Search Results (76)

The wheat curl mite (WCM, Aceria tosichella Keifer), a complex of eriophyid mite species, transmits wheat streak mosaic virus (WSMV) and Triticum mosaic virus (TriMV), which in single or mixed infections cause wheat streak mosaic (WSM) disease—a major threat to wheat production across the U.S. Great Plains. Resistant wheat cultivars bearing Cmc3 and Cmc4 (targeting WCM), Wsm1 and Wsm2 (targeting WSMV), and Wsm1 (targeting TriMV) are widely used to manage this pest–pathogen complex. However, comprehensive studies investigating how these resistance mechanisms influence both vector biology and virus transmission remain scarce. To address this gap, we evaluated disease development and WCM fitness across nine wheat cultivars with differential resistance profiles under single and mixed infections of WSMV and TriMV. We found strong viral synergy in co-infected plants, with TriMV accumulation markedly enhanced during mixed infections, irrespective of host genotype. Symptom severity and virus titers (both WSMV and TriMV) were highest in the cultivars carrying Wsm2, suggesting a potential trade-off in resistance effectiveness under mixed infection pressure. While mite development time (egg to adult) was unaffected by host genotype or infection status, mite fecundity was significantly reduced on infected plants carrying Wsm1 or Wsm2, but not on those with Cmc3 and Cmc4. Notably, virus accumulation in mites was reduced on the cultivars with Cmc3 and Cmc4, correlating with virus titers in the host tissues. Our findings highlight the complex interplay between host resistance, virus dynamics, and vector performance. Cultivars harboring Cmc3 and Cmc4 may offer robust field-level protection by simultaneously suppressing mite reproduction and limiting virus accumulation in both plant and vector. 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

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

Lesion mimic phenotypes, characterized by leaf spots formed in the absence of pathogens or pests, are often associated with reactive oxygen species (ROS) accumulation and cell necrosis. This study identified a novel and stable homozygous spotted phenotype (HSP) from the F₈ population of common wheat (XN509 × N07216). The yellow spots that appeared at the booting stage were light-sensitive, and accompanied by cell necrosis and H₂O₂ accumulation. Compared with homozygous normal plants (HNPs), HSPs exhibited enhanced resistance to stripe rust and powdery mildew without compromising yield. RNA-Seq analysis at three stages revealed that differentially expressed genes (DEGs) between HSPs and HNPs were significantly enriched in KEGG pathways related to photosynthesis and photosynthesis-antenna proteins. GO analysis highlighted chloroplast and light stimulus-related down-regulated DEGs. Fine mapping identified TaSpl1 within a 0.91 Mb interval on chromosome 3DS, flanked by the markers KASP188 and KASP229, using two segregating populations comprising 1117 individuals. The candidate region contained 42 annotated genes, including 14 DEGs based on previous BSR-Seq data. PCR amplification and qRT-PCR verification identified the expression of TraesCS3D02G022100 was consistent with RNA-Seq data. Gene homology analysis and silencing experiments confirmed that TraesCS3D02G022100 was associated with stay-green traits. These findings provide new insights into the genetic regulation of lesion mimics, photosynthesis, and disease resistance in wheat. Full article

Hydroponics is a method of growing plants without soil and serves as an efficient agricultural production system. Compared to traditional farming, hydroponic crops offer significant water savings while also reducing the need for chemical pesticides by eliminating soil-borne diseases and pests. Additionally, hydroponic materials are being studied as a potential food source for space missions and as a substitute for industrially produced animal feed during winter. This paper explores the acoustic absorption properties of green materials derived from hydroponic systems. The roots of wheat grown in a porous layer formed a rigid skeleton structure. After drying, test specimens were prepared for acoustic measurements, undertaken using an impedance tube, to assess the material’s sound absorption performance. The results indicate optimal absorption around 600 Hz and 2000 Hz, reaching α = 0.95–1.0, which is significant. A brief description of the substrate layers is also provided. Full article

