Hasil untuk "Plant culture"

Nurul Labib Sayeedi, Md. Faiyaz Abdullah Sayeedi, Shubhashis Roy Dipta et al.

Bangla culture is richly expressed through region, dialect, history, food, politics, media, and everyday visual life, yet it remains underrepresented in multimodal evaluation. To address this gap, we introduce BanglaVerse, a culturally grounded benchmark for evaluating multilingual vision-language models (VLMs) on Bengali culture across historically linked languages and regional dialects. Built from 1,152 manually curated images across nine domains, the benchmark supports visual question answering and captioning, and is expanded into four languages and five Bangla dialects, yielding ~32.3K artifacts. Our experiments show that evaluating only standard Bangla overestimates true model capability: performance drops under dialectal variation, especially for caption generation, while historically linked languages such as Hindi and Urdu retain some cultural meaning but remain weaker for structured reasoning. Across domains, the main bottleneck is missing cultural knowledge rather than visual grounding alone, with knowledge-intensive categories. These findings position BanglaVerse as a more realistic test bed for measuring culturally grounded multimodal understanding under linguistic variation.

Solomon Peter Wante, Kar Mun Chooi, Ranjith Pathirana et al.

IntroductionGrapevine leafroll-associated virus 3 (GLRaV-3) poses a significant threat to viticulture and is the major virus pathogen in New Zealand. The presence of the virus is therefore undesirable within the New Zealand Winegrowers National Vine Collection, which serves as a repository of diverse and valuable grapevine genotypes.MethodsThis study evaluated the effectiveness of in vitro virus eradication protocols, specifically thermotherapy combined with chemotherapy (ribavirin or oseltamivir) or cryotherapy, for eliminating GLRaV-3 from infected grapevine cultivars. Virus presence was initially confirmed using enzyme-linked immunosorbent assay (ELISA) and high-throughput sequencing (HTS). Following treatment, plantlets were screened using reverse transcription quantitative polymerase chain reaction (RT-qPCR) for GLRaV-3 detection.ResultsThermotherapy followed by cryotherapy achieved the highest virus elimination rates across cultivars, including complete eradication in Sauvignon Blanc 217, Chenin Blanc, and Riesling Gm 239. Oseltamivir chemotherapy combined with thermotherapy showed higher elimination rates than ribavirin-based treatments, with complete success in Ehrenfelser and Golden Chasselas.DiscussionCultivar-specific responses emphasise the need to optimise treatment protocols to achieve broad efficacy across diverse cultivars. After virus elimination, tissue-cultured material could be maintained in vitro or by cryopreservation for long-term conservation. These findings provide a scalable strategy for restoring high-health status to grapevine cultivars within germplasm repositories, thereby supporting the long-term sustainability of viticulture.

J.I. Mnyango, B. Hlangothi, B. Nyoni et al.

Natural rubber (NR) is a high-performance elastomer valued for its elasticity, tensile strength, and tear resistance, making it indispensable in diverse industrial applications, including automotive tires, vibration-damping systems, medical devices, and consumer goods. However, conventional synthetic additives used in NR compounding pose environmental and health concerns due to their persistence, toxicity, and non-renewable origin. This review critically examines recent developments in natural-based additives—sourced from plants (e.g., lignin and vegetable oils), animals (e.g., collagen and chitosan), and minerals (e.g., silica and clay)—and their integration into NR compounding and processing. The discussion highlights how these bio-derived alternatives influence rheological, mechanical, thermal, and chemical properties of NR compounds, with comparative insights against conventional formulations. Challenges related to additive availability, processing behavior, cost, and suitability for high-performance applications are highlighted, alongside their potential to reduce reliance on petroleum-based materials. Finally, emerging directions such as smart/nano-enabled additives and AI/ML-driven formulation optimization are considered, underscoring the potential for natural-based additives to advance environmentally sustainable practices while meeting evolving industrial demands.

Pengju Xu, Yan Wang, Shuyuan Zhang et al.

