Hasil untuk "Plant culture"

Haixiao Dong, Hao Chen, Yuan Jiang et al.

Abstract Maize (Zea mays L.) kernel mutants are valuable tools for investigating kernel development. In this study, we employed ethyl methanesulfonate (EMS) mutagenesis of pollen on five inbred lines, which displayed varying performance after treatment. Over 400 independent kernel mutants were generated, showing a wide range of defects in both type and severity. Bulked segregant analysis (BSA) combined with whole‐genome sequencing was employed to map two representative mutants. For a shrunken kernel mutant, a missense mutation (P155L) was identified in the classical ZmBT1 gene, which encodes an ADP‐glucose transporter. For a small kernel mutant, a start‐lost mutation (M1?) was discovered in the ZmTOP6A gene, which encodes the DNA topoisomerase VI subunit A. Allelic verification of ZmBT1 and ZmTOP6A confirmed their association with the mutant phenotypes. Furthermore, we analyzed the protein conservation, expression patterns, and subcellular localization of both genes. Our study highlights the effectiveness of combining EMS mutagenesis with BSA for mining maize kernel genes. The mutants and the identified genes will advance our understanding of maize kernel development.

Fang Bai, Sean P. Donohoe, Abdelraheem Abdelraheem et al.

Water deficiency is a prevalent abiotic stress that significantly constrains cotton productivity worldwide. This study aimed to evaluate the impact of water deficiency on ginning efficiency, fiber quality, and seed composition in cotton (Gossypium spp.). Ten cotton genotypes were assessed under irrigated and non-irrigated field conditions. Water deficiency markedly reduced plant height and the number of bolls, with genotypes MD 52ne, MD 25-26ne, and 1517–99 displaying high sensitivity, whereas CIM 432 exhibited notable water deficiency tolerance. Ginning efficiency analysis showed a general reduction in energy requirements under water deficiency, particularly in MD10-5, MD 15, and MD 52ne. CIM 432, however, maintained high boll numbers, and stable ginning rate and ginning energy under stress. Fiber quality traits such as length, strength, and uniformity were adversely affected by water deficiency across most genotypes, although CIM 432, MD 15 and 84524 showed greater stability. Correlation analyses under water deficiency revealed strong positive associations among fiber length, strength, and uniformity, along with a significant negative correlation between lint percentage and oil content, suggesting a trade-off between lint yield and seed oil accumulation. Cottonseed composition analysis indicated that when oil content declined under water deficiency, protein and seed fiber levels remained relatively unaffected. Significant genotypic variation was observed for most traits, with minimal genotype-by-treatment interactions, indicating consistent genotype performance across irrigated and non-irrigated treatments. Overall, CIM 432 emerged as a robust candidate for breeding water deficiency-tolerant cotton, combining agronomic resilience with stable fiber quality. These findings underscore the complexity of genotype-water deficiency stress interactions and highlight the importance of integrated phenotypic assessment for developing water deficiency-tolerant cotton varieties.

Wakana Nakao, Hiroyuki Daimon, Atsushi Matsumura

Producing cash and green manure crops during the same growing season is a promising strategy for sustainable crop production. The present study evaluated the relay intercropping of a legume green manure crop (Vicia villosa Roth, hairy vetch) and root crops (radish and carrot). The hairy vetch was interseeded approximately 40 d after root crop seeding. Competitive effects were not observed in the production of either root crop, even when relay intercropped with hairy vetch. The biomass production of hairy vetch ranged from 376–990 kg DW 10a−1, and the biomass and nutrient (nitrogen, phosphorus, and potassium) accumulation of hairy vetch relay intercropped with carrot were significantly higher than those of radish. The fertilizer effects of hairy vetch incorporation obtained by relay intercropping were compared with those of conventional chemical fertilizer input in subsequent crop production (vegetable soybean and sweet corn). The yield of vegetable soybean was maintained at the same level as that of chemical fertilizer, even when cultivated with hairy vetch alone. In the sweet corn, although hairy vetch alone could not provide sufficient nutrients and growth was inferior compared to chemical fertilizers, the incorporation of hairy vetch relay intercropped with carrot showed relatively higher fertilizer effects and significant difference in ear weight was not observed with chemical fertilizer. Our results show the possibility of reducing chemical fertilizer input in succeeding crop production by hairy vetch relay intercropped with root crops.

