Hasil untuk "Plant culture"

Xia Cheng, Yan Luo, Minghua Dong et al.

Industrial hemp has an excellent tolerance for lead (Pb) and accumulation capacity. Further improving the Pb tolerance in industrial hemp is of great interest for its future application in phytoremediation. Present study was performed to evaluate the various Pb contaminated soils (normal soil; Pb spiked soil using Pb(NO3)2 and Pb polluted mine soil) with the Pb level 1300 mg kg−1 and various treatments of the vermicompost (VC) and cerium oxide nanoparticles (CeO2 NPs), (T0 = no VC and CeO2-NPs; T1 = CeO₂ NPs (30 mg L−1); T2 = VC (5 % w/w of soil); and T3 = T1 + T2 on the Pb accumulation and hemp productivity. The findings indicated that Pb stress (artificially spiked and natural contamination) led to significant reduction in the growth, biomass, and physiological traits of hemp. The Pb polluted mine soil exhibits more harmful impacts in comparison to artificially Pb spiked soil. The sole application of CeO2-NPs leads to less pronounced enhancements in growth and development parameters at rapid growth and harvesting stage in comparison with soil applied VC treatment under the treated and untreated Pb-stressed plants. Combined application of CeO2-NPs and VC effectively reduced the malondialdehyde contents (41.34 %), increased the soluble protein (62.35 %) and soluble sugar (29.97 %) as compared to control group. Moreover, the co-active effect of VC and CeO2-NPs also had the prospects of reducing Pb accumulation in difference tissues by enhancing physiological resiliency in hemp. Particularly, combined use of VC and CeO2-NPs counteracted the adverse effect of Pb stress by boosting growth, biomass, enzymatic antioxidants, and osmoprotectants through limiting the Pb accumulation. Use of organic amendments (VC) and metallic oxide NPs (CeO2-NPs) holds promising tool for mitigating the Pb stress, offering a practical and viable approach for hemp production.

Fanglan Wu, Yunxiao Zhao, Yicun Chen et al.

Flavonoids are not only widely applied in the food, pharmaceutical and cosmetic industries, but also possess diverse biological functions that play crucial roles in plant physiology, growth and development, and ecology. The camphor tree (Cinnamomum camphora (L.)) holds significant economic and ecological value; however, its flavonoid composition and the underlying biosynthetic mechanisms remain largely unexplored. In this study, widely targeted metabolomics and transcriptomics were integrated to comprehensively analyze the leaves of C. camphora. The results revealed significant metabolic differences among fresh leaves of different C. camphora chemotypes. with both DAMs and DEGs being significantly enriched in the flavonoid biosynthesis pathway. Quercetin-5-O-glucuronide, rhamnetin-3-O-glucoside, juglanin, catechin and (-)-epicatechin were identified as the major DAMs. Furthermore, the key enzymes involved in the flavonoid biosynthetic pathway, such as PAL, C4H, and 4CL, CHS, CHI, F3H, FLS, DFR, ANS, LAR, C3H, and HCT were identified. Among these, CcC4H1 was identified as a hub gene in co-expression network. Compared with the control, transient overexpression of CcC4H1 significantly increased the total flavonoid content in C. camphora leaves by 1.45-fold (reaching 2.42 mg/g), while significantly elevating the flavonoids compounds such as 8-C-methylquercetin 3-xyloside, 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-3-[(2S,3S,4 R,5S,6 R)-2,3,5-trihydroxy-6-methyloxan-4-yl]oxychromen-4-one, and rhamnetin-3-O-glucoside, with increases ranging from 1.88- to 22.79-fold. These findings provide important insights into the molecular regulation mechanisms of flavonoid biosynthesis in camphor trees and provide important information for the selection of varieties rich in flavonoids.

Herve Goeau, Pierre Bonnet, Alexis Joly

The LifeCLEF plant identification challenge aims at evaluating plant identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2015 evaluation was actually conducted on a set of more than 100K images illustrating 1000 plant species living in West Europe. The main originality of this dataset is that it was built through a large-scale participatory sensing plateform initiated in 2011 and which now involves tens of thousands of contributors. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.