Recent progress in Multimodal Large Language Models (MLLMs) have significantly enhanced the ability of artificial intelligence systems to understand and generate multimodal content. However, these models often exhibit limited effectiveness when applied to non-Western cultural contexts, which raises concerns about their wider applicability. To address this limitation, we propose the Traditional Chinese Culture understanding Benchmark (TCC-Bench), a bilingual (i.e., Chinese and English) Visual Question Answering (VQA) benchmark specifically designed for assessing the understanding of traditional Chinese culture by MLLMs. TCC-Bench comprises culturally rich and visually diverse data, incorporating images from museum artifacts, everyday life scenes, comics, and other culturally significant contexts. We adopt a semi-automated pipeline that utilizes GPT-4o in text-only mode to generate candidate questions, followed by human curation to ensure data quality and avoid potential data leakage. The benchmark also avoids language bias by preventing direct disclosure of cultural concepts within question texts. Experimental evaluations across a wide range of MLLMs demonstrate that current models still face significant challenges when reasoning about culturally grounded visual content. The results highlight the need for further research in developing culturally inclusive and context-aware multimodal systems. The code and data can be found at: https://tcc-bench.github.io/.

Sai Nath Chowdary Medikonduru, Hongpeng Jin, Yanzhao Wu

Plant diseases pose a significant threat to global agriculture, causing over $220 billion in annual economic losses and jeopardizing food security. The timely and accurate detection of these diseases from plant leaf images is critical to mitigating their adverse effects. Deep neural network Ensembles (Deep Ensembles) have emerged as a powerful approach to enhancing prediction accuracy by leveraging the strengths of diverse Deep Neural Networks (DNNs). However, selecting high-performing ensemble member models is challenging due to the inherent difficulty in measuring ensemble diversity. In this paper, we introduce the Synergistic Diversity (SQ) framework to enhance plant disease detection accuracy. First, we conduct a comprehensive analysis of the limitations of existing ensemble diversity metrics (denoted as Q metrics), which often fail to identify optimal ensemble teams. Second, we present the SQ metric, a novel measure that captures the synergy between ensemble members and consistently aligns with ensemble accuracy. Third, we validate our SQ approach through extensive experiments on a plant leaf image dataset, which demonstrates that our SQ metric substantially improves ensemble selection and enhances detection accuracy. Our findings pave the way for a more reliable and efficient image-based plant disease detection.

Ji-Eun Han, Yoonseok Heo

Incorporating personas into conversational AI models is crucial for achieving authentic and engaging interactions. However, the cultural diversity and adaptability of existing persona datasets is often overlooked, reducing their efficacy in building culturally aware AI systems. To address this issue, we propose a two-step pipeline for generating culture-specific personas and introduce KoPersona, a dataset comprising 200,000 personas designed to capture Korean cultural values, behaviors, and social nuances. A comprehensive evaluation through various metrics validates the quality of KoPersona and its relevance to Korean culture. This work not only contributes to persona-based research, but also establishes a scalable approach for creating culturally relevant personas adaptable to various languages and cultural contexts.

Xiutian Zhao, Rochelle Choenni, Rohit Saxena et al.

Despite their impressive performance, vision-language models (VLMs) still struggle on culturally situated inputs. To understand how VLMs process culturally grounded information, we study the presence of culture-sensitive neurons, i.e. neurons whose activations show preferential sensitivity to inputs associated with particular cultural contexts. We examine whether such neurons are important for culturally diverse visual question answering and where they are located. Using the CVQA benchmark, we identify neurons of culture selectivity and perform causal tests by deactivating the neurons flagged by different identification methods. Experiments on three VLMs across 25 cultural groups demonstrate the existence of neurons whose ablation disproportionately harms performance on questions about the corresponding cultures, while having minimal effects on others. Moreover, we propose a new margin-based selector - Contrastive Activation Selection (CAS), and show that it outperforms existing probability- and entropy-based methods in identifying culture-sensitive neurons. Finally, our layer-wise analyses reveals that such neurons tend to cluster in certain decoder layers. Overall, our findings shed new light on the internal organization of multimodal representations.

