with residual help extract comprehensive information about the crops and potential diseases. The classification module uses softmax to obtain the score for different classes. The importance of each block is analyzed via an ablation study. To understand the feature extraction ability of the modules, extracted features at different stages are fed to the SVM classifier to obtain the classification accuracy. This technique was experimented on eight classes with 7857 paddy crop images, which were obtained from local paddy fields and freely available open sources. The classification performance of the proposed technique is evaluated according to accuracy, sensitivity, specificity, F1 score, MCC, area under curve (AUC), and receiver operating characteristic (ROC). The model was fine-tuned by setting the hyperparameters (such as batch size, learning rate, optimizer, epoch, and train and test ratio). Training, validation, and testing accuracies of 99.91%, 99.87%, and 99.49%, respectively, were obtained for 20 epochs with a learning rate of 0.001 and sgdm optimizer. The proposed network robustness was studied via an ablation study and with noisy data. The model’s classification performance was evaluated for other agricultural data (such as mango, maize, and wheat diseases). These research outcomes can empower farmers with smarter agricultural practices and contribute to economic growth. Full article

The African armyworm Spodoptera exempta (Lepidoptera: Noctuidae) is a significant pest that affects cereal crops and pasture grasses in sub-Saharan Africa. This migratory pest causes extensive defoliation, which can result in significant yield losses, particularly in maize. This review focuses on the recent outbreaks of the African armyworm and identifies key factors contributing to its success across sub-Saharan Africa. Understanding these factors is essential for developing effective and sustainable pest management strategies. This review emphasizes the importance of innovative technologies and data-driven approaches in managing pest outbreaks and underscores the need to implement technology-enabled integrated pest management (IPM) strategies to control the African armyworm effectively. Full article

The fall armyworm (Spodoptera frugiperda) is a highly invasive agricultural pest that has caused significant damage to maize and other crops since its initial detection in China in 2019. Understanding its invasion dynamics, migration patterns, genetic diversity, and overwintering capacity is crucial for developing effective pest management strategies. This study investigates these aspects in Shaanxi Province, a critical transitional zone between northern and southern climates in China, from 2019 to 2023. We conducted field surveys in six cities across Shaanxi to monitor the initial infestation of FAW. Migration trajectories were simulated using the HYSPLIT model, integrating pest occurrence data and meteorological information. Genetic analyses were performed on 113 FAW individuals from 12 geographical populations using mitochondrial COI and nuclear Tpi genes. Additionally, an overwintering experiment was conducted to assess the survival of FAW pupae under local winter conditions. The first detection dates of FAW in Shaanxi showed significant interannual variation, with a trend of delayed infestation each year. Three primary migration routes into Shaanxi were identified, originating from Sichuan, Hubei-Chongqing, and Henan. Genetic analysis revealed a predominance of the rice-strain FAW in Shaanxi, with some corn-strain variants in northern regions. The overwintering experiment indicated that FAW pupae could not survive the winter in Shaanxi, suggesting that the region does not support year-round breeding of this pest. This study provides comprehensive insights into the spatiotemporal dynamics and migration patterns of FAW in Shaanxi. The findings highlight the importance of integrated pest management approaches, including monitoring migration routes and genetic diversity, to develop targeted control measures. The inability of FAW to overwinter in Shaanxi suggests that regional climate conditions play a significant role in limiting its year-round presence, which is valuable information for designing early warning systems and sustainable pest management strategies. Full article

Effective stored-grain insect pest detection is crucial in grain storage management to prevent economic losses and ensure food security throughout production and supply chains. Existing detection methods suffer from issues such as high labor costs, environmental interference, high equipment costs, and inconsistent performance. To address these limitations, we proposed PDA-YOLO, an improved stored-grain insect pest detection algorithm based on YOLO11n which integrates three key modules: PoolFormer_C3k2 (PF_C3k2) for efficient local feature extraction, Attention-based Intra-Scale Feature Interaction (AIFI) for enhanced global context awareness, and Dynamic Multi-scale Aware Edge (DMAE) for precise boundary detection of small targets. Trained and tested on 6200 images covering five common stored-grain insect pests (Lesser Grain Borer, Red Flour Beetle, Indian Meal Moth, Maize Weevil, and Angoumois Grain Moth), PDA-YOLO achieved an mAP@0.5 of 96.6%, mAP@0.5:0.95 of 60.4%, and F1 score of 93.5%, with a computational cost of only 6.9 G and mean detection time of 9.9 ms per image. These results demonstrate the advantages over mainstream detection algorithms, balancing accuracy, computational efficiency, and real-time performance. PDA-YOLO provides a reference for pest detection in intelligent grain storage management. Full article

