Hasil untuk "Plant culture"

Daniel Mejer Christensen, Katja Stougård Jørgensen, Josefine Palsgaard Wyrtz et al.

Research in Human-Computer Interaction (HCI) has shown that caring for others, including both humans (e.g., close friends) and computers (e.g., Tamagotchi), can have a positive effect on people's wellbeing. However, we know less about the potential role of conversational AI in such settings. In this work, we explore how AI chatbots can support plant care and, in turn, positively influence people's well-being. We developed a mobile application that allows users to `talk' to their plants via chatbots. We evaluated the application with ten participants and conducted semi-structured interviews based on Seligman's PERMA model, which identifies pillars of psychological well-being. Our findings suggest positive effects, with participants reflecting on a sense of connection to their plants and corresponding feelings of accomplishment. While our findings suggest that participants were generally positive about the app, they also raised concerns about the diverse preferences and expectations of users regarding interactions with chatbots representing plants.

Yueqing Hu, Xinyang Peng, Yukun Zhao et al.

Recent scholarship typically characterizes Large Language Models (LLMs) through either an \textit{Instrumental Paradigm} (viewing models as reflections of their developers' culture) or a \textit{Substitutive Paradigm} (viewing models as bilingual proxies that switch cultural frames based on language). This study challenges these anthropomorphic frameworks by proposing \textbf{Machine Culture} as an emergent, distinct phenomenon. We employed a 2 (Model Origin: US vs. China) $\times$ 2 (Prompt Language: English vs. Chinese) factorial design across eight multimodal tasks, uniquely incorporating image generation and interpretation to extend analysis beyond textual boundaries. Results revealed inconsistencies with both dominant paradigms: Model origin did not predict cultural alignment, with US models frequently exhibiting ``holistic'' traits typically associated with East Asian data. Similarly, prompt language did not trigger stable cultural frame-switching; instead, we observed \textbf{Cultural Reversal}, where English prompts paradoxically elicited higher contextual attention than Chinese prompts. Crucially, we identified a novel phenomenon termed \textbf{Service Persona Camouflage}: Reinforcement Learning from Human Feedback (RLHF) collapsed cultural variance in affective tasks into a hyper-positive, zero-variance ``helpful assistant'' persona. We conclude that LLMs do not simulate human culture but exhibit an emergent Machine Culture -- a probabilistic phenomenon shaped by \textit{superposition} in high-dimensional space and \textit{mode collapse} from safety alignment.

Maria Guadalupe Castillo-Texta, Tania Belén Álvarez-Gómez, Mario Ramírez-Yáñez et al.

The APETALA2/Ethylene Response Factor (AP2/ERF) family of transcription factors (TF) is characterized by their participation in various biological processes related to growth, development, and response to stress. ERFs are ideal candidates for crop improvement because they regulate defense genes like <i>JERF1</i>, <i>JERF3</i>, <i>LeERF2</i>, <i>NtERF5</i>, and <i>Tsil</i> which confer tolerance to drought, salinity, osmotic stress, and pathogen attack, respectively. The ERF subfamily includes the TF Pti4, whose activity is regulated by different signaling pathways, thus providing tolerance response to multiple factors such as drought, salinity, cold, and pathogen attack in tomato. In this work we evaluated the effect of overexpression of TF <i>SlPti4</i> from <i>Solanum lycopersicum</i> in transgenic tobacco plants when subjected to saline, osmotic, and drought stress. Our results from this study demonstrated that transgenic lines overexpressing <i>Pti4</i> tolerate abiotic stress during germination and in plants. The transgenic lines showed improvements in photoinhibition, electron transport rate, chlorophyll content, and biomass, as well as a reduction in malondialdehyde content.

Wieslaw Kopeć, Anna Jaskulska, Władysław Fuchs et al.

Digital technologies and tools have transformed the way we can study cultural heritage and the way we can recreate it digitally. Techniques such as laser scanning, photogrammetry, and a variety of Mixed Reality solutions have enabled researchers to examine cultural objects and artifacts more precisely and from new perspectives. In this part of the panel, we explore how Virtual Reality (VR) and eXtended Reality (XR) can serve as tools to recreate and visualize the remains of historical cultural heritage and experience it in simulations of its original complexity, which means immersive and interactive. Visualization of material culture exemplified by archaeological sites and architecture can be particularly useful when only ruins or archaeological remains survive. However, these advancements also bring significant challenges, especially in the area of transdisciplinary cooperation between specialists from many, often distant, fields, and the dissemination of virtual immersive environments among both professionals and the general public.