Certain entomopathogenic fungi, such as Beauveria bassiana, are highly pathogenic to arthropod pests and are able to colonize plant tissues, thereby enhancing both plant growth and disease resistance. This study assessed three B. bassiana strains (CBM1, CBM2, and CBM3) for their pathogenicity toward insect larvae and colonization potential in wheat. The insecticidal activity of the fungi against the larvae of the major lepidopteran pests Helicoverpa armigera, Spodoptera frugiperda, Mythimna separata, and Plutella xylostella was determined. The fungi were then applied to wheat plants using seed immersion and soil drench methods; their colonization rates were compared, and the impacts of fungal colonization on wheat growth and survival were evaluated. The results demonstrated that all three strains were effective in reducing insect damage, with B. bassiana CBM1 exhibiting the highest pathogenicity followed by CBM3 and CBM2. B. bassiana CBM1 was particularly effective, with a significantly higher colonization rate achieved through soil drenching compared to seed immersion. The soil inoculation of B. bassiana resulted in increased plant height at 30 days after sowing (DAS) and root length at 15 DAS compared to the control group. B. bassiana CBM1-colonized wheat increased the mortality of fall armyworm. This research has enriched the biological control microbial resource pool and highlights the potential of B. bassiana in integrated pest management strategies. Full article

Wheat pathogens pose a significant risk to global wheat production, with climate change further complicating disease dynamics. Effective management requires a combination of genetic resistance, cultural practices, and careful use of chemical controls. Ongoing research and adaptation to changing environmental conditions are crucial for sustaining wheat yields and food security. Based on selective academic literature retrieved from the Scopus database and analyzed by a bibliographic software such as the VOSviewer we discussed and focused on various aspects of current and future strategies for managing major wheat pathogens and diseases such as Tan spot, Septoria tritici blotch, Fusarium head blight, etc. Chemical management methods, such as the use of fungicides, can be effective but are not always preferred. Instead, agronomic practices like crop rotation and tillage play a significant role in managing wheat diseases by reducing both the incidence and severity of these diseases. Moreover, adopting resistance strategies is essential for effective disease management. Full article

Traditional sweet potato disease and pest detection methods have the limitations of low efficiency, poor accuracy and manual dependence, while deep learning-based target detection can achieve an efficient and accurate detection. This paper proposed an efficient sweet potato leaf disease and pest detection method SPLDPvB, as well as a low-complexity version SPLDPvT, to achieve accurate identification of sweet potato leaf spots and pests, such as hawk moth and wheat moth. First, a residual module containing three depthwise separable convolutional layers and a skip connection was proposed to effectively retain key feature information. Then, an efficient feature extraction module integrating the residual module and the attention mechanism was designed to significantly improve the feature extraction capability. Finally, in the model architecture, only the structure of the backbone network and the decoupling head combination was retained, and the traditional backbone network was replaced by an efficient feature extraction module, which greatly reduced the model complexity. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of the proposed SPLDPvB model were 88.7% and 74.6%, respectively, and the number of parameters and the amount of calculation were 1.1 M and 7.7 G, respectively. Compared with YOLOv11S, mAP0.5 and mAP0.5:0.95 increased by 2.3% and 2.8%, respectively, and the number of parameters and the amount of calculation were reduced by 88.2% and 63.8%, respectively. The proposed model achieves higher detection accuracy with significantly reduced complexity, demonstrating excellent performance in detecting sweet potato leaf pests and diseases. This method realizes the automatic detection of sweet potato leaf pests and diseases and provides technical guidance for the accurate identification and spraying of pests and diseases. Full article