The northeast region of China plays a crucial role in crop production. The leaf beetle Monolepta hieroglyphica (Motschulsky, 1858) (Coleoptera: Chrysomelidae) has emerged as a potential threat to food security in the region. With a wide distribution spanning Asia and Russia, this beetle affects various crops. However, limited information is available regarding its occurrence patterns and genetic diversity among major crops in the region. Based on systematic observations across various hosts, coupled with genetic variation analysis using mitochondrial DNA markers, the main results were as follows. Leaf beetle occurrence varied among hosts, peaking from late July to mid-August, with maize and soybean fields exhibiting higher infestation rates compared with other crops. Notably, late-cultivated maize fields harbored the highest beetle numbers due to the species’ preference for young leaves. The host transfer trajectory may have originated in soybean and weeds, with subsequent alternation between host plants and other crops, before the final migration to cabbage and late-cultivated maize fields. Genetic analysis revealed nine COI haplotypes, four COII haplotypes, eleven Cytb haplotypes, and twenty-one combined haplotypes. No clear relationship existed between genetic diversity and occurrence, and no distinct host-based genetic patterns emerged from neighbor-joining tree and haplotype network analyses. High gene flow rates were observed, likely contributing to decreased genetic variation. An analysis of molecular variance results indicated major genetic variation within populations, although genetic distance and haplotype distribution indicated divergence among host populations. These results provide foundational data for developing effective M. hieroglyphica pest management strategies. Full article

Spodoptera frugiperda, a major global agricultural pest, poses significant challenges to chemical control methods due to pesticide resistance and environmental concerns, underscoring the need for sustainable management strategies. Attractants based on host plant volatiles offer a promising eco-friendly approach, but their development for S. frugiperda is hindered by limited research on host recognition mechanisms. This study reveals that female S. frugiperda preferentially oviposit on maize at the seedling stage. Using electrophysiological techniques, we identified p-xylene and (+)-camphor from seedling-stage maize volatiles as key compounds eliciting strong responses in female S. frugiperda. Behavioral assays confirmed that these compounds (p-xylene at the concentration of 5%, 10%, and 20% and (+)-camphor at 1%, 5%, and 10%) significantly attract females, establishing them as the key odor cues for host selection. Moreover, these volatiles are more abundant in seedling-stage maize, suggesting that S. frugiperda assesses maize growth stages based on their concentrations. Importantly, larvae reared on seedling-stage maize exhibited higher survival rates than those on later-stage maize, indicating that oviposition site selection directly affects offspring fitness. These findings demonstrate that S. frugiperda uses p-xylene and (+)-camphor to evaluate maize development and select suitable oviposition sites, thereby enhancing larval survival. This study provides a foundation for developing targeted attractants for S. frugiperda and highlights the seedling stage as a critical period for implementing pest control measures, particularly in autumn maize production, given the higher pest population density during this phase. Full article

Climate change poses a significant threat to Ethiopian agriculture, impacting both cereal and livestock production through rising temperatures, erratic rainfall, prolonged droughts, and increased pest and disease outbreaks. These challenges intensify food insecurity, particularly for smallholder farmers and pastoralists who rely on climate-sensitive agricultural systems. This systematic review aims to synthesize the impacts of climate change on Ethiopian agriculture, with a specific focus on cereal production and livestock feed quality, while exploring effective adaptation strategies that can support resilience in the sector. The review synthesizes 50 peer-reviewed publications (2020–2024) from the Climate Change Effects on Food Security project, which supports young African academics and Higher Education Institutions (HEIs) in addressing Sustainable Development Goals (SDGs). Using PRISMA guidelines, the review assesses climate change impacts on major cereal crops and livestock feed in Ethiopia and explores adaptation strategies. Over the past 30 years, Ethiopia has experienced rising temperatures (0.3–0.66 °C), with future projections indicating increases of 0.6–0.8 °C per decade resulting in more frequent and severe droughts, floods, and landslides. These shifts have led to declining yields of wheat, maize, and barley, shrinking arable land, and deteriorating feed quality and water availability, severely affecting livestock health and productivity. The study identifies key on-the-ground adaptation strategies, including adjusted planting dates, crop diversification, drought-tolerant varieties, soil and water conservation, agroforestry, supplemental irrigation, and integrated fertilizer use. Livestock adaptations include improved breeding practices, fodder enhancement using legumes and local browse species, and seasonal climate forecasting. These results have significant practical implications: they offer a robust evidence base for policymakers, extension agents, and development practitioners to design and implement targeted, context-specific adaptation strategies. Moreover, the findings support the integration of climate resilience into national agricultural policies and food security planning. The Climate Change Effects on Food Security project’s role in generating scientific knowledge and fostering interdisciplinary collaboration is vital for building institutional and human capacity to confront climate challenges. Ultimately, this review contributes actionable insights for promoting sustainable, climate-resilient agriculture across Ethiopia. Full article

In Africa, the current harmful maize pest is Spodoptera frugiperda. Its attack can be severe and cause total economic losses. Spodoptera eridania is another species of the same genus, detected a few months after S. frugiperda’s outbreaks in West and Central Africa. Though both species share a range of host plants, socioeconomic studies are yet to provide specific figures on the potential impacts of S. eridania. The high and inappropriate application of insecticides to control Spodoptera species has negative effects on the environmental elements’ health. Semiochemical tools are increasingly exploited to design alternative pest management strategies. We hypothesize that host plants release components used by the pests and a shared parasitoid to locate the host. To verify that hypothesis, we conducted behavioral assays and GC-MS analyses to identify the potential chemical signals involved in the communications of the moths and their shared parasitoid C. icipe. The results showed that healthy and herbivory-induced maize and amaranth produced some chemical compounds including α-pinene, limonene, isopentyl acetate, (Z)-beta-farnesene, and methyl dodecanoate, which prospects their potential use in alternative pest management strategies for recruiting C. icipe to control these pests. Further work will focus on field validation to develop an alternative control strategy for the moths. Full article