Viktor Kozák, Jan Chudoba, Libor Přeučil

An accurate and up-to-date model of a photovoltaic (PV) power plant is essential for its optimal operation and maintenance. However, such a model may not be easily available. This work introduces a novel approach for PV power plant mapping based on aerial overview images. It enables the automation of the mapping process while removing the reliance on third-party data. The presented mapping method takes advantage of the structural layout of the power plants to achieve detailed modeling down to the level of individual PV modules. The approach relies on visual segmentation of PV modules in overview images and the inference of structural information in each image, assigning modules to individual benches, rows, and columns. We identify visual keypoints related to the layout and use these to merge detections from multiple images while maintaining their structural integrity. The presented method was experimentally verified and evaluated on two different power plants. The final fusion of 3D positions and semantic structures results in a compact georeferenced model suitable for power plant maintenance.

Balram Singh, Ram Prakash Sharma, Somnath Dey

Plant diseases pose a significant threat to global food security, necessitating accurate and interpretable disease detection methods. This study introduces an interpretable attention-guided Convolutional Neural Network (CNN), CBAM-VGG16, for plant leaf disease detection. By integrating Convolution Block Attention Module (CBAM) at each convolutional stage, the model enhances feature extraction and disease localization. Trained on five diverse plant disease datasets, our approach outperforms recent techniques, achieving high accuracy (up to 98.87%) and demonstrating robust generalization. Here, we show the effectiveness of our method through comprehensive evaluation and interpretability analysis using CBAM attention maps, Grad-CAM, Grad-CAM++, and Layer-wise Relevance Propagation (LRP). This study advances the application of explainable AI in agricultural diagnostics, offering a transparent and reliable system for smart farming. The code of our proposed work is available at https://github.com/BS0111/PlantAttentionCBAM.

Elise Schnabel, César Augusto Diniz Xavier, Anna E. Whitfield et al.

Numerous viruses infect blackberry, and they are associated with virus disease complexes with complicated etiologies. Blackberry virus diseases limit the lifespan of blackberry production in the Southeastern United States. Although some previous research has been conducted to understand which viruses are prevalent in South Carolina, a comprehensive study on the virome of blackberry has not been done in this region. Additionally, the role of wild Rubus as a virus inoculum source is likely underappreciated and represents a potential opportunity for disease management. We took a comprehensive approach to characterize viral genome sequences from known and novel viruses using metatranscriptomic sequencing of blackberry and wild Rubus spp. leaf samples collected in 2021 from eight sites across South Carolina. We detected 17 known and 6 novel plant viruses and describe relevant genome sequence information. Although the etiologies of these novel viruses are yet to be elucidated, they should be considered part of the blackberry/wild Rubus virome and further studied. We describe instances of potential connectivity of virus populations between cultivated blackberry and wild Rubus for several viruses at several sites. In addition to plant viruses, we describe numerous viruses likely associated with foliar fungi, referred to as Rubus leaf-associated viruses. This study revealed a diverse landscape of both known and novel viruses in blackberry and wild Rubus in South Carolina and has stimulated topics for future research, such as temporal analyses of virus spread at the landscape scale and investigating potential vectors and the biological relevance of novel viruses. [Figure: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Cristina Cebrián-Tarancón, Francisco Fernández-Roldán, M. Rosario Salinas et al.

This study assessed the quality of Tempranillo wines after 35 days of contact with two different doses of SEGs (“Shoots from vines - Enological - Granule”) and two micro-oxygenation levels, analysed at bottling and after six months of aging. Treated wines exhibited a smoother, more harmonious sensory profile, with well-integrated notes of toast, vanilla, nuts, and “SEGs”, than the control wine. Phenolic compounds decreased, except for trans-resveratrol, which mainly increased with higher SEGs doses. Regarding volatile compounds, the presence of vanillin and guaiacol in the treated wines was particularly notable, especially after six months in the bottle.