Tan spot (Pyrenophora tritici-repentis) and stripe rust (Puccinia striiformis f. sp. tritici) are major foliar diseases of wheat, causing significant yield losses globally. This study evaluated the efficacy of fungicide seed treatments in managing these diseases during early growth stages under greenhouse, growth chamber, and field conditions in the Northern Great Plains. Winter and spring wheat cultivars were treated with pyraclostrobin or combinations of thiamethoxam, difenoconazole, mefenoxam, fludioxonil, and sedaxane, among others. Greenhouse and growth chamber plants were inoculated with the respective pathogens, while field trials relied on natural inoculum. Fungicide treatments significantly reduced stripe rust severity (up to 36%) (p ≤ 0.05) and moderately reduced tan spot severity during early growth stages (15–20%). Treated plants demonstrated a 30–40% improvement in plant vigor, and a 25–50% increase in winter survival. Additionally, grain yield in treated plots increased by 25–50% (p ≤ 0.05), with test weight and protein content improving by 10% and 15%, respectively. These findings demonstrate the potential of fungicide seed treatments as an integrated pest (or pathogen) management (IPM) strategy to enhance early foliar disease control and wheat productivity. Full article

The knowledge within wheat production chain data has multiple levels and complex semantic relationships, making it difficult to extract knowledge from them. Therefore, this paper proposes a fine-grained knowledge extraction method for the wheat production chain based on ontology. For the first time, the conceptual layers of ploughing, planting, managing, and harvesting were defined around the main agricultural activities of the wheat production chain. Based on this, the entities, relationships, and attributes in the conceptual layers were defined at a fine-grained level, and a spatial–temporal association pattern layer with four conceptual layers, twenty-eight entities, and forty-two relationships was constructed. Then, based on the characteristics of the self-constructed dataset, the Word2vec-BiLSTM-CRF model was designed for extracting the knowledge within it, i.e., the entity–relationship–attribute model and the Word2vec-BiLSTM-CRF model in this paper were compared with the four SOTA models. The results show that the accuracy and F1 value improved by 8.44% and 8.89%, respectively, compared with the BiLSTM-CRF model. Furthermore, the entities of the pest and disease dataset were divided into two different granularities for the comparison experiment; the results show that for entities with “disease names” and “pest names”, the recognition accuracy at the fine-grained level is improved by 32.71% and 31.58%, respectively, compared to the coarse-grained level, and the recognition performance of various fine-grained entities has been improved. Full article

Most chemical pesticides, in addition to their main functions (protection against diseases, weeds, and pests), also have a noticeable inhibitory effect on target crops. In a laboratory experiment and two-year field experiments (Russia, Trans-Urals), a study was made of the effect of the biopreparation Azolen^® (Azotobacter vinelandii IB-4) on plants of the Ekada 113 wheat variety under conditions of drought and stress caused by the exposure to the herbicide Chistalan (2.4-D and dicamba). The biopreparation and the herbicide were used separately and together on wheat during the tillering phase. Treatment with the biological preparation under stressful conditions had a significant effect on the hormonal balance of plants (a decrease in the amount of abscisic acid and a normalization of the balance of indolyl-3-acetic acid and cytokinins in shoots and roots of plants was noted), while the osmoprotective, antioxidant, and photosynthetic systems of plants were activated. In drought conditions, the treatment of plants with biological preparation prevented the inhibition of root growth caused by the use of the herbicide. This, in turn, improved the absorption of water by plants and ensured an increase in wheat yield (1.6 times). The results obtained give reason to believe that microbiological preparations can be used as antidotes that weaken the phytotoxic effect of herbicidal treatments, including in drought conditions. Full article

Gene-edited crops and livestock have the potential to transform food systems by providing resilience to climate change, pest and disease resistance, and the enhancement of nutrients in feed and food in a time-efficient and precise way. In 2023, the UK Parliament passed the Genetic Technology (Precision Breeding) Bill, paving the way for gene-edited products to be farmed in England and sold, providing they could have theoretically been produced via traditional breeding. In this paper, we describe the possible risks of gene-edited products for consumption using four case studies of gene-edited organisms: increased vitamin D tomatoes, reduced linoleic acid cottonseed oil, porcine reproductive and respiratory virus (PRRSV) resistant pigs and reduced-asparagine wheat. Assuming that the only requirement for an organism to be a Precision-Bred Organism (PBO) is that no transgenic material remains within the organism and that the edit could have, in theory, occurred spontaneously or through traditional breeding methods, then all our case studies would likely be defined as PBOs. We also conclude that the food safety risks of these products appear to be similar to those that society accepts in traditionally bred organisms used for food and feed. However, PBOs that possess markedly altered nutrient profiles may require a dedicated identity-preserved retail chain and/or labelling to avoid unintended over-consumption. Full article