Emily Quiroga

The importance of maritime heritage in providing benefits such as a sense of place and identity has been widely discussed. However, there remains a lack of comprehensive quantitative analysis, particularly regarding monetary valuation and its impact on people's preferences. In this study, I present the results of a choice experiment that assesses the value of the maritime cultural heritage associated with shrimp fishing through seafood consumption preferences in Germany. Additionally, I investigate people's attitudes toward cultural heritage and examine how these attitudes affect their stated preferences. I find that these attitudes are significantly stronger in towns where local fishermen led a prominent awareness campaign on fishing culture during the study period. Moreover, I observe a positive willingness to pay for a cultural heritage attribute in shrimp dishes, which varies depending on individuals' attitudes toward cultural heritage.

Fatih Merdan, Ozgur B. Akan

Molecular communication (MC) studies biological signals that are found in nature. Most MC literature focuses on particle properties, even though many natural phenomena exhibit wave-like behavior. One such signal is sound waves. Understanding how sound waves are used in nature can help us better utilize this signal in our interactions with our environment. To take a step in this direction, in this paper, we examine how plants process incoming sound waves and take informed actions. Indeed, plants respond to sound, yet no quantitative communication-theoretic model currently explains this behavior. This study develops the first end-to-end acoustic communication framework for plants. The model is formed following the biological steps of the incoming signal, and a mathematical description is constructed at each step following basic biological models. The resulting end-to-end communication-theoretic model is analyzed using MATLAB. Simulations show that a $200$ $Hz$, $20$ $mu Pa$ stimulus elevates cytosolic $Ca^{2+}$ from $150$ $nM$ to $230 \pm 10$ $nM$ within $50$ seconds which can cause root bending in plants in the long run. This work establishes quantitative phytoacoustics, enabling bio-inspired acoustic connections for precision agriculture and plant signaling research.

Ivona Veličković, Stevan Samardžić, Marina T. Milenković et al.

Reducing agricultural waste through reuse has become one of the most important strategies to minimise impact on the environment—an emerging global issue. Sunflower ranks fourth in the world in the production of vegetable oilseeds and therefore generates large amounts of agricultural waste. The aim of this study was to investigate the phytochemical composition and bioactivity of sunflower crop residues in order to open up new opportunities for waste management. TPC and TFC were determined spectrophotometrically, while the dominant compounds were identified by LC-DAD-ESI-MS as <i>ent</i>-kaur-16-en-19-oic acid (KA) and 6Ac-7OH-dimethylchromone (DMC). Both compounds were present in higher concentrations in the ethyl acetate fraction (245.5 and 16.8 mg/g, respectively) than in the ethanol extract. None of the tested samples showed antimicrobial effects in the microdilution test. DMC showed remarkable antioxidant activity by DPPH, ABTS, FRAP and TRC in vitro assays, while both compounds proved to be promising enzyme inhibitory agents, being particularly efficient in inhibiting anti-neurodegenerative enzymes (IC₅₀ values of DMC and KA were 1.20/1.37 mg/mL and 1.44/1.63 mg/mL for AChE/BChE, respectively) and tyrosinase. The results presented indicate that sunflower crop residues are a good candidate for the extraction of bioactive compounds with potential application in the food, pharmaceutical and cosmetic industries.

Ana Sredojevic, Dragan Radivojevic, Steva M. Levic et al.

Apples are the most widely consumed temperate fruit worldwide and are often stored for long-term to ensure year-round availability. However, maintaining fruit quality during storage and subsequent shelf-life remain a significant postharvest challenge. This study investigated the combined effects of the harvest stage, cold storage duration, and shelf-life on the physico-chemical properties of Granny Smith apples. Key quality attributes including texture, maturity indices, color, and starch degradation were evaluated using instrumental methods and Raman microscopy. Fruit quality was affected differently by individual factors and their interactions. Texture parameters showed varied sensitivity: the harvest stage affected several parameters, storage duration had the strongest overall impact, shelf-life influenced a moderate number of parameters, and some were affected by combined factor interactions. Maturity indices were significantly influenced by all factors individually and combined. Color parameters were consistently affected by harvest stage and storage, with shelf-life and interactions influencing fewer parameters. These findings emphasize the complex interplay of factors shaping apple quality after harvest. The study demonstrates the importance of timing harvest and tailoring postharvest handling to maintain apple quality. It also demonstrates the potential of combining traditional and advanced techniques for effective ripeness monitoring.

