Search Results (4,674)

Grapevine (Vitis vinifera L.) cultivation is an important agricultural sector worldwide. Its expansion into new areas, like Kazakhstan, brings significant phytosanitary risks. Viroids, such as grapevine yellow speckle viroid 1 (GYSVd-1) and hop stunt viroid (HSVd), are RNA pathogens that threaten vineyard productivity. They can cause a progressive decline through latent infections. Traditional diagnostic methods are usually targeted and therefore not suitable for thorough surveillance. In contrast, modern high-throughput sequencing (HTS) methods often face challenges due to their high costs and complicated sample preparation, such as ribosomal RNA (rRNA) depletion. This study introduces a simplified diagnostic workflow that overcomes these barriers. We utilized the latest Oxford Nanopore V14 cDNA chemistry, which is designed to prevent internal priming, by substituting a targeted oligo(dT)VN priming strategy to facilitate the sequencing of non-polyadenylated viroids from total RNA extracts, completely bypassing the rRNA depletion step and use of random oligonucleotides for c DNA synthesis. This method effectively detects and identifies both GYSVd-1 and HSVd. This workflow significantly reduces the time, cost, and complexity of HTS-based diagnostics. It provides a powerful and scalable tool for establishing strong genomic surveillance and phytosanitary certification programs, which are essential for supporting the growing viticulture industry in Kazakhstan. Full article

Peanut cultivation is widely practiced using plastic mulch film, resulting in the accumulation of microplastics/nanoplastics (MPs/NPs) in agricultural soils, potentially negatively affecting peanut growth. To investigate the effects of two polystyrene (PS) sizes (5 μm, 50 nm) and three concentrations (0, 10, and 100 mg L⁻¹) on peanut growth, photosynthetic efficiency, and physiological characteristics, a 15-day hydroponic experiment was conducted using peanut seedlings as the experimental material. The results indicated that PS-MPs/NPs inhibited peanut growth, reduced soil and plant analyzer development (SPAD) values (6.7%), and increased levels of malondialdehyde (MDA, 22.0%), superoxide anion (O₂⁻, 3.8%) superoxide dismutase (SOD, 16.1%) and catalase (CAT, 12.1%) activity, and ascorbic acid (ASA, 12.6%) and glutathione (GSH, 9.1%) contents compared to the control. Moreover, high concentrations (100 mg L⁻¹) of PS-MPs/NPs reduced the peanut shoot fresh weight (16.1%) and SPAD value (7.2%) and increased levels of MDA (17.1%), O₂⁻ (5.6%), SOD (10.6%), POD (27.2%), CAT (7.3%), ASA (12.3%), and GSH (6.8%) compared to low concentrations (10 mg L⁻¹) of PS-MPs/NPs. Notably, under the same concentration, the impact of 50 nm PS-NPs was stronger than that of 5 μm PS-MPs. The peanut shoot fresh weight of PS-NPs was lower than that of PS-MPs by an average of 7.9%. Additionally, we found that with an increasing exposure time of PS-MPs/NPs, the inhibitory effect of low concentrations of PS-MPs/NPs on the fresh weight was decreased by 2.5%/9.9% (5 d) and then increased by 7.7%/2.7% (15 d). Conversely, high concentrations of PS-MPs/NPs consistently reduced the fresh weight. Correlation analysis revealed a clear positive correlation between peanut biomass and both the SPAD values as well as Fv/Fm, and a negative correlation with MDA, SOD, CAT, ASA, and GSH. Furthermore, the presence of PS-MPs/NPs in roots, stems, and leaves was confirmed using a confocal laser scanning microscope. The internalization of PS-MPs/NPs within peanut tissues negatively impacted peanut growth by increasing the MDA and O₂⁻ levels, reducing the SPAD values, and inhibiting the photosynthetic capacity. In conclusion, the study demonstrated that the effects of PS on peanuts were correlated with the PS size, concentration, and exposure time, highlighting the potential risk of 50 nm to 5 μm PS being absorbed by peanuts. Full article

Current learning and development approaches often struggle to capture dynamic individual capabilities, particularly the skills they acquire informally every day on the job. This dynamic creates a significant gap between what traditional models think people know and their actual performance, leading to an incomplete and often outdated understanding of how ready the workforce truly is, which can hinder organizational adaptability in rapidly evolving environments. This paper proposes a novel dynamic learner-state ecosystem—an AI-driven solution designed to bridge this gap. Our approach leverages specialized AI agents, orchestrated via the Model Context Protocol (MCP), to continuously track and evolve an individual’s multi-dimensional state (e.g., mastery, confidence, context, and decay). The seamless integration of in-workflow performance data will transform daily work activities into granular and actionable data points through AI-powered dynamic xAPI generation into Learning Record Stores (LRSs). This system enables continuous, authentic performance-based assessment, precise skill gap identification, and highly personalized interventions. The significance of this ecosystem lies in its ability to provide a real-time understanding of everyone’s capabilities, enabling more accurate workforce planning for the future and cultivating a workforce that is continuously learning and adapting. It ultimately helps to transform learning from a disconnected, occasional event into an integrated and responsive part of everyday work. Full article