This study addresses the challenges in plant pest and disease prediction within the context of smart agriculture, highlighting the need for efficient data processing techniques. In response to the limitations of existing models, which are characterized by slow training speeds and a low prediction accuracy, we introduce an innovative prediction method that integrates gene expression programming (GEP) with support vector machines (SVM). Our approach, the gene expression programming—support vector machine (GEP-SVM) model, begins with encoding and fitness function determination, progressing through cycles of selection, crossover, mutation, and the application of a convergence criterion. This method uniquely employs individual gene values as parameters for SVM, optimizing them through a grid search technique to refine genetic parameters. We tested this model using historical data on wheat blossom midges in Shaanxi Province, spanning from 1933 to 2010, and compared its performance against traditional methods, such as GEP, SVM, naive Bayes, K-nearest neighbor, and BP neural networks. Our findings reveal that the GEP-SVM model achieves a leading back-generation accuracy rate of 90.83%, demonstrating superior generalization and fitting capabilities. These results not only enhance the computational efficiency of pest and disease prediction in agriculture but also provide a scientific foundation for future predictive endeavors, contributing significantly to the optimization of agricultural production strategies. Full article

Overall, there is a major wish that European farmers implement integrated pest management (IPM), particularly to reduce dependence on pesticides. In the European Rustwatch project, partners conducted nineteen trials across nine different countries during 2020 and 2021 to investigate different IPM strategies, focusing on controlling rust diseases in winter wheat. The trials included the use of varieties with contrasting levels of resistance, variety mixtures, reduced fungicide rates, thresholds, and Decision Support Systems (DSSs), and testing alternative products to fungicides. Sixteen trials developed yellow rust (Puccinia striiformis f. sp. tritici) infections, and six trials developed brown rust (Puccinia triticina) infections. Resistant varieties proved highly effective in keeping down yellow rust infection, and variety mixtures also effectively reduced infection levels and stabilized yields. Rust was fully controlled using 25% of standard fungicide rates, even under high disease pressure. Using DSSs provided sufficient control of rust diseases and resulted in competitive net economic returns due to fewer fungicide applications. The alternative products tested included two biological control agents and four alternative chemistries, which all gave inferior and insufficient control against rust compared with chemical fungicides. The trial work demonstrated that there are good and reliable options for including IPM into disease control in wheat. Full article

Thinopyrum intermedium (2n = 6x = 42, E^eE^eE^bE^bStSt or JJJ^sJ^sStSt) contains a large number of genes that are highly adaptable to the environment and immune to a variety of wheat diseases, such as powdery mildew, rust, and yellow dwarf, making it an important gene source for the genetic improvement of common wheat. Currently, an important issue plaguing wheat production and breeding is the spread of pests and illnesses. Breeding disease-resistant wheat varieties using disease-resistant genes is currently the most effective measure to solve this problem. Moreover, alien resistance genes often have a stronger disease-resistant effect than the resistance genes found in common wheat. In this study, the wheat-Th. intermedium partial amphiploid line 92048 was developed through hybridization between Th. intermedium and common wheat. The chromosome structure and composition of 92048 were analyzed using ND-FISH and molecular marker analysis. The results showed that the chromosome composition of 92048 (Octoploid Trititrigia) was 56 = 42W + 6J + 4J^s + 4St. In addition, we found that 92048 was highly resistant to a mixture of stripe rust races (CYR32, CYR33, and CYR34) during the seedling stage and fusarium head blight (FHB) in the field during the adult plant stage, suggesting that the alien or wheat chromosomes in 92048 had disease-resistant gene(s) to stripe rust and FHB. There is a high probability that the gene(s) for resistance to stripe rust and FHB are from the alien chromosomes. Therefore, 92048 shows promise as a bridge material for transferring superior genes from Th. intermedium to common wheat and improving disease resistance in common wheat. Full article