Pramit Sahoo, Maharaj Brahma, Maunendra Sankar Desarkar

Large language models (LLMs) are widely used in various tasks and applications. However, despite their wide capabilities, they are shown to lack cultural alignment \citep{ryan-etal-2024-unintended, alkhamissi-etal-2024-investigating} and produce biased generations \cite{naous-etal-2024-beer} due to a lack of cultural knowledge and competence. Evaluation of LLMs for cultural awareness and alignment is particularly challenging due to the lack of proper evaluation metrics and unavailability of culturally grounded datasets representing the vast complexity of cultures at the regional and sub-regional levels. Existing datasets for culture specific items (CSIs) focus primarily on concepts at the regional level and may contain false positives. To address this issue, we introduce a novel CSI dataset for Indian culture, belonging to 17 cultural facets. The dataset comprises ~8k cultural concepts from 36 sub-regions. To measure the cultural competence of LLMs on a cultural text adaptation task, we evaluate the adaptations using the CSIs created, LLM as Judge, and human evaluations from diverse socio-demographic region. Furthermore, we perform quantitative analysis demonstrating selective sub-regional coverage and surface-level adaptations across all considered LLMs. Our dataset is available here: https://huggingface.co/datasets/nlip/DIWALI, project webpage https://nlip-lab.github.io/nlip/publications/diwali/, and our codebase with model outputs can be found here: https://github.com/pramitsahoo/culture-evaluation

Chuancheng Shi, Shangze Li, Shiming Guo et al.

Multilingual text-to-image (T2I) models have advanced rapidly in terms of visual realism and semantic alignment, and are now widely utilized. Yet outputs vary across cultural contexts: because language carries cultural connotations, images synthesized from multilingual prompts should preserve cross-lingual cultural consistency. We conduct a comprehensive analysis showing that current T2I models often produce culturally neutral or English-biased results under multilingual prompts. Analyses of two representative models indicate that the issue stems not from missing cultural knowledge but from insufficient activation of culture-related representations. We propose a probing method that localizes culture-sensitive signals to a small set of neurons in a few fixed layers. Guided by this finding, we introduce two complementary alignment strategies: (1) inference-time cultural activation that amplifies the identified neurons without backbone fine-tuned; and (2) layer-targeted cultural enhancement that updates only culturally relevant layers. Experiments on our CultureBench demonstrate consistent improvements over strong baselines in cultural consistency while preserving fidelity and diversity.

Robin-Nico Kampa, Fabian Deuser, Konrad Habel et al.

Plant phenotyping involves analyzing observable characteristics of plants to better understand their growth, health, and development. In the context of deep learning, this analysis is often approached through single-view classification or regression models. However, these methods often fail to capture all information required for accurate estimation of target phenotypic traits, which can adversely affect plant health assessment and harvest readiness prediction. To address this, the Growth Modelling (GroMo) Grand Challenge at ACM Multimedia 2025 provides a multi-view dataset featuring multiple plants and two tasks: Plant Age Prediction and Leaf Count Estimation. Each plant is photographed from multiple heights and angles, leading to significant overlap and redundancy in the captured information. To learn view-invariant embeddings, we incorporate 24 views, referred to as the selection vector, in a random selection. Our ViewSparsifier approach won both tasks. For further improvement and as a direction for future research, we also experimented with randomized view selection across all five height levels (120 views total), referred to as selection matrices.

Mai AlKhamissi, Yunze Xiao, Badr AlKhamissi et al.

Cultural evaluation of large language models has become increasingly important, yet current benchmarks often reduce culture to static facts or homogeneous values. This view conflicts with anthropological accounts that emphasize culture as dynamic, historically situated, and enacted in practice. To analyze this gap, we introduce a four-part framework that categorizes how benchmarks frame culture, such as knowledge, preference, performance, or bias. Using this lens, we qualitatively examine 20 cultural benchmarks and identify six recurring methodological issues, including treating countries as cultures, overlooking within-culture diversity, and relying on oversimplified survey formats. Drawing on established anthropological methods, we propose concrete improvements: incorporating real-world narratives and scenarios, involving cultural communities in design and validation, and evaluating models in context rather than isolation. Our aim is to guide the development of cultural benchmarks that go beyond static recall tasks and more accurately capture the responses of the models to complex cultural situations.