Christine Böttcher, Debra McDavid, Angelica M. Jermakow et al.

Conclusions: This study has demonstrated that a relatively simple and robust protoplast isolation, DNA-free protoplast transfection and plant regeneration workflow can be used to efficiently produce nontransgenic, diploid, edited clones with desired phenotypes of four elite winegrape cultivars, including the highly recalcitrant Cabernet Sauvignon.

Chen Cecilia Liu, Iryna Gurevych, Anna Korhonen

The surge of interest in "culture" in NLP has inspired much recent research, but a shared understanding of "culture" remains unclear, making it difficult to evaluate progress in this emerging area. Drawing on prior research in NLP and related fields, we propose a fine-grained taxonomy of elements in culture that can provide a systematic framework for analyzing and understanding research progress. Using the taxonomy, we survey existing resources and methods for culturally aware and adapted NLP, providing an overview of the state of the art and the research gaps that still need to be filled.

Baohua Yang, Runchao Chen, Zhiwei Gao et al.

Wheat is one of the important food crops in the world, and the stability and growth of wheat production have a decisive impact on global food security and economic prosperity. Wheat counting is of great significance for agricultural management, yield prediction and resource allocation. Research shows that the wheat ear counting method based on deep learning has achieved remarkable results and the model accuracy is high. However, the complex background of wheat fields, dense wheat ears, small wheat ear targets, and different sizes of wheat ears make the accurate positioning and counting of wheat ears still face great challenges. To this end, an automatic positioning and counting method of wheat ears based on FIDMT-GhostNet (focal inverse distance transform maps - GhostNet) is proposed. Firstly, a lightweight wheat ear counting network using GhostNet as the feature extraction network is proposed, aiming to obtain multi-scale wheat ear features. Secondly, in view of the difficulty in counting caused by dense wheat ears, the point annotation-based network FIDMT (focal inverse distance transform maps) is introduced as a baseline network to improve counting accuracy. Furthermore, to address the problem of less feature information caused by the small ear of wheat target, a dense upsampling convolution module is introduced to improve the resolution of the image and extract more detailed information. Finally, to overcome background noise or wheat ear interference, a local maximum value detection strategy is designed to realize automatic processing of wheat ear counting. To verify the effectiveness of the FIDMT-GhostNet model, the constructed wheat image data sets including WEC, WEDD and GWHD were used for training and testing. Experimental results show that the accuracy of the wheat ear counting model reaches 0.9145, and the model parameters reach 8.42M, indicating that the model FIDMT-GhostNet proposed in this study has good performance.

Laurence V. Madden, Peter S. Ojiambo

Modern data analysis typically involves the fitting of a statistical model to data, which includes estimating the model parameters and their precision (standard errors) and testing hypotheses based on the parameter estimates. Linear mixed models (LMMs) fitted through likelihood methods have been the foundation for data analysis for well over a quarter of a century. These models allow the researcher to simultaneously consider fixed (e.g., treatment) and random (e.g., block and location) effects on the response variables and account for the correlation of observations, when it is assumed that the response variable has a normal distribution. Analysis of variance (ANOVA), which was developed about a century ago, can be considered a special case of the use of an LMM. A wide diversity of experimental and treatment designs, as well as correlations of the response variable, can be handled using these types of models. Many response variables are not normally distributed, of course, such as discrete variables that may or may not be expressed as a percentage (e.g., counts of insects or diseased plants) and continuous variables with asymmetrical distributions (e.g., survival time). As expansions of LMMs, generalized linear mixed models (GLMMs) can be used to analyze the data arising from several non-normal statistical distributions, including the discrete binomial, Poisson, and negative binomial, as well as the continuous gamma and beta. A GLMM allows the data analyst to better match the model to the data rather than to force the data to match a specific model. The increase in computer memory and processing speed, together with the development of user-friendly software and the progress in statistical theory and methodology, has made it practical for non-statisticians to use GLMMs since the late 2000s. The switch from LMMs to GLMMs is deceptive, however, as there are several major issues that must be thought about or judged when using a GLMM, which are mostly resolved for routine analyses with LMMs. These include the consideration of conditional versus marginal distributions and means, overdispersion (for discrete data), the model-fitting method [e.g., maximum likelihood (integral approximation), restricted pseudo-likelihood, and quasi-likelihood], and the choice of link function to relate the mean to the fixed and random effects. The issues are explained conceptually with different model formulations and subsequently with an example involving the percentage of diseased plants in a field study with wheat, as well as with simulated data, starting with a LMM and transitioning to a GLMM. A brief synopsis of the published GLMM-based analyses in the plant agricultural literature is presented to give readers a sense of the range of applications of this approach to data analysis.

