Search Results (4,281)

Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. To address the issue of insufficient sample data for certain disease categories, we utilize OpenAI’s DALL-E image generation model to synthesize images for low-frequency diseases, which effectively improves the model’s recognition ability and generalization performance for underrepresented classes. To tackle the challenges of large-scale variations and irregular lesion distribution, we design a multi-scale feature aggregation module (MFAM). This module integrates multi-scale semantic information through a lightweight, multi-branch convolutional structure, enhancing the model’s ability to detect small-scale lesions. To further overcome the receptive field limitations of traditional convolution, we propose incorporating a deformable attention transformer (DAT) into the C2PSA module. This allows the model to flexibly focus on lesion areas amidst complex backgrounds, improving feature extraction and robustness. Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. Lastly, we replace the traditional regression loss with the MPDIoU loss function, enhancing bounding box accuracy and accelerating model convergence. Experimental results demonstrate that the proposed DEMM-YOLO model achieves 94.8% precision, 93.1% recall, and 96.7% mAP@0.5 in cotton leaf disease detection, highlighting its strong performance and promising potential for real-world agricultural applications. Full article

Plant peptides, as key signaling molecules, play pivotal roles in plant growth, development, and stress responses. This review focuses on research progress in plant peptides involved in plant immunity, providing a detailed classification of immunity-related plant polypeptides, including small post-translationally modified peptides, cysteine-rich peptides, and non-cysteine-rich peptides. It discusses the mechanisms by which plant polypeptides confer disease resistance, such as their involvement in pattern-triggered immunity (PTI), effector-triggered immunity (ETI), and regulation of hormone-mediated defense pathways. Furthermore, it explores potential agricultural applications of plant polypeptides, including the development of novel biopesticides and enhancement of crop disease resistance via genetic engineering. By summarizing current research, this review aims to provide a theoretical basis for in-depth studies on peptide-mediated disease resistance and offer innovative insights for plant disease control. Full article

The widespread use of pesticides in modern agriculture has significantly enhanced food production by managing pests and diseases; however, their degradation in soil can lead to unintended consequences for soil fertility and nutrient cycling. This review explores the mechanisms of pesticide degradation, both abiotic and biotic, and the soil factors influencing these processes. It critically examines how degradation products impact soil microbial communities, organic matter decomposition, and key nutrient cycles, including nitrogen, phosphorus, potassium, and micronutrients. This review highlights emerging evidence linking pesticide residues with altered enzymatic activity, disrupted microbial populations, and reduced nutrient bioavailability, potentially compromising soil structure, water retention, and long-term productivity. Additionally, it discusses the broader environmental and agricultural implications, including decreased crop yields, biodiversity loss, and groundwater contamination. Sustainable management strategies such as bioremediation, the use of biochar, eco-friendly pesticides, and integrated pest management (IPM) are evaluated for mitigating these adverse effects. Finally, this review outlines future research directions emphasizing long-term studies, biotechnology innovations, and predictive modeling to support resilient agroecosystems. Understanding the intricate relationship between pesticide degradation and soil health is crucial to ensuring sustainable agriculture and food security. Full article

Cassava (Manihot esculenta Crantz) is essential for global food security, especially in tropical regions. As an important genetic resource, its genetics plays a key role in crop breeding, enabling the development of more productive and pest- and disease-resistant varieties. Scientometrics, which quantitatively analyzes the production and impact of scientific research, is crucial for understanding trends in cassava genetics. This study aimed to apply bibliometric methods to conduct a scientific mapping analysis based on yearly publication trends, paper classification, author productivity, journal impact factor, keywords occurrences, and omic approaches to investigate the application of genetics to the species from 1960 to 2022. From the quantitative data analyzed, 3246 articles were retrieved from the Web of Science platform, of which 654 met the inclusion criteria. A significant increase in scientific production was observed from 1993, peaking in 2018. The first article focused on genetics was published in 1969. Among the most relevant journals, Euphytica stood out with 36 articles, followed by Genetics and Molecular Research (n = 30) and Frontiers in Plant Science (n = 25). Brazil leads in the number of papers on cassava genetics (n = 143), followed by China (n = 110) and the United States (n = 75). The analysis of major methodologies (n = 185) reveals a diversified panorama during the study period. Morpho-agronomic descriptors persisted from 1978 to 2022; however, microsatellite markers were the most widely used, with 102 records. Genomics was addressed in 87 articles, and transcriptomics in 65. By clarifying the current landscape, this study supports cassava conservation and breeding, assists in public policy formulation, and guides future research in the field. Full article

