Search Results (32)

To address limitations such as cleaning difficulties or secondary contamination/damage of cultural relics caused by the uncontrollable diffusion of water/cleaning agent/dirty liquids during the cleaning process in traditional cleaning methods, this study, using cotton textiles as an example, systematically investigated the cleaning efficacy of four in situ methods (blank gel, cleaning gel, ultrasonic emulsification, and gel + ultrasonic emulsification synergistic cleaning) on eight types of stains, including sand, clay, rust, blood, ink, oil, and mixed solid/liquid stains. Building upon this, this study proposed an efficient, targeted, in situ, and controllable cleaning strategy tailored for fragile, stained textile relics. Results demonstrated that, regardless of the stain type, the synergistic cleaning method of G+U (gel poultice + ultrasonic emulsification) consistently outperformed the cleaning methods of blank gel poultice, cleaning gel poultice, and ultrasonic emulsification. Furthermore, the gel loaded with cleaning agents was always more effective than the blank gel (unloaded cleaning agents). The poultice methods of blank gel and cleaning gel were better suited for solid stains, while the ultrasonic emulsification cleaning method was more effective for liquid stains. Meanwhile, it was also found that the optimal cleaning method proposed in this study (the G+U synergistic cleaning method) was a cleaning method that restricted the cleaning agent within the gel network/emulsion system, and utilized the porous network physical structure of gel, the chemical action of emulsion’s wetting/dissolving dirt, and the cavitation synergistic effect of ultrasound to achieve the targeted removal of contaminants from relics’ surfaces. Crucially, the cleaning process of G+U also had the characteristics of controlling the cleaning area at the designated position and effectively regulating the diffusion rate of the cleaning solution within the treatment zone, as well as the reaction intensity. Therefore, the proposed optimal (the synergistic cleaning method of G+U) cleaning method conforms to the significant implementation of the “minimal intervention and maximal preservation” principle in modern cultural heritage conservation. Consequently, the synergistic cleaning method of G+U holds promise for practical application in artifact cleaning work. Full article

Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study centers on maize, training deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease. The performance of multiple convolutional neural networks, such as ResNet-50, DenseNet-121, etc., is evaluated by their ability to classify maize diseases such as Northern Leaf Blight, Gray Leaf Spot, Common Rust, and Blight using UAV drone data. Remotely sensed MODIS satellite data was used to generate spatial severity maps over a uniform grid by implementing time-series modeling. Furthermore, reinforcement learning techniques were used to identify hotspots and prioritize the next locations for inspection by analyzing spatial and temporal patterns, identifying critical factors that affect disease progression, and enabling better decision-making. The integrated pipeline automates data ingestion and delivers farm-level condition views without manual uploads. The combination of multiple remotely sensed data sources leads to an efficient and scalable solution for early disease detection. Full article

Accurate and timely assessment of crop disease severity is crucial for effective management strategies and ensuring sustainable agricultural production. Traditional visual disease scoring methods are subjective and labor-intensive, highlighting the need for automated, objective alternatives. This study evaluates the effectiveness of a model for field-based identification and quantification of stripe rust severity in wheat using red, green, blue RGB imaging. Based on crop reflectance hyperspectra (CRHS) acquired using a FieldSpec ASD spectroradiometer, two complementary approaches were developed. In the first approach, we estimate single leaf disease severity (LDS) under laboratory conditions, while in the second approach, we assess crop disease severity (CDS) from field-based RGB images. The high accuracy of both methods enabled the development of a predictive model for estimating LDS from CDS, offering a scalable solution for precision disease monitoring in wheat cultivation. The experiment was conducted on four winter wheat plots subjected to varying fungicide treatments to induce different levels of stripe rust severity for model calibration, with treatment regimes ranging from no application to three applications during the growing season. RGB images were acquired in both laboratory conditions (individual leaves) and field conditions (nadir and oblique perspectives), complemented by hyperspectral measurements in the 350–2500 nm range. To achieve automated and objective assessment of disease severity, we developed custom image-processing scripts and applied Random Forest classification and regression models. The models demonstrated high predictive performance, with the combined use of nadir and oblique RGB imagery achieving the highest classification accuracy (97.87%), sensitivity (100%), and specificity (95.83%). Oblique images were more sensitive to early-stage infection, while nadir images offered greater specificity. Spectral feature selection revealed that wavelengths in the visible (e.g., 508–563 nm and 621–703 nm) and red-edge/SWIR regions (around 1556–1767 nm) were particularly informative for disease detection. In classification models, shorter wavelengths from the visible range proved to be more useful, while in regression models, longer wavelengths were more effective. The integration of RGB-based image analysis with the Random Forest algorithm provides a robust, scalable, and cost-effective solution for monitoring stripe rust severity under field conditions. This approach holds significant potential for enhancing precision agriculture strategies by enabling early intervention and optimized fungicide application. Full article

Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa, has become an intermittent yet economically significant disease of wheat in the Upper Midwest during the last decade. Because chemical and cultural controls remain ineffective, breeders rely on developing resistant varieties, yet visual ratings in inoculated nurseries are labor-intensive, subjective, and time-consuming. To accelerate this process, we combined unmanned-aerial-vehicle hyperspectral imaging (UAV-HSI) with a carefully tuned chemometric workflow that delivers rapid, objective estimates of disease severity. Principal component analysis cleanly separated BLS, leaf rust, and Fusarium head blight, with the first component explaining 97.76% of the spectral variance, demonstrating in-field pathogen discrimination. Pre-processing of the hyperspectral cubes, followed by robust Partial Least Squares (RPLS) regression, improved model reliability by managing outliers and heteroscedastic noise. Four variable-selection strategies—Variable Importance in Projection (VIP), Interval PLS (iPLS), Recursive Weighted PLS (rPLS), and Genetic Algorithm (GA)—were evaluated; rPLS provided the best balance between parsimony and accuracy, trimming the predictor set from 244 to 29 bands. Informative wavelengths clustered in the near-infrared and red-edge regions, which are linked to chlorophyll loss and canopy water stress. The best model, RPLS with optimal preprocessing and variable selection based on the rPLS method, showed high predictive accuracy, achieving a cross-validated R² of 0.823 and cross-validated RMSE of 7.452, demonstrating its effectiveness for detecting and quantifying BLS. We also explored the spectral overlap with Sentinel-2 bands, showing how UAV-derived maps can nest within satellite mosaics to link plot-level scouting to landscape-scale surveillance. Together, these results lay a practical foundation for breeders to speed the selection of resistant lines and for agronomists to monitor BLS dynamics across multiple spatial scales. Full article

Leaf rust and yellow rust are globally significant fungal diseases that severely impact wheat production, causing yield losses of up to 60% in highly susceptible cultivars. Early and accurate detection is crucial for integrating precision crop protection strategies to mitigate these losses. This study investigates the potential of 3D LiDAR technology for monitoring rust-induced physiological changes in wheat by analyzing variations in plant height, biomass, and light reflectance intensity. Results showed that grain yield decreased by 10–50% depending on cultivar susceptibility, with the durum wheat cultivar ‘Kiko Nick’ and bread wheat ‘Califa’ exhibiting the most severe reductions (~50–60%). While plant height and biomass remained relatively unaffected, LiDAR-derived intensity values strongly correlated with disease severity (R² = 0.62–0.81, depending on the cultivar and infection stage). These findings demonstrate that LiDAR can serve as a non-destructive, high-throughput tool for early rust detection and biomass estimation, highlighting its potential for integration into precision agriculture workflows to enhance disease monitoring and improve wheat yield forecasting. To promote transparency and reproducibility, the dataset used in this study is openly available on Zenodo, and all processing code is accessible via GitHub, cited at the end of this manuscript. Full article