José Edson Fontes Figueiredo, Gisele de Fátima Dias Diniz, Mikaely Sousa Marins et al.

IntroductionBacillus velezensis is a ubiquitous bacterium with potent antifungal activity and a plant growth promoter. This study investigated the potential of B. velezensis CNPMS-22 as a biocontrol agent against phytopathogenic fungi under diverse experimental conditions and its potential as a plant growth promoter. Genome sequencing and analysis revealed putative genes involved in these traits.MethodsThis research performed in vitro experiments to evaluate the CNPMS-22 antagonistic activity against 10 phytopathogenic fungi using dual culture in plate (DCP) and inverted sealed plate assay (ISP). Greenhouse and field tests evaluated the ability of CNPMS-22 to control Fusarium verticillioides in maize plants in vivo. The CNPMS-22 genome was sequenced using the Illumina HiSeq 4,000 platform, and genomic analysis also included manual procedures to identify genes of interest accurately.ResultsCNPMS-22 showed antifungal activity in vitro against all fungi tested, with notable reductions in mycelial growth in both DCP and ISP experiments. In the ISP, volatile organic compounds (VOCs) produced by CNPMS-22 also altered the mycelium coloration of some fungi. Scanning electron microscopy revealed morphological alterations in the hyphae of F. verticillioides in contact with CNPMS-22, including twisted, wrinkled, and ruptured hyphae. Eight cluster candidates for synthesizing non-ribosomal lipopeptides and ribosomal genes for extracellular lytic enzymes, biofilm, VOCs, and other secondary metabolites with antifungal activity and plant growth promoters were identified by genomic analysis. The greenhouse and field experiments showed that seed treatment with CNPMS-22 reduced Fusarium symptoms in plants and increased maize productivity.ConclusionOur findings highlight the CNPMS-22’s potential as bioinoculant for fungal disease control and plant growth with valuable implications for a sustainable crop productivity.

K. Handberg, J. Stougaard

Ibrahim Said Ahmad, Shiran Dudy, Resmi Ramachandranpillai et al.

Large Language Models (LLMs), such as ChatGPT, are widely used to generate content for various purposes and audiences. However, these models may not reflect the cultural and emotional diversity of their users, especially for low-resource languages. In this paper, we investigate how ChatGPT represents Hausa's culture and emotions. We compare responses generated by ChatGPT with those provided by native Hausa speakers on 37 culturally relevant questions. We conducted experiments using emotion analysis and applied two similarity metrics to measure the alignment between human and ChatGPT responses. We also collected human participants ratings and feedback on ChatGPT responses. Our results show that ChatGPT has some level of similarity to human responses, but also exhibits some gaps and biases in its knowledge and awareness of the Hausa culture and emotions. We discuss the implications and limitations of our methodology and analysis and suggest ways to improve the performance and evaluation of LLMs for low-resource languages.

Mohamed Debbagh, Shangpeng Sun, Mark Lefsrud

Accurate predictions and representations of plant growth patterns in simulated and controlled environments are important for addressing various challenges in plant phenomics research. This review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions. We provide a comprehensive examination of deterministic, probabilistic, and generative modeling approaches, emphasizing their applications in high-throughput phenotyping and simulation-based plant growth forecasting. Key topics include regressions and neural network-based representation models for the task of forecasting, limitations of existing experiment-based deterministic approaches, and the need for dynamic frameworks that incorporate uncertainty and evolving environmental feedback. This review surveys advances in 2D and 3D structured data representations through functional-structural plant models and conditional generative models. We offer a perspective on opportunities for future works, emphasizing the integration of domain-specific knowledge to data-driven methods, improvements to available datasets, and the implementation of these techniques toward real-world applications.

Xiaolin Xing, Xiaolin Xing, Tianhua Hu et al.