Zane K J Hartley, Lewis A G Stuart, Andrew P French et al.

Recent years have seen substantial improvements in the ability to generate synthetic 3D objects using AI. However, generating complex 3D objects, such as plants, remains a considerable challenge. Current generative 3D models struggle with plant generation compared to general objects, limiting their usability in plant analysis tools, which require fine detail and accurate geometry. We introduce PlantDreamer, a novel approach to 3D synthetic plant generation, which can achieve greater levels of realism for complex plant geometry and textures than available text-to-3D models. To achieve this, our new generation pipeline leverages a depth ControlNet, fine-tuned Low-Rank Adaptation and an adaptable Gaussian culling algorithm, which directly improve textural realism and geometric integrity of generated 3D plant models. Additionally, PlantDreamer enables both purely synthetic plant generation, by leveraging L-System-generated meshes, and the enhancement of real-world plant point clouds by converting them into 3D Gaussian Splats. We evaluate our approach by comparing its outputs with state-of-the-art text-to-3D models, demonstrating that PlantDreamer outperforms existing methods in producing high-fidelity synthetic plants. Our results indicate that our approach not only advances synthetic plant generation, but also facilitates the upgrading of legacy point cloud datasets, making it a valuable tool for 3D phenotyping applications.

Caleb Ziems, William Held, Jane Yu et al.

To serve global users safely and productively, LLMs need culture-specific knowledge that might not be learned during pre-training. How do we find such knowledge that is (1) salient to in-group users, but (2) unknown to LLMs? The most common solutions are single-initiative: either researchers define challenging questions that users passively answer (traditional annotation), or users actively produce data that researchers structure as benchmarks (knowledge extraction). The process would benefit from mixed-initiative collaboration, where users guide the process to meaningfully reflect their cultures, and LLMs steer the process towards more challenging questions that meet the researcher's goals. We propose a mixed-initiative methodology called CultureCartography. Here, an LLM initializes annotation with questions for which it has low-confidence answers, making explicit both its prior knowledge and the gaps therein. This allows a human respondent to fill these gaps and steer the model towards salient topics through direct edits. We implement this methodology as a tool called CultureExplorer. Compared to a baseline where humans answer LLM-proposed questions, we find that CultureExplorer more effectively produces knowledge that leading models like DeepSeek R1 and GPT-4o are missing, even with web search. Fine-tuning on this data boosts the accuracy of Llama-3.1-8B by up to 19.2% on related culture benchmarks.

Guohua Bao, Xiao Xu, Jing Yang et al.

Leaf senescence is an important agronomic trait that significantly influences the quality and yield of soybeans. v-Myb avian myeloblastosis viral oncogene homolog (MYB) transcription factors are considered crucial regulators governing leaf senescence, which can be utilized to improve agronomic traits in crops. However, our knowledge regarding the functional roles of soybean MYBs in leaf senescence is extremely limited. In this study, GmMYBLJ, a CCA1-like MYB, was identified and functionally characterized with respect to leaf senescence. The GmMYBLJ protein is localized in the nucleus, and a high accumulation of its transcripts was observed in nodules and embryos. Notably, GmMYBLJ was highly expressed in soybean senescent leaves and was transcriptionally induced by dark or NaCl treatment, as confirmed by histochemical GUS staining analysis. Ectopic overexpression of GmMYBLJ in Arabidopsis not only led to earlier leaf senescence, reduced chlorophyll content, and increased MDA accumulation but also promoted the expression of several WRKY family transcription factors and senescence-associated genes, such as SAG12 and ORE1. Further investigation showed that overexpression of GmMYBLJ accelerated Arabidopsis leaf senescence under darkness and in response to Pst DC3000 infection. Moreover, transgenic soybean plants overexpressing GmMYBLJ grew faster and exhibited accelerated senescence under salt stress. DAB staining analysis showed that GmMYBLJ induced ROS accumulation in soybean hairy roots and Arabidopsis leaves. Collectively, our results provided useful information into the functional roles of GmMYBLJ in both age-dependent and stress-induced senescence.

Badr AlKhamissi, Muhammad ElNokrashy, Mai AlKhamissi et al.