Wheat Stripe Rust Disease (WRD) poses a significant threat to wheat crops, causing substantial yield losses and can result in total crop damage if not detected early. The localization of WRD-infected areas is a labor-intensive and time-consuming task due to the intricate and varied nature of the disease spread, especially for large plantations. Hence, segmentation of wheat crops is vital for early identification of the WRD-affected area, which allows for the implementation of targeted intervention measures. The state-of-the-art segmentation technique for WRD using the real-world semantic segmentation NWRD dataset is based on a UNet model with the Adaptive Patching with Feedback (APF) technique. However, this implementation is complex and requires significant resources and time for training due to the processing of each patch of the dataset. Our work in this paper improves the state-of-the-art by using a two-stage model: a Vision Transformer (ViT) classifier to distinguish between the rust and non-rust patches and a less complex co-salient object detection (Co-SOD) model for segmentation of the classified images. The Co-SOD model uses multiple rust patches to extract contextual features from a group of images. By analyzing multiple patches of wheat rust disease simultaneously, we can segment disease regions more accurately. Our results show that the proposed approach achieves a higher F1 score (0.638), precision (0.621), and recall (0.675) for the rust class with $5\times$ less training time as compared to the previous works. Full article

Computer vision techniques offer promising tools for disease detection in orchards and can enable effective phenotyping for the selection of resistant cultivars in breeding programmes and research. In this study, a digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry, focusing on European pear rust (Gymnosporangium sabinae) as a model pathogen. High-resolution RGB images from ten low-altitude drone flights were collected in 2021, 2022 and 2023. A total of 16,251 annotations of leaves with pear rust symptoms were created on 584 images using the Computer Vision Annotation Tool (CVAT). The YOLO algorithm was used for the automatic detection of symptoms. A novel photogrammetric approach using Agisoft’s Metashape Professional software ensured the accurate localisation of symptoms. The geographic information system software QGIS calculated the infestation intensity per tree based on the canopy areas. This drone-based phenotyping system shows promising results and could considerably simplify the tasks involved in fruit breeding research. Full article

Protected cultivation is the sustainable approach to horticultural crop production under adverse climates. In this study, the performance of yard-long beans under three protected cultivations, including single-span polyhouse (SSP), five-span polyhouse (FSP), and insect-proof net house (IPN), is examined and compared to open field cultivation. The above protected cultivation can extend the harvest period of pods by 6–10 days, improve their quality, and increase yield by 15.6% to 25.1%, reducing the incidence and severity of thrips and Cercospora leaf spot, rust, and powdery mildew. Among them, yard-long beans grown in SSP are longer and straighter in shape and have the lowest incidence and severity of pests and diseases and the highest levels of total polyphenols, total sugar, soluble protein, starch, and fiber. This indicates that protected cultivation has broad application in the production of yard-long beans. Through full subset regression analysis (FSRA), we report here that the yield and of yard-long bean occurrences of pests and diseases were highly impacted by climatic factors, especially UV radiation intensity and air temperature. These results have considerable implications for improving pod yield and quality and green prevention and control of pests and diseases through optimizing facility structure and fertilizer management. Full article

Pseudomonas are known as higher producers of secondary metabolites with antimicrobial properties and plant growth promoters, including resistance induction. These mechanisms should be an alternative to pesticide use in crop production. Phakopsora pachyrhizi causes Asian soybean rust, representing a high loss of yield around the world. The objective of this paper was to evaluate the application of secondary metabolites produced by Pseudomonas aeruginosa LV strain from the semi-purified fraction F4A in soybean plants to induce plant resistance against P. pachyrhizi in field conditions. The experimental design was performed in randomized blocks with three replicates using two F4A doses (1 and 10 μg mL⁻¹) combined or not with fungicides (Unizeb Gold^® or Sphere Max^®). The control treatment, with Uni + Sph, saponins, flavonoids, and sphingolipids, showed higher intensities in the plants. In contrast, plants treated with the F4A fraction mainly exhibited fatty acid derivatives and some non-identified compounds with nitrogen. Plants treated with Sphere Max^®, with or without F4A10, showed higher intensities of glycosylated flavonoids, such as kaempferol, luteolin, narigenin, and apigenin. Plants treated with F4A showed higher intensities of genistein and fatty acid derivatives. These increases in flavonoid compound biosynthesis and antioxidant properties probably contribute to the protection against reactive oxygen species (ROS). Full article