James Winters, Mathieu Charbonneau

Humans stand alone in terms of their potential to collectively and cumulatively change their culture in an open-ended manner. This open-endedness provides societies with the ability to continually expand their resources and to increase their capacity to store, transmit and process information at a collective-level. Here, we propose that the production of resources arises from the interaction between cultural systems (a society's repertoire of interdependent techniques, artifacts, norms and knowledge) and search spaces (an ensemble of needs, problems and goals facing a society). Starting from this premise we develop a macro-level model wherein both cultural systems and search spaces are subject to evolutionary dynamics. By manipulating the extent to which these dynamics are characterised by stochastic or selection-like processes, we demonstrate that open-ended growth is extremely rare, historically contingent and only possible when cultural systems and search spaces co-evolve. Here, stochastic factors must be strong enough to continually perturb the dynamics into a far-from-equilibrium state, whereas selection-like factors help maintain effectiveness and ensure the sustained production of resources. Only when this co-evolutionary dynamic maintains effective cultural systems, supports the ongoing expansion of the search space and leads to an increased provision of resources do we observe open-ended cultural evolution.

Herve Goeau, Pierre Bonnet, Alexis Joly

It is estimated that there are more than 300,000 species of vascular plants in the world. Increasing our knowledge of these species is of paramount importance for the development of human civilization (agriculture, construction, pharmacopoeia, etc.), especially in the context of the biodiversity crisis. However, the burden of systematic plant identification by human experts strongly penalizes the aggregation of new data and knowledge. Since then, automatic identification has made considerable progress in recent years as highlighted during all previous editions of PlantCLEF. Deep learning techniques now seem mature enough to address the ultimate but realistic problem of global identification of plant biodiversity in spite of many problems that the data may present (a huge number of classes, very strongly unbalanced classes, partially erroneous identifications, duplications, variable visual quality, diversity of visual contents such as photos or herbarium sheets, etc). The PlantCLEF2022 challenge edition proposes to take a step in this direction by tackling a multi-image (and metadata) classification problem with a very large number of classes (80k plant species). This paper presents the resources and evaluations of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of key findings.

Vladimir Martirosyan, Rachit Kamdar

Online reviews shape impressions across products and workplaces. Employer reviews combine narratives and ratings that reflect culture. Glassdoor permits fully anonymous posts; Blind requires employment verification while preserving anonymity. We ask how verification changes reviews. Evidence suggests verified reviews can be more trustworthy, yet verification can also erode authenticity when expectations are unmet. We use the Competing Values Framework (clan, adhocracy, hierarchy, market) and the CultureBERT model by Koch and Pasch, 2023 to over 300k ratings. We find that Blind reviews emphasize clan and hierarchy while Glassdoor skews positive and highlights clan and market. Verification on its own does not remove bias but shifts how culture is represented. Job seekers using different platforms receive systematically different signals about workplace culture, affecting application decisions and job-matching.

Arnav Yayavaram, Siddharth Yayavaram, Simran Khanuja et al.

As text-to-image models become increasingly prevalent, ensuring their equitable performance across diverse cultural contexts is critical. Efforts to mitigate cross-cultural biases have been hampered by trade-offs, including a loss in performance, factual inaccuracies, or offensive outputs. Despite widespread recognition of these challenges, an inability to reliably measure these biases has stalled progress. To address this gap, we introduce CAIRe, an evaluation metric that assesses the degree of cultural relevance of an image, given a user-defined set of labels. Our framework grounds entities and concepts in the image to a knowledge base and uses factual information to give independent graded judgments for each culture label. On a manually curated dataset of culturally salient but rare items built using language models, CAIRe surpasses all baselines by 22% F1 points. Additionally, we construct two datasets for culturally universal concepts, one comprising T2I-generated outputs and another retrieved from naturally occurring data. CAIRe achieves Pearson's correlations of 0.56 and 0.66 with human ratings on these sets, based on a 5-point Likert scale of cultural relevance. This demonstrates its strong alignment with human judgment across diverse image sources.

Ayushi Sharma, Johanna Trost, Daniel Lusk et al.

Global plant maps of plant traits, such as leaf nitrogen or plant height, are essential for understanding ecosystem processes, including the carbon and energy cycles of the Earth system. However, existing trait maps remain limited by the high cost and sparse geographic coverage of field-based measurements. Citizen science initiatives offer a largely untapped resource to overcome these limitations, with over 50 million geotagged plant photographs worldwide capturing valuable visual information on plant morphology and physiology. In this study, we introduce PlantTraitNet, a multi-modal, multi-task uncertainty-aware deep learning framework that predictsfour key plant traits (plant height, leaf area, specific leaf area, and nitrogen content) from citizen science photos using weak supervision. By aggregating individual trait predictions across space, we generate global maps of trait distributions. We validate these maps against independent vegetation survey data (sPlotOpen) and benchmark them against leading global trait products. Our results show that PlantTraitNet consistently outperforms existing trait maps across all evaluated traits, demonstrating that citizen science imagery, when integrated with computer vision and geospatial AI, enables not only scalable but also more accurate global trait mapping. This approach offers a powerful new pathway for ecological research and Earth system modeling.