Enhancing the yield and qualitative traits of horticultural crops without further hampering the environment constitutes an urgent challenge that could be addressed by implementing innovative agronomic tools, such as plant biostimulants. This study investigated the effects of three commercial biostimulants—BIO1 (fulvic/humic acids), BIO2 (leonardite-humic acids), and BIO3 (plant-based extracts)—on leaf ecophysiology, yield, and fruit quality in two raspberry cultivars, ‘Autumn Bliss’ (AB) and ‘Zeva’ (Z), grown in an open-field context, to assess their effectiveness in raspberry cultivation. Experimental activities involved two Research Years (RYs), namely, year 2023 (RY 1) and 2024 (RY 2). Leaf parameters such as chlorophyll, flavonols, anthocyanins, and the Nitrogen Balance Index (NBI) were predominantly influenced by the interaction between Treatment, Year and Cultivar factors, indicating context-dependent responses rather than direct biostimulant effects. BIO2 showed a tendency to increase yield (g plant⁻¹) and berry number plant⁻¹, particularly in RY 2 (417.50 g plant⁻¹, +33.93% vs. control). Fruit quality responses were cultivar and time-specific: BIO3 improved soluble solid content in AB (12.8 °Brix, RY 2, Intermediate Harvest) and Z (11.43 °Brix, +13.91% vs. BIO2). BIO2 reduced titratable acidity in AB (3.12 g L⁻¹) and increased pH in Z (3.02, RY 2) but also decreased °Brix in Z. These findings highlight the potential of biostimulants to modulate raspberry physiology and productivity but underscore the critical role of cultivar, environmental conditions, and specific biostimulant composition in determining the outcomes, which were found to critically depend on tailored application strategies. Full article

Direct seeding has gained prominence as a labor-efficient and environmentally sustainable alternative to conventional transplanting in rice cultivation. In direct seeding systems, early-stage management is crucial for stable seedling establishment, with sowing uniformity measured by seed counts being a critical indicator of success. However, conventional manual seed counting methods are time-consuming, prone to human error, and impractical for large-scale or repetitive tasks, necessitating advanced automated solutions. Recent advances in computer vision technologies and precision agriculture tools, offer the potential to automate seed counting tasks. Nevertheless, challenges such as domain discrepancies and limited labeled data restrict robust real-world deployment. To address these issues, we propose a density estimation-based seed counting framework integrating semi-supervised learning and background augmentation. This framework includes a cost-effective data acquisition system enabling diverse domain data collection through indoor background augmentation, combined with semi-supervised learning to utilize augmented data effectively while minimizing labeling costs. The experimental results on field data from unknown domains show that our approach reduces seed counting errors by up to 58.5% compared to conventional methods, highlighting its potential as a scalable and effective solution for agricultural applications in real-world environments. Full article

With the expansion of muskmelon cultivation, manual pollination is increasingly inadequate for sustaining industry development. Therefore, the development of automatic pollination robots holds significant importance in improving pollination efficiency and reducing labor dependency. Accurate flower detection and localization is a key technology for enabling automated pollination robots. In this study, the YOLO-MDL model was developed as an enhancement of YOLOv7 to achieve real-time detection and localization of muskmelon flowers. This approach adds a Coordinate Attention (CA) module to focus on relevant channel information and a Contextual Transformer (CoT) module to leverage contextual relationships among input tokens, enhancing the model’s visual representation. The pollination robot converts the 2D coordinates into 3D coordinates using a depth camera and conducts experiments on real-time detection and localization of muskmelon flowers in a greenhouse. The YOLO-MDL model was deployed in ROS to control a robotic arm for automatic pollination, verifying the accuracy of flower detection and measurement localization errors. The results indicate that the YOLO-MDL model enhances AP and F1 scores by 3.3% and 1.8%, respectively, compared to the original model. It achieves AP and F1 scores of 91.2% and 85.1%, demonstrating a clear advantage in accuracy over other models. In the localization experiments, smaller errors were revealed in all three directions. The RMSE values were 0.36 mm for the X-axis, 1.26 mm for the Y-axis, and 3.87 mm for the Z-axis. The YOLO-MDL model proposed in this study demonstrates strong performance in detecting and localizing muskmelon flowers. Based on this model, the robot can execute more precise automatic pollination and provide technical support for the future deployment of automatic pollination robots in muskmelon cultivation. Full article