Red solar-induced chlorophyll fluorescence (SIF_B) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIF_B by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIF_B using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PK_B) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PK_B and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPAR_chl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIF_B by the vegetation canopy. Consequently, we constructed an expression of PK_B to modify SIF_B. To evaluate the modified SIF_B (MSIF_B) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIF_B, MSIF_B, SIF_B-VIs (a fusion of the vegetation index and SIF_B), and MSIF_B-VIs (a fusion of the vegetation index and MSIF_B) with the severity level (SL), respectively. The results show that the correlation between MSIF_B and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIF_B. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIF_B, MSIF_B, SIF_B-VIs, and MSIF_B-VIs as independent variables. PK_B significantly improves the accuracy and robustness of models based on SIF_B and its fusion index SIF_B-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIF_B-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIF_B-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIF_B modified by PK_B can weaken the effects of chlorophyll reabsorption and canopy architecture on SIF_B and improve the ability of SIF_B to detect stress information. Full article

Cephaleuros species are well-known as plant pathogens that cause red rust or algae spot diseases in many economically cultivated plants that grow in shady and humid environments. Despite their prevalence, the adaptive evolution of these pathogens remains poorly understood. We sequenced and characterized three Cephaleuros (Cephaleuros lagerheimii, Cephaleuros diffusus, and Cephaleuros virescens) chloroplast genomes, and compared them with seven previously reported chloroplast genomes. The chloroplast sequences of C. lagerheimii, C. diffusus, and C. virescens were 480,613 bp, 383,846 bp, and 472,444 bp in length, respectively. These chloroplast genomes encoded 94 genes, including 27 tRNA genes, 3 rRNA genes, and 64 protein-coding genes. Comparative analysis uncovered that the variation in genome size was principally due to the length of intergenic spacer sequences, followed by introns. Furthermore, several highly variable regions (trnY-GTA, trnL-TAG, petA, psbT, trnD-GTC, trnL-TAA, ccsA, petG, psaA, psaB, rps11, rps2, and rps14) were identified. Codon bias analysis revealed that the codon usage pattern of Cephaleuros is predominantly shaped by natural selection. Additionally, six chloroplast protein-coding genes (atpF, chlN, psaA, psaB, psbA, and rbcL) were determined to be under positive selection, suggesting they may play a vital roles in the adaptation of Cephaleuros to low-light intensity habitats. Full article

Sugarcane brown rust (SCBR), caused by Puccinia melanocephala, is a destructive fungal disease that has extensively spread in the sugarcane-cultivating regions across the world. Early monitoring plays an important role in predicting the P. melanocephala epidemic and managing SCBR. However, accurately identifying SCBR based on symptoms and urediniospore morphology at the initial stage is a challenge. Further, it is tedious, time-consuming, labor-intensive, and requires expensive equipment to detect P. melanocephala using PCR-based methods. Loop-mediated isothermal amplification (LAMP) technology is renowned for its speed, simplicity, and low equipment requirements for specifically and sensitively identifying many pathogens. Therefore, in this study, a novel and highly sensitive LAMP assay was developed for the specific detection of P. melanocephala in sugarcane. Here, the internal transcribed spacer (ITS) sequence of P. melanocephala was selected as the target gene for LAMP primer design. Based on the color change of SYBR Green I and gel electrophoresis, specific LAMP primers were screened. Further, the optimal reaction conditions for the LAMP assay were determined at 63 °C for 60 min. The LAMP assay showed a high degree of specificity for the detection of P. melanocephala in sugarcane, with no cross-reactivity with other fungal pathogens. The established LAMP protocol was highly sensitive and can be used to detect as low as 1 pg/μL of P. melanocephala plasmid DNA, which is comparable to that of nested PCR and ~100 times more sensitive than conventional PCR. Finally, the detection rate of the LAMP method was higher than that of conventional and nested PCR in field samples. Full article