Kewei Hu, Ying Wei, Yaoqiang Pan et al.

Accurate reconstruction of plant phenotypes plays a key role in optimising sustainable farming practices in the field of Precision Agriculture (PA). Currently, optical sensor-based approaches dominate the field, but the need for high-fidelity 3D reconstruction of crops and plants in unstructured agricultural environments remains challenging. Recently, a promising development has emerged in the form of Neural Radiance Field (NeRF), a novel method that utilises neural density fields. This technique has shown impressive performance in various novel vision synthesis tasks, but has remained relatively unexplored in the agricultural context. In our study, we focus on two fundamental tasks within plant phenotyping: (1) the synthesis of 2D novel-view images and (2) the 3D reconstruction of crop and plant models. We explore the world of neural radiance fields, in particular two SOTA methods: Instant-NGP, which excels in generating high-quality images with impressive training and inference speed, and Instant-NSR, which improves the reconstructed geometry by incorporating the Signed Distance Function (SDF) during training. In particular, we present a novel plant phenotype dataset comprising real plant images from production environments. This dataset is a first-of-its-kind initiative aimed at comprehensively exploring the advantages and limitations of NeRF in agricultural contexts. Our experimental results show that NeRF demonstrates commendable performance in the synthesis of novel-view images and is able to achieve reconstruction results that are competitive with Reality Capture, a leading commercial software for 3D Multi-View Stereo (MVS)-based reconstruction. However, our study also highlights certain drawbacks of NeRF, including relatively slow training speeds, performance limitations in cases of insufficient sampling, and challenges in obtaining geometry quality in complex setups.

Ivan Nekrasov, Aleksandr Zagulyaev, Vladimir Bukhtoyarov et al.

The article considers the issue of increasing the structural reliability of a biofuel production plant. A review of the existing basic technological schemes of the biofuel production plant has been carried out. The main structural elements are determined and a functional diagram is constructed. Processed cooking oil was chosen as the input raw material. A structural analysis of the reliability of each element and the entire system as a whole was carried out. The least reliable elements are determined, options for improving the overall reliability of the installation are proposed.

Kathryn Dumschott, Hannah Dörpholz, Marie-Angélique Laporte et al.

The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human and machine interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.

Diederik Aerts, Jonito Aerts Arguëlles, Lester Beltran et al.

Inspired by foundational studies in classical and quantum physics, and by information retrieval studies in quantum information theory, we prove that the notions of 'energy' and 'entropy' can be consistently introduced in human language and, more generally, in human culture. More explicitly, if energy is attributed to words according to their frequency of appearance in a text, then the ensuing energy levels are distributed non-classically, namely, they obey Bose-Einstein, rather than Maxwell-Boltzmann, statistics, as a consequence of the genuinely 'quantum indistinguishability' of the words that appear in the text. Secondly, the 'quantum entanglement' due to the way meaning is carried by a text reduces the (von Neumann) entropy of the words that appear in the text, a behaviour which cannot be explained within classical (thermodynamic or information) entropy. We claim here that this 'quantum-type behaviour is valid in general in human language', namely, any text is conceptually more concrete than the words composing it, which entails that the entropy of the overall text decreases. In addition, we provide examples taken from cognition, where quantization of energy appears in categorical perception, and from culture, where entities collaborate, thus 'entangle', to decrease overall entropy. We use these findings to propose the development of a new 'non-classical thermodynamic theory' for human cognition, which also covers broad parts of human culture and its artefacts and bridges concepts with quantum physics entities.

Samik Bhattacharya, Franziska Gröne, Felix Przesdzink et al.