The expression of recombinant proteins in heterologous hosts is a common strategy to obtain larger quantities of the “protein of interest” (POI) for scientific, therapeutic or commercial purposes. However, the experimental success of such an approach critically depends on the choice of an appropriate host system to obtain biologically active forms of the POI. The correct folding of the molecule, mediated by disulfide bond formation, is one of the most critical steps in that process. Here we describe the recombinant expression of hirudin, a leech-derived anticoagulant and thrombin inhibitor, in the yeast Komagataella phaffii (formerly known and mentioned throughout this publication as Pichia pastoris) and in two different strains of Escherichia coli, one of them being especially designed for improved disulfide bond formation through expression of a protein disulfide isomerase. Cultivation of the heterologous hosts and expression of hirudin were performed at different temperatures, ranging from 22 to 42 °C for the bacterial strains and from 20 to 30 °C for the yeast strain, respectively. The thrombin-inhibitory potencies of all hirudin preparations were determined using the thrombin time coagulation assay. To our surprise, the hirudin preparations of P. pastoris were considerably less potent as thrombin inhibitors than the respective preparations of both E. coli strains, indicating that a eukaryotic background is not per se a better choice for the expression of a biologically active eukaryotic protein. The hirudin preparations of both E. coli strains exhibited comparable high thrombin-inhibitory potencies when the strains were cultivated at their respective optimal temperatures, whereas lower or higher cultivation temperatures reduced the inhibitory potencies. Full article

Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems. Full article

Mushrooms are eukaryotic organisms with absorptive heterotrophic nutrition, capable of feeding on organic matter rich in cellulose and lignocellulose. Since ancient times, they have been considered allies and, in certain cultures, they were seen as magical beings or food of the gods. Of the great variety of edible mushrooms identified worldwide, less than 2% are traded on the market. Although mushrooms have been valued for their multiple nutritional and healing benefits, some cultures perceive them as toxic and do not accept them in their culinary practices. Despite the existing skepticism, several researchers are promoting the potential of edible mushrooms. There are two main methods of mushroom cultivation: solid-state fermentation and submerged fermentation. The former is the most widely used and simplest, since the fungus grows in its natural environment; in the latter, the fungus grows suspended without developing a fruiting body. In addition, submerged fermentation is easily monitored and scalable. Both systems are important and have their limitations. This article discusses the main methods used to increase the performance of submerged fermentation with emphasis on the modes of operation used, types of bioreactors and application of morphological bioengineering of filamentous fungi, and especially the use of intelligent automatic control technologies and the use of non-invasive monitoring in fermentation systems thanks to the development of machine learning (ML), neural networks, and the use of big data, which will allow more accurate decisions to be made in the fermentation of filamentous fungi in submerged environments with improvements in production yields. Full article

The accuracy of classification and localization of plants on images obtained from the board of an unmanned aerial vehicle (UAV) is of great importance when implementing precision farming technologies. It allows for the effective application of variable rate technologies, which not only saves chemicals but also reduces the environmental load on cultivated fields. Machine learning algorithms are widely used for plant classification. Research on the application of the YOLO algorithm is conducted for simultaneous identification, localization, and classification of plants. However, the quality of the algorithm significantly depends on the training set. The aim of this study is not only the detection of a cultivated plant (soybean) but also weeds growing in the field. The dataset developed in the course of the research allows for solving this issue by detecting not only soybean but also seven weed species common in the fields of Kazakhstan. The article describes an approach to the preparation of a training set of images for soybean fields using preliminary thresholding and bound box (Bbox) segmentation of marked images, which allows for improving the quality of plant classification and localization. The conducted research and computational experiments determined that Bbox segmentation shows the best results. The quality of classification and localization with the application of Bbox segmentation significantly increased (f1 score increased from 0.64 to 0.959, mAP50 from 0.72 to 0.979); for a cultivated plant (soybean), the best classification results known to date were achieved with the application of YOLOv8x on images obtained from the UAV, with an f1 score = 0.984. At the same time, the plant detection rate increased by 13 times compared to the model proposed earlier in the literature. Full article

The cigarette butt (CB) recycling process yields several byproducts, including cleaned filters, solid debris (mainly paper and tobacco), and wastewater. This study aimed to assess, for the first time, the long-term suitability of these recycled byproducts for turfgrass cultivation. Under controlled conditions, Paspalum vaginatum Swartz was grown in sand–peat substrate, either unmodified (control) or amended with small pieces of uncleaned CBs or solid byproducts from CB recycling at concentrations of 25% or 50% (v/v). In additional tests, turfgrass grown in unmodified substrate received wastewater instead of tap water once or twice weekly. Over 7 weeks, physiological and biometric parameters were assessed. Plants grown with solid debris showed traits comparable to the control. Those grown with intact CBs or cleaned filters had similar biomass and coverage as the control but accumulated more carotenoids and antioxidants. Wastewater significantly enhanced plant growth when applied once weekly, while becoming toxic when applied twice, reducing biomass and coverage. After scalping, turfgrass recovered well across all treatments, and in some cases biomass improved. Overall, recycled CB byproducts, particularly wastewater used at optimal concentrations, can be a sustainable resource for promoting turfgrass growth. Full article