Alberta’s forest industry is predicted to be impacted by a medium-term decline in timber supply. Intensive silviculture tools, such as pre-commercial thinning, have been shown to increase individual tree growth, shorten rotation lengths, and improve stand merchantability in important commercial species such as lodgepole pine. However, lodgepole pine stands are susceptible to western gall rust infections, and thinning at an early stage may increase infection rates. This study collected tree and stand level data from 33 operational harvest origin lodgepole pine stands consisting of 11 stands thinned at age 17–19 years (PCT_18), 11 stands thinned at age 23–25 (PCT_24), and 11 unthinned stands. Approximately 40 years after pre-commercial thinning, merchantable volume is similar in all stands but thinned stands, regardless of timing, had greater individual tree size (~15% higher) compared to unthinned stands. Pre-commercially thinned stands also had a higher potential for commercial thinning since they have lower variability in tree size and longer live crown lengths. In addition, delayed thinning (PCT_24) reduced western gall rust infections and the severity of infections compared to both PCT_18 and unthinned stands. In conclusion, pre-commercial thinning should be considered for lodgepole pine stands in order to address timber supply issues in Alberta. Full article

Physic nut (Jatropha curcas L.) has emerged as a promising fruit crop in Ecuador, but the recent identification of rust poses a potential threat to its productive development. This study focused on elucidating the morphological aspects of the basidiomycete and assessing rust intensity across different canopy levels of physic nut hybrids and genotypes under field and semi-controlled conditions in Manabí, Ecuador. For the first time, this study confirms that Phakopsora arthuriana should be responsible for rust on physic nut in Ecuador based on the characteristics of the fungal structures. Rust incidence was 100% across all canopy layers, with the lower and middle canopies exhibiting higher severity and lesion numbers than the upper canopy. Using the Weibull nonlinear distribution model, we epidemiologically modeled disease progression, revealing that hybrid JAT 001100 displayed the highest temporal progress, recording 15% severity and an area under the disease progression curve of 3228.9 units. Promising genotypes CP-041 and CP-052 demonstrated lower rust intensity. Environmental parameters, including dew point, temperature, precipitation, and relative humidity, were correlated with rust severity and lesion numbers. In greenhouse assays, hybrid JAT 001165 showed higher severity, whereas JAT 001103 and JAT 001164 had more lesions than other genotypes. In contrast, promising genotypes CP-041 and CP-052 consistently exhibited lower rust intensity in both field and greenhouse environments. This study demonstrated that P. arthuriana could be epidemiologically modeled with the Weibull model, providing crucial insights into the dynamic interplay between rust infection and physic nut hybrids and genotypes under diverse conditions in the Manabí region of Ecuador. Full article

Geophytes are a characteristic element of deciduous forests in the temperate zone, as well as a common component of urban green spaces due to their early flowering and high decorative value. Nevertheless, in urban areas, geophytes are constantly threatened by recreational activities, especially in parks where intensive trampling occurs. In this study, we tested the effect of the distance from formal paths on the species richness, abundance and diversity of geophytes in relation to habitat conditions in urban forests and parks. We established a total of 400 plots (1 m × 1 m) located close (CL) to and further (FU) from paths in 10 forests and 10 parks in Kraków, southern Poland, in spring 2022. We recorded 23 species from nine groups of geophytes forming different underground storage organs, i.e., bulbs (B), hypocotyl bulbs (HB), rhizomes (RH), runners (RU), runners and rhizomes (RU-RH), runner-like rhizomes (RL-RH), runners and runners with tuberous tip (RU-TU), runners and shoot tubers (RU-ST) and root tubers (RT). The differences in the number, share and cover-abundance of geophytes between the CL and FU plots were statistically insignificant. In contrast, the total number, share and cover-abundance of geophytes were significantly higher in forests than in parks. Additionally, the share and cover-abundance of RH and RT were significantly higher in forests than in parks. Moreover, in CL plots in forests and parks, the cover-abundance of RH and RT were negatively correlated with soil compaction. Urban forests provide a high abundance of RH, RU-RH and RT, while parks support a high abundance of BU. To protect forest geophytes in urban forests and parks, it is recommended to limit trampling and soil eutrophication, as well as reduce the increase in soil pH along paths. Full article