Anthracnose, a significant disease of aloe with characteristics of contact transmission, poses a considerable threat to the economic viability of aloe cultivation. To address the challenges of accurately detecting and classifying crop diseases in complex environments, this study proposes an enhanced algorithm, YOLOv11-seg-DEDB, based on the improved YOLOv11-seg model. This approach integrates multi-scale feature enhancement and a dynamic attention mechanism, aiming to achieve precise segmentation of aloe anthracnose lesions and effective disease level discrimination in complex scenarios. Specifically, a novel Disease Enhance attention mechanism is introduced, combining spatial attention and max pooling to improve the accuracy of lesion segmentation. Additionally, the DCNv2 is incorporated into the network neck to enhance the model’s ability to extract multi-scale features from targets in challenging environments. Furthermore, the Bidirectional Feature Pyramid Network structure, which includes an additional p2 detection head, replaces the original PANet network. A more lightweight detection head structure is designed, utilizing grouped convolutions and structural simplifications to reduce both the parameter count and computational load, thereby enhancing the model’s inference capability, particularly for small lesions. Experiments were conducted using a self-collected dataset of aloe anthracnose infected leaves. The results demonstrate that, compared to the original model, the improved YOLOv11-seg-DEDB model improves segmentation accuracy and mAP@50 for infected lesions by 5.3% and 3.4%, respectively. Moreover, the model size is reduced from 6.0 MB to 4.6 MB, and the number of parameters is decreased by 27.9%. YOLOv11-seg-DEDB outperforms other mainstream segmentation models, providing a more accurate solution for aloe disease segmentation and grading, thereby offering farmers and professionals more reliable disease detection outcomes. Full article

Atriplex hortensis var. rubra (red orache, RO) is a halotolerant species rich in nutraceutical compounds, which makes it a valuable crop for human nutrition. This plant could also be exploited for phytoremediation of contaminated soil and wastewater, and for saline aquaponics. A root rot disease was observed on hydroponically grown RO plants, caused by Pythium deliense and Pythium Cluster B2a sp. Identification was based on morphology, molecular analysis (ITS and COI), and phylogenetic analysis. We assessed disease severity in plants grown in a growth chamber with nutrient solutions containing different NaCl concentrations (0, 7, and 14 g L⁻¹ NaCl). In vitro growth at different salinity levels and temperatures was also evaluated. Both Pythium species were pathogenic but showed different responses. Pythium deliense was significantly more virulent than Pythium Cluster B2a sp., causing a steady reduction in root dry weight (RDW) of 70% across all salinity levels. Pythium Cluster B2a sp. reduced RDW by 50% at 0 and 7 g L⁻¹ NaCl while no symptoms were observed at 14 g L⁻¹ NaCl. Pythium deliense grew best at 7 and 14 g L⁻¹ NaCl, while Pythium Cluster B2a sp. growth was reduced at 14 g L⁻¹ NaCl. Both pathogens had an optimum temperature of 30 °C. This is the first report of Pythium spp. causing root rot on RO grown hydroponically. The effective use of halophytic crops must consider pathogen occurrence and fitness in saline conditions. Full article

The health of crops is a major challenge for productivity growth in agriculture, with plant diseases playing a key role in limiting crop yield. Identifying and understanding these diseases is crucial to preventing their spread. In particular, greenhouse pepper leaves are susceptible to diseases such as mildew, mites, caterpillars, aphids, and blight, which leave distinctive marks that can be used for disease classification. The study proposes a seven-class classifier for the rapid and accurate diagnosis of pepper diseases, with a primary focus on pre-processing techniques to enhance colour differentiation between green and yellow shades, thereby facilitating easier classification among the classes. A novel algorithm is introduced to improve image vibrancy, contrast, and colour properties. The diagnosis is performed using a modified VGG16Net model, which includes three additional layers for fine-tuning. After initialising on the ImageNet dataset, some layers are frozen to prevent redundant learning. The classification is additionally accelerated by introducing flattened, dense, and dropout layers. The proposed model is tested on a private dataset collected specifically for this study. Notably, this work is the first to focus on diagnosing aphid and caterpillar diseases in peppers. The model achieves an average accuracy of 92.00%, showing promising potential for seven-class deep learning-based disease diagnostics. Misclassifications in the aphid class are primarily due to the limited number of samples available. Full article