This article examines the concept of ming 命 (mandate/command or fate/destiny) in the Analects, Mencius, and Zhuangzi, exploring its relationship to tian 天 (Heaven). Across these works, ming retains an intrinsic connection to tian—an inviolable cosmic force beyond human control. All three texts exhibit profound reverence and submission to tian, acknowledging the boundary between human control and cosmic inevitability, yet, at the same time, advocating active alignment with tian’s ordained patterns. In the Analects, a central tension emerges between tian’s teleological purpose—centered on preserving human culture and ethical cultivation—and the seemingly arbitrary fluctuations of individual fate, particularly regarding lifespan and personal fulfillment. This tension persists in the Mencius, articulated as a conflict between the political disorder of Mencius’ contemporary era and tian’s normative moral order. The Zhuangzi, by contrast, resolves this tension through advocating for withdrawal from the political life, as well as a radical reinterpretation of tian. Stripping tian off the Confucian moral–cultural imperatives, the text deconstructs dichotomies like life and death, championing inner equanimity via flowing with the cosmic transformation. Full article

Winery wastewater (WWW), if released untreated, poses a serious environmental threat due to its high organic load. In this study, Chlorella vulgaris was cultivated in diluted WWW to assess its suitability as a culture medium. Two outdoor cultivation systems—a 270 L raceway and a 40 L bubble column—were operated over 33 days using synthetic medium (control) and WWW. A flow cytometry (FC) protocol was implemented to monitor key physiological parameters in near-real time, including cell concentration, membrane integrity, chlorophyll content, cell size, and internal complexity. At the end of cultivation, the bubble column yielded the highest cell concentrations: 2.85 × 10⁶ cells/mL (control) and 2.30 × 10⁶ cells/mL (WWW), though with lower proportions of intact cells (25% and 31%, respectively). Raceway cultures showed lower cell concentrations: 1.64 × 10⁶ (control) and 1.54 × 10⁶ cells/mL (WWW), but higher membrane integrity (76% and 36% for control and WWW cultures, respectively). On average, cells grown in the bubble column had a 22% larger radius than those in the raceway, favouring sedimentation. Heterotrophic cells were more abundant in WWW cultures, due to the presence of organic carbon, indicating its potential for use as animal feed. This study demonstrates that FC is a powerful, real-time tool for monitoring microalgae physiology and optimising cultivation in complex effluents like WWW. Full article

Potatoes are one of the world’s most widely cultivated crops, but their yield is coming under mounting pressure from early blight, a fungal disease caused by Alternaria solani. Early detection and accurate identification are key to effective disease management and yield protection. This paper introduces a novel deep learning framework called ZeroShot CNN, which integrates convolutional neural networks (CNNs) and ZeroShot Learning (ZSL) for the efficient classification of seen and unseen disease classes. The model utilizes convolutional layers for feature extraction and employs semantic embedding techniques to identify previously untrained classes. Implemented on the Kaggle potato disease dataset, ZeroShot CNN achieved 98.50% accuracy for seen categories and 99.91% accuracy for unseen categories, outperforming conventional methods. The hybrid approach demonstrated superior generalization, providing a scalable, real-time solution for detecting agricultural diseases. The success of this solution validates the potential in harnessing deep learning and ZeroShot inference to transform plant pathology and crop protection practices. Full article

The need for systems that forecast and respond proactively to meteorological disasters is growing amid climate variability. Although the early warning system in South Korea includes countermeasure information, it remains limited in terms of data recency, granularity, and regional adaptability. Additionally, its closed architecture hinders interoperability with external systems. This study aims to redesign the countermeasure function as an independent cloud-based platform grounded in the common standard terminology framework in South Korea. A multi-dimensional data model was developed using attributes such as crop type, cultivation characteristics, growth stage, disaster type, and risk level. The platform incorporates user-specific customization features and history tracking capabilities, and it is structured using a microservices architecture to ensure modularity and scalability. The proposed system enables real-time management and dissemination of localized countermeasure suggestions tailored to various user types, including central and local governments and farmers. This study offers a practical model for enhancing the precision and applicability of agrometeorological response information. It is expected to serve as a scalable reference platform for future integration with external agricultural information systems. Full article