Auwalu Abdullahi Umar, Muneer Ahmad, Dr M Sadik Batcha

The significance of libraries in preserving and maintaining history and traditional culture cannot be overlooked. It is from this purpose that libraries are to envisage in their programmes cultural activities which must be collected, documented and preserved for posterity. The usefulness of preserved information lies in the fact that the generation to come will be able to establish their identity. This will also assist them with a foundation to build from. This study focus on the growth and development of Library and Culture research in forms of publications reflected in Web of Science database, during the span of 2010-2019. A total 890 publications were found and the highest 124 (13.93%) publications published in 2019.The analysis maps comprehensively the parameters of total output, growth of output, authorship, institution wise and country-level collaboration patterns, major contributors (individuals, top publication sources, institutions, and countries). It exposed that the most prolific author is Lo P secured first place by contributing 4 (0.45%) publications, followed by Bressan V 3 (0.34%) publications in Library and Culture literature. Journal of Academic Librarianship produced the highest number of records 29 (3.26%) followed by Australian Library Journal having contributed 21 (2.36%).It is identified the domination of Wuhan University; School Information Management had contributed 6 (0.67%) of total research output. Authors from USA published the highest number of publications with a total of 244 (27.42%), followed by UK and Australia with 118 (13.26%) and 76 (8.54%) publications were produced respectively.

Mozhgan Hadadi, Talukder Z. Jubery, Patrick S. Schnable et al.

Accurate 3D plant models are crucial for computational phenotyping and physics-based simulation; however, current approaches face significant limitations. Learning-based reconstruction methods require extensive species-specific training data and lack editability. Procedural modeling offers parametric control but demands specialized expertise in geometric modeling and an in-depth understanding of complex procedural rules, making it inaccessible to domain scientists. We present FloraForge, an LLM-assisted framework that enables domain experts to generate biologically accurate, fully parametric 3D plant models through iterative natural language Plant Refinements (PR), minimizing programming expertise. Our framework leverages LLM-enabled co-design to refine Python scripts that generate parameterized plant geometries as hierarchical B-spline surface representations with botanical constraints with explicit control points and parametric deformation functions. This representation can be easily tessellated into polygonal meshes with arbitrary precision, ensuring compatibility with functional structural plant analysis workflows such as light simulation, computational fluid dynamics, and finite element analysis. We demonstrate the framework on maize, soybean, and mung bean, fitting procedural models to empirical point cloud data through manual refinement of the Plant Descriptor (PD), human-readable files. The pipeline generates dual outputs: triangular meshes for visualization and triangular meshes with additional parametric metadata for quantitative analysis. This approach uniquely combines LLM-assisted template creation, mathematically continuous representations enabling both phenotyping and rendering, and direct parametric control through PD. The framework democratizes sophisticated geometric modeling for plant science while maintaining mathematical rigor.

Sergio J. Ochatt

Chen-Chi Chang, Ching-Yuan Chen, Hung-Shin Lee et al.

This study introduces a comprehensive benchmark designed to evaluate the performance of large language models (LLMs) in understanding and processing cultural knowledge, with a specific focus on Hakka culture as a case study. Leveraging Bloom's Taxonomy, the study develops a multi-dimensional framework that systematically assesses LLMs across six cognitive domains: Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating. This benchmark extends beyond traditional single-dimensional evaluations by providing a deeper analysis of LLMs' abilities to handle culturally specific content, ranging from basic recall of facts to higher-order cognitive tasks such as creative synthesis. Additionally, the study integrates Retrieval-Augmented Generation (RAG) technology to address the challenges of minority cultural knowledge representation in LLMs, demonstrating how RAG enhances the models' performance by dynamically incorporating relevant external information. The results highlight the effectiveness of RAG in improving accuracy across all cognitive domains, particularly in tasks requiring precise retrieval and application of cultural knowledge. However, the findings also reveal the limitations of RAG in creative tasks, underscoring the need for further optimization. This benchmark provides a robust tool for evaluating and comparing LLMs in culturally diverse contexts, offering valuable insights for future research and development in AI-driven cultural knowledge preservation and dissemination.

Junchen Deng, Samhita Marri, Jonathan Klein et al.