Wheat (Triticum aestivum L.) is a crucial global food crop. The intensive crop farming, monoculture cultivation, and impact of climate change affect the susceptibility of wheat cultivars to biotic stresses, mainly caused by soil fungal pathogens, especially those belonging to the genus Fusarium. This situation threatens yield and grain quality through root and crown rot. While conventional chemical fungicides face resistance issues and environmental concerns, biological alternatives like seed priming with natural metabolites are gaining attention. Polyamines, including putrescine, spermidine, and spermine, are attractive priming agents influencing plant development and abiotic stress responses. Spermine in particular shows potential for in vitro antifungal activity against Fusarium. Optimising spermine concentration for seed priming is crucial to maximising protection against Fusarium infection while ensuring robust plant growth. In this research, we explored the potential of the polyamine spermine as a seed treatment to enhance wheat resilience, aiming to identify a sustainable alternative to synthetic fungicides. Our findings revealed that a six-hour seed soak in spermine solutions ranging from 0.5 to 5 mM did not delay germination or seedling growth. In fact, the 5 mM concentration significantly stimulated root weight and length. In complementary in vitro assays, we evaluated the antifungal activity of spermine (0.5–5 mM) against three Fusarium species. The results demonstrated complete inhibition of Fusarium culmorum growth at 5 mM spermine. A less significant effect on Fusarium graminearum and little to no impact on Fusarium oxysporum were found. The performed analysis revealed that the spermine had a fungistatic effect against the pathogen, retarding the mycelium growth of F. culmorum inoculated on the seed surface. A pot experiment with Bulgarian soft wheat cv. Sadovo-1 was carried out to estimate the effect of seed priming with spermine against infection with isolates of pathogenic fungus F. culmorum on plant growth and disease severity. Our results demonstrated that spermine resulted in a reduced distribution of F. culmorum and improved plant performance, as evidenced by the higher fresh weight and height of plants pre-treated with spermine. This research describes the efficacy of spermine seed priming as a novel strategy for managing Fusarium root and crown rot in wheat. Full article

Legumes are among the most important crops globally, serving as a major food source for protein and oil. In tropical regions, the cultivation of legumes has expanded significantly due to the increasing demand for food, plant-based products, and sustainable agriculture practices. However, tropical environments pose unique challenges, including high temperatures, erratic rainfall, soil infertility, and a high incidence of pests and diseases. Indeed, legumes are vulnerable to infections caused by bacteria, fungi, oomycetes, viruses, and nematodes. This review highlights the importance of legumes in tropical farming and discusses major diseases affecting productivity and their impact on the economy, environment, and lives of smallholder legume farmers. We emphasize the use of legume genetic resources and breeding, and biotechnology innovations to foster resistance and address the challenges posed by pathogens in legumes. However, an integrated approach that includes other cultivation techniques (e.g., crop rotation, rational fertilization, deep plowing) remains important for the prevention and control of diseases in legume crops. Finally, we highlight the contributions of plant genetic resources to smallholder resilience and food security. Full article

Honey bees are essential pollinators for the ecosystem and food crops. However, their health and survival face threats from both biotic and abiotic stresses. Fungi, microsporidia, and bacteria might significantly contribute to colony losses. Therefore, rapid and sensitive diagnostic tools are crucial for effective disease management. In this study, molecular assays were developed to quickly and efficiently detect the main honey bee pathogens: Nosema ceranae, Aspergillus flavus, Paenibacillus larvae, and Black queen cell virus. In this context, new primer pairs were designed for use in quantitative Real-time PCR (qPCR) reactions. Various protocols for extracting total nucleic acids from bee tissues were tested, indicating a CTAB-based protocol as the most efficient and cost-effective. Furthermore, excluding the head of the bee from the extraction, better results were obtained in terms of quantity and purity of extracted nucleic acids. These assays showed high specificity and sensitivity, detecting up to 250 fg of N. ceranae, 25 fg of P. larvae, and 2.5 pg of A. flavus DNA, and 5 pg of BQCV cDNA, without interference from bee DNA. These qPCR assays allowed pathogen detection within 3 h and at early stages of infection, supporting timely and efficient management interventions. Full article