Successful plant establishment in a particular environment depends on the root architecture of the seedlings and the extent of edaphic resource utilization. However, diverse habitats often pose a predicament on the suitability of the fundamental root structure of a species that evolved over a long period. We hypothesized that the plasticity in the genetically controlled root architecture in variable habitats provides an adaptive advantage to worldwide-distributed wild radish (Raphanus raphanistrum, Rr) over its close relative (R. pugioniformis, Rp) that remained endemic to the East Mediterranean region. To test the hypothesis, we performed a reciprocal comparative analysis between the two species, growing in a common garden experiment on their native soils (Hamra/Sandy for Rr, Terra Rossa for Rp) and complementary controlled experiments mimicking the major soil compositions. Additionally, we analyzed the root growth kinetics via semi-automated digital profiling and compared the architecture between Rr and Rp. In both experiments, the primary roots of Rr were significantly longer, developed fewer lateral roots, and showed slower growth kinetics than Rp. Multivariate analyses of seven significant root architecture variables revealed that Rr could successfully adapt to different surrogate growth conditions by only modulating their main root length and number of lateral roots. In contrast, Rp needs to modify several other root parameters, which are very resource-intensive, to grow on non-native soil. Altogether the findings suggest an evo-devo adaptive advantage for Rr as it can potentially establish in various habitats with the minimal tweak of key root parameters, hence allocating resources for other developmental requirements.

Aneta Gerszberg, K. Hnatuszko-Konka, Tomasz Kowalczyk et al.

Originating in the Andes, the tomato (Solanum lycopersicum L.) was imported to Europe in the 16th century. At present, it is an important crop plant cultivated all over the world, and its production and consumption continue to increase. This popular vegetable is known as a major source of important nutrients including lycopene, β-carotene, flavonoids and vitamin C as well as hydroxycinnamic acid derivatives. Since the discovery that lycopene has anti-oxidative, anti-cancer properties, interest in tomatoes has grown rapidly. The development of genetic engineering tools and plant biotechnology has opened great opportunities for engineering tomato plants. This review presents examples of successful tissue culture and genetically modified tomatoes which resistance to a range of environmental stresses improved, along with fruit quality. Additionally, a successful molecular farming model was established.

Stefano Cassano, Fabrizio Sossan

A hybrid hydropower power plant is a conventional HydroPower Plant (HPP) augmented with a Battery Energy Storage System (BESS) to decrease the wear and tear of sensitive mechanical components and improve the reliability and regulation performance of the overall plant. A central task of controlling hybrid power plants is determining how the total power set-point should be split between the BESS and the hybridized unit (power set-point splitting) as a function of the operational objectives. This paper describes a Model Predictive Control (MPC) framework for hybrid medium- and high-head plants to determine the power set-point of the hydropower unit and the BESS. The splitting policy relies on an explicit formulation of the mechanical loads incurred by the HPP's penstock, which can be damaged due to fatigue when providing regulation services to the grid. By filtering out from the HPP's power set-point the components conducive to excess penstock fatigue and properly controlling the BESS, the proposed MPC is able to maintain the same level of regulation performance while significantly decreasing damages to the hydraulic conduits. A proof-of-concept by simulations is provided considering a 230 MW medium-head hydropower plant.

Simon Julien, Amirhossein Sajadi, Bri-Mathias Hodge

Renewable energy technologies including solar and wind inevitably play a leading role in meeting the growing demand for a decarbonized and clean power grid. However, these technologies are highly dependent of meteorological conditions of power plant site and the challenge remains on how to cope with their short-term and momentarily variability. This paper presents a hierarchical control system to provide ancillary services from a solar PV power plant to the grid without the need for additional non-solar resources. With coordinated management of each inverter in the system, the control system commands the power plant to proactively curtail a fraction of its instantaneous maximum power potential, which gives the plant enough headroom to ramp up or down power production from the overall power plant, for a service such as regulation reserve, even under changing cloud cover conditions. A case study from a site in Hawaii with one-second resolution solar irradiance data is used to verify the efficacy of the proposed control system. The algorithm is subsequently compared with an alternative control technology from the literature, the grouping control algorithm; the results show that the proposed hierarchical control system is over 10 times more effective in reducing generator mileage to support power fluctuations from solar PV power plants.