Radish (Raphanus sativus L.) is a vegetable crop with economic value and ecological significance in the genus Radish, family Brassicaceae. In recent years, developed countries have attached great importance to the collection and conservation of radish germplasm resources and their research and utilization, but the lack of population genetic information and molecular markers has hindered the development of the genetic breeding of radish. In this study, we integrated the radish genomic data published in databases for the development of single-nucleotide polymorphism (SNP) markers, and obtained a dataset of 308 high-quality SNPs under strict selection criteria. With the support of Kompetitive Allele-Specific PCR (KASP) technology, we screened a set of 32 candidate core SNP marker sets to analyse the genetic diversity of the collected 356 radish varieties. The results showed that the mean values of polymorphism information content (PIC), minor allele frequency (MAF), gene diversity and heterozygosity of the 32 candidate core SNP markers were 0.32, 0.30, 0.40 and 0.25, respectively. Population structural analysis, principal component analysis and genetic evolutionary tree analysis indicated that the 356 radish materials were best classified into two taxa, and that the two taxa of the material were closely genetically exchanged. Finally, on the basis of 32 candidate core SNP markers we calculated 15 core markers using a computer algorithm to construct a fingerprint map of 356 radish varieties. Furthermore, we constructed a core germplasm population consisting of 71 radish materials using 32 candidate core markers. In this study, we developed SNP markers for radish cultivar identification and genetic diversity analysis, and constructed DNA fingerprints, providing a basis for the identification of radish germplasm resources and molecular marker-assisted breeding as well as genetic research.

Masoumeh Panahi, Rasool Rezaei, Habiballah Charehgani

Meloidogyne javanica and Ralstonia solanacearum are the highly specialized soil-born plant parasites with economic importance causing root-knot and bacterial wilt diseases in tomatoes, respectively. The occurrence and intensity of the bacterial wilt escalated in the presence of root-knot nematodes and R. solanacearum concurrently detected in different vegetable crops. Sampling and preparation of leaf extract were done to investigate the activity of catalase (CAT), superoxide dismutase (SOD), and peroxidase (POX) enzymes at 24, 48, 72, and 120 hours post-inoculation (hpi) of tomato plants with R. solanacearum and M. javanica. The enzyme activity was measured at each time interval. The CAT and SOD enzymes exhibited maximum activity levels at 120 and 48 hpi in the nematode treatment, respectively. Meanwhile, the levels of POX enzyme peaked at 48 and 72 hpi in the nematode and nematode-bacterium treatments, respectively. Pathogen stress eventually led to a decrease in the SOD and POX enzymes 120 hours after inoculation and a significant increase in CAT during nematode-bacterium treatment. The results revealed apparent enzyme activity variations in tomato plants infected with both pathogens at different time intervals after inoculation.

Songwei Wu, Guozhen Gao, Yuxia Du et al.

Abstract Fruit acidity significantly influences fruit flavor, but the specific impact of soil pH on fruit acidity remains unclear. This study investigated the effects of various soil pH levels on fruit acidity and citric acid (CA) metabolism in lemon (Citrus limon L.). High soil pH (pH 8) decreased total soluble solids concentrations in lemon fruits, while low soil pH (pH 4) increased titratable acid and CA concentrations. Although low soil pH reduced the synthesis of CA due to the decreased citrate synthase and phosphoenolpyruvate carboxylase activities, the elevated fruit acidity under low soil pH conditions is not directly related to CA synthesis. Instead, low soil pH was found to suppress the activity of cytosolic aconitase (Cyt-ACO), an iron-dependent enzyme, indicating a potential role for CA degradation inhibition in low soil pH-induced CA accumulation. Furthermore, low soil pH significantly reduced cytosolic iron (Cyt-Fe) concentration, which was positively correlated with Cyt-ACO activity. In conclusion, low soil pH contributes to increasing fruit acidity in lemon, partially by inhibiting CA degradation due to the reduced Cyt-Fe concentrations. Our work unravels the influence of soil pH on CA accumulation and provides important clues for modulating CA levels through microelement fertilization in citrus.

H. Mason, J. Ball, Jianjian Shi et al.

I. Pickering, R. Prince, Martin J George et al.