The soil-borne fungi, Setophoma terrestris and Fusarium spp., are often associated with pink root, although the etiology of the disease remains doubtful. While recognized as the primary inoculum, studies show conflicting views on the formation of chlamydospores and microsclerotia in Setophoma. Therefore, this study aims to clarify the etiology of the pink root of garlic and onion and the formation of chlamydospores and microsclerotia in Setophoma. The isolates were obtained from symptomatic tissues of garlic, leeks, brachiaria, onions, chives, and maize collected from seven different states in Brazil. Representative isolates were selected for pathogenicity tests. Sequence comparison of the tubulin gene showed Setophoma (n = 50) and Fusarium clades (n = 25). Garlic and onion plants inoculated with Setophoma showed pink root symptoms, while plants inoculated with different Fusarium isolates remained asymptomatic. Multigene analysis of pathogenic isolates confirms that only Setophoma terrestris causes pink root in garlic and onion. In addition, brachiaria, chives, and leeks are newly identified hosts of this pathogen in Brazil. To our knowledge, the main sources of primary inoculum of the disease are chlamydospores, pycnidia, colonized roots of garlic, onion, and plant debris of susceptible crops. The new information obtained in this study will be fundamental for researchers in the development of genotypes that are resistant to pink root and will help the efficient management of the disease. Full article

Plant disease classification is critical for effective crop management. Recent advances in deep learning, especially Vision Transformers (ViTs), have shown promise due to their strong global feature modeling capabilities. However, ViTs often overlook local features and suffer from feature extraction degradation during patch merging as channels increase. To address these issues, we propose PLTransformer, a hybrid model designed to symmetrically capture both global and local features. We design a symmetric multi-scale convolutional module that combines two different-scale receptive fields to simultaneously extract global and local features so that the model can better perceive multi-scale disease morphologies. Additionally, we propose an overlap-attentive channel downsampler that utilizes inter-channel attention mechanisms during spatial downsampling, effectively preserving local structural information and mitigating semantic loss caused by feature compression. On the PlantVillage dataset, PLTransformer achieves 99.95% accuracy, outperforming DeiT (96.33%), Twins (98.92%), and DilateFormer (98.84%). These results demonstrate its superiority in handling multi-scale disease features. Full article

Among the new strategies for managing diseases in agricultural crops is the application of metallic nanoparticles due to their ability to inhibit the development of phytopathogenic microorganisms and to induce plant defense responses. Therefore, this research evaluated the effects of silver (AgNPs), zinc oxide (ZnONPs), and silicon dioxide (SiO₂NPs) nanoparticles on symptom progression and physiological parameters in two pathosystems: Pseudomonas syringae pv. tomato (Psto) in tomato (pathosystem one, culturable pathogen) and Candidatus Liberibacter solanacearum (CaLso) in pepper plants (pathosystem two, non-culturable pathogen). For in vitro pathosystem one assays, SiO₂NPs did not inhibit Psto growth. The minimum inhibitory concentration (MIC) was 31.67 ppm for AgNPs and 194.3 ppm for ZnONPs. Furthermore, the minimum lethal concentration (MLC) for AgNPs was 100 ppm, while for ZnONPs, it was 1000 ppm. For in planta assays, ZnONPs, AgNPs, and SiO₂NPs reduced the number of lesions per leaf, but only ZnONPs significantly decreased the severity. Regarding pathosystem two, AgNPs, ZnONPs, and SiO₂NPs application delayed symptom progression. However, only AgNPs significantly reduced severity percentage. Moreover, treatments with AgNPs and SiO₂NPs increased the plant height and dry weight compared to the results for the control. Full article

Rice blast caused by Magnaporthe oryzae is a major cause of yield reductions and quality deterioration in rice. Therefore, early detection of the disease is necessary for controlling the spread of rice blast. This study proposed a dual-channel feature fusion model (DCFM) to achieve effective identification of rice blast. The DCFM model extracted spectral features using successive projection algorithm (SPA), random frog (RFrog), and competitive adaptive reweighted sampling (CARS), and extracted spatial features from spectral images using MobileNetV2 combined with the convolutional block attention module (CBAM). Then, these features were fused using the feature fusion adaptive conditioning module in DCFM and input into the fully connected layer for disease identification. The results show that the model combining spectral and spatial features was superior to the classification models based on single features for rice blast detection, with OA and Kappa higher than 90% and 88%, respectively. The DCFM model based on SPA screening obtained the best results, with an OA of 96.72% and a Kappa of 95.97%. Overall, this study enables the early and accurate identification of rice blast, providing a rapid and reliable method for rice disease monitoring and management. It also offers a valuable reference for the detection of other crop diseases. Full article

Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning. Full article