Robotic harvesting has the potential to positively impact agricultural productivity, reduce costs, improve food quality, enhance sustainability, and to address labor shortage. In the rapidly advancing field of agricultural robotics, the necessity of training robots in a virtual environment has become essential. Generating training data to automatize the underlying computer vision tasks such as image segmentation, object detection and classification, also heavily relies on such virtual environments as synthetic data is often required to overcome the shortage and lack of variety of real data sets. However, physics engines commonly employed within the robotics community, such as ODE, Simbody, Bullet, and DART, primarily support motion and collision interaction of rigid bodies. This inherent limitation hinders experimentation and progress in handling non-rigid objects such as plants and crops. In this contribution, we present a plugin for the Gazebo simulation platform based on Cosserat rods to model plant motion. It enables the simulation of plants and their interaction with the environment. We demonstrate that, using our plugin, users can conduct harvesting simulations in Gazebo by simulating a robotic arm picking fruits and achieve results comparable to real-world experiments.

Reza Ahmadi, Mohammad Mahmoudi, Farid Shekari et al.

Zinc deficiency is one of the most widespread nutritional problems, affecting nearly one-third of the world population. In addition, it is known that zinc deficiency not only reduces crop yield but also its quality. The effect of different methods of zinc application on the growth, yield, and quality of safflower seeds under regular irrigation and interruption of irrigation from flowering to harvest (82 and 80 DAS in the first and second years, respectively) was evaluated. Zinc sulfate was applied in both soil and foliar methods. The zinc sulfate treatments include no zinc sulfate, soil application of 20, 40, and 60 kg ha⁻¹ at the planting stage; spraying 2.5, 5, and 7.5 g L⁻¹ in the rosette stage; and spraying 2.5, 5, and 7.5 g L⁻¹ in the flowering stage. The end-season drought caused a decrease in the chlorophyll index, leaf area index, relative water content, plant height, yield components, biological yield, seed yield, harvest index, seed oil content, oil harvest index, and seed element content compared to regular irrigation. The decrease in yield occurred with a decrease in the capitol number and diameter, seed number per capitol, and 1000-seed weight. The severity of the damage of the end-season drought stress in the second year was higher than in the first year due to the higher temperatures and the decrease in the rainfall. In both years, the application of zinc sulfate in different ways had an increasing effect on the studied traits in both normal and stress conditions. The application of zinc sulfate reduced the negative effects of unfavorable environmental conditions and improved the yield and nitrogen, phosphorus, potassium, zinc, and iron element content in the seed. In both application methods of zinc sulfate, the increment in the zinc sulfate concentration decreased the seed phosphorus content. However, the phosphorous content was more than that of the treatment of non-zinc application. The application of zinc increased the biological, seed, and oil yield of the treated plants, but the seed and oil yield were more affected. This effect was shown in the seed and oil harvest index increment. Under regular irrigation, higher concentrations of zinc sulfate enhanced plant performance, but under stress conditions, medium and lower concentrations were more effective. The highest 1000-seed weight and potassium and zinc content were obtained by spraying zinc sulfate at 5 g L⁻¹ in the flowering stage under normal irrigation conditions. A comparison of the two methods of applying zinc sulfate showed that foliar spraying was more effective than soil application in improving the seed yield. The soil application is more effective on biological yield than seed yield.

Selvaraj Arokiyaraj, Rajagopal Rajakrishnan, Subhanandaraj Russalamma Flanetraj

The study aim was to optimise the C/N ratio, improve the compost quality, reduce pathogenic bacteria load in the compost, and improve guava yield. Vegetable wastes were mixed with cow dung, grasses, and food wastes in ratios of 4:3:2:1 (w/w) for achieving a C/N ratio of approximately 37. Co-composting is an important strategy because the mixture of bulking agents can help achieve optimal composting conditions. Experimental results were obtained from a pilot-scale rotary drum reactor with forced aeration. In the reactor, the temperature increased during the thermophilic phase (58±2 °C) and decreased after 10 days (54±2 °C). The pH values moderately increased, then decreased, and were near to neutral after maturation. The results indicated that co-composting of bio-wastes at a C/N ratio of 37.6%±1.02% could be effectively decomposed to reduce the residuals to just 13.6%±1.05% after 28 days. The microbial population increased in both mesophilic and thermophilic stages and decreased at the end of the composting, reflecting stability. The stable compost was applied to the growth of guava plant, and the yield was calculated. The organic compost improved plant growth, fruit yield, and enriched phytochemical compounds in the fruit and peels. The phytochemical compounds improved antioxidant activity in the guava fruits.

Alejandro Rápalo-Cruz, Cintia Gómez-Serrano, Cynthia Victoria González-López et al.

The utilization of treated wastewater can enhance the crops’ irrigation efficacy by offering an extra source of water and nutrients for agricultural purposes. This methodology helps alleviate the pressure on conventional water resources and can be a sustainable strategy to address the challenges of water scarcity. However, it is essential to ensure that wastewater is properly treated to meet quality and safety standards before its application to agricultural crops. This study focuses on exploring the reuse of wastewater from microalgae production and its impact on <i>Pelargonium</i> × <i>hortorum</i> growth during two seasons (autumn and spring). The established treatments were as follows: tap water (control 1); 100% IW—inlet wastewater (control 2); 100% OW—outlet from the reactor; 75% OW + 25% W—75% outlet from the reactor and 25% tap water; and 50% OW + 50%W—50% outlet from the reactor and 50% tap water. Irrigation with wastewater in autumn did not have a significant negative effect (<i>p</i> > 0.05) on plant height, plant diameter, leaf dry weight, roots, or total dry weight, and it was comparable to the control in all applied percentages. On the other hand, wastewater irrigation during spring had a meaningful positive (<i>p</i> < 0.05) impact on plant growth compared to the control. These wastewater resources have a high concentration of nutrients, making them a valuable source of essential and/or beneficial elements. The levels of nutrients such as NO₃⁻ range from 144.08 ppm to 82.10 ppm, PO₄³⁻ ranges from 14.14 ppm to 7.11 ppm, and K⁺ ranges from 36.83 ppm to 29.71 ppm. Therefore, the obtained results support the viability and effectiveness of using wastewater after microalgae production in agriculture to reduce water demand, mitigate water pollution, and substitute chemical fertilizer input, contributing to more sustainable agricultural practices. These results, with more detailed evaluations, would be applicable to other related plant species and are even applicable to the commercial production sectors.

B. Rakesh, W. N. Sudheer, Praveen Nagella

José Martín-Roca, C. Miguel Barriuso-Gutiérrez, Raúl Martínez Fernández et al.

Carnivory in plants is an unusual trait that has arisen multiple times, independently, throughout evolutionary history. Plants in the genus Genlisea are carnivorous, and feed on microorganisms that live in soil using modified subterranean leaf structures (rhizophylls). A surprisingly broad array of microfauna has been observed in the plants' digestive chambers, including ciliates, amoebae and soil mites. Here we show, through experiments and simulations, that Genlisea exploit active matter physics to 'rectify' bacterial swimming and establish a local flux of bacteria through the structured environment of the rhizophyll towards the plant's digestion vesicle. In contrast, macromolecular digestion products are free to diffuse away from the digestion vesicle and establish a concentration gradient of carbon sources to draw larger microorganisms further inside the plant. Our experiments and simulations show that this mechanism is likely to be a localised one, and that no large-scale efflux of digested matter is present.

Anqi Xie, Jingyue Wu, Yajie Shi et al.

To identify herbaceous peony cultivars with strong photosynthetic productivity, we compared the photosynthetic characteristics of 20 herbaceous peony cultivars based on four photosynthetic characteristics parameters and established light–response curves under a light intensity gradient, using CIRAS-3 portable photosynthetic dynamic monitoring. The net photosynthetic rate (Pn) showed a “unimodal” diurnal variation pattern, with a peak around 12:00. The diurnal pattern of the transpiration rate was the same as that of Pn. Stomatal conductance values (Gs) showed similar patterns among the cultivars, with only small differences. The daily variation in intracellular CO₂ concentration (Ci) showed an opposite trend to that of Pn. When the photosynthetically active radiation was 0–400 μmol·m⁻²s⁻¹, Pn increased linearly and gradually with increasing light intensity. ‘Xueyuanhonghua’, ‘Qingwen’, ‘Taohuafeixue’, ‘Chifen’, and ‘Qihualushuang’ showed high photosynthetic productivity. ‘Xueyuanhonghua’, ‘Fushi’, ‘Qingwen’, ‘Tianshanhongxing’, ‘Qingtianlan’, ‘Dafugui’, and ‘Hongfushi’ had high light saturation points and the highest light resistance. ‘Xueyuanhonghua’, ‘Qingwen’, ‘Taohuafeixue’, ‘Tianshanhongxing’, ‘Qingtianlan’, ‘Guifeichacui’, ‘Chifen’, and ‘Hongxiuqiu’ had low light compensation points. Thus, two cultivars with strong photosynthetic productivity, ‘Xueyuanhonghua’ and ‘Qingwen’, can be cross-bred to obtain both light- and shade-tolerant plants. This study provides a theoretical basis for breeding new cultivars with high photosynthetic productivity.

Tommaso Frioni, Chiara Pastore, Maria P. Diago et al.