The intricate relationship between language and culture has long been a subject of exploration within the realm of linguistic anthropology. Large Language Models (LLMs), promoted as repositories of collective human knowledge, raise a pivotal question: do these models genuinely encapsulate the diverse knowledge adopted by different cultures? Our study reveals that these models demonstrate greater cultural alignment along two dimensions -- firstly, when prompted with the dominant language of a specific culture, and secondly, when pretrained with a refined mixture of languages employed by that culture. We quantify cultural alignment by simulating sociological surveys, comparing model responses to those of actual survey participants as references. Specifically, we replicate a survey conducted in various regions of Egypt and the United States through prompting LLMs with different pretraining data mixtures in both Arabic and English with the personas of the real respondents and the survey questions. Further analysis reveals that misalignment becomes more pronounced for underrepresented personas and for culturally sensitive topics, such as those probing social values. Finally, we introduce Anthropological Prompting, a novel method leveraging anthropological reasoning to enhance cultural alignment. Our study emphasizes the necessity for a more balanced multilingual pretraining dataset to better represent the diversity of human experience and the plurality of different cultures with many implications on the topic of cross-lingual transfer.

Tianqi Wei, Zhi Chen, Xin Yu et al.

Plant diseases pose significant threats to agriculture. It necessitates proper diagnosis and effective treatment to safeguard crop yields. To automate the diagnosis process, image segmentation is usually adopted for precisely identifying diseased regions, thereby advancing precision agriculture. Developing robust image segmentation models for plant diseases demands high-quality annotations across numerous images. However, existing plant disease datasets typically lack segmentation labels and are often confined to controlled laboratory settings, which do not adequately reflect the complexity of natural environments. Motivated by this fact, we established PlantSeg, a large-scale segmentation dataset for plant diseases. PlantSeg distinguishes itself from existing datasets in three key aspects. (1) Annotation type: Unlike the majority of existing datasets that only contain class labels or bounding boxes, each image in PlantSeg includes detailed and high-quality segmentation masks, associated with plant types and disease names. (2) Image source: Unlike typical datasets that contain images from laboratory settings, PlantSeg primarily comprises in-the-wild plant disease images. This choice enhances the practical applicability, as the trained models can be applied for integrated disease management. (3) Scale: PlantSeg is extensive, featuring 11,400 images with disease segmentation masks and an additional 8,000 healthy plant images categorized by plant type. Extensive technical experiments validate the high quality of PlantSeg's annotations. This dataset not only allows researchers to evaluate their image classification methods but also provides a critical foundation for developing and benchmarking advanced plant disease segmentation algorithms.

Otacilio B. L. Neto, Michela Mulas, Francesco Corona

This work presents a general framework for the advanced control of a common class of activated sludge plants (ASPs). Based on a dynamic model of the process and plant sensors and actuators, we design and configure a highly customisable Output Model-Predictive Controller (Output MPC) for the flexible operation of ASPs as water resource recovery facilities. The controller consists of a i) Moving-Horizon Estimator for determining the state of the process, from plant measurements, and ii) a Model-Predictive Controller for determining the optimal actions to attain high-level operational goals. The Output MPC can be configured to satisfy the technological limits of the plant equipment, as well as operational desiderata defined by plant personnel. We consider exemplary problems and show that the framework is able to control ASPs for tasks of practical relevance, ranging from wastewater treatment subject to normative limits, to the production of an effluent with varying nitrogen content, and energy recovery.

Xianjun Yang, Junfeng Gao, Wenxin Xue et al.

Large Language Models (LLMs) have exhibited remarkable capabilities in understanding and interacting with natural language across various sectors. However, their effectiveness is limited in specialized areas requiring high accuracy, such as plant science, due to a lack of specific expertise in these fields. This paper introduces PLLaMa, an open-source language model that evolved from LLaMa-2. It's enhanced with a comprehensive database, comprising more than 1.5 million scholarly articles in plant science. This development significantly enriches PLLaMa with extensive knowledge and proficiency in plant and agricultural sciences. Our initial tests, involving specific datasets related to plants and agriculture, show that PLLaMa substantially improves its understanding of plant science-related topics. Moreover, we have formed an international panel of professionals, including plant scientists, agricultural engineers, and plant breeders. This team plays a crucial role in verifying the accuracy of PLLaMa's responses to various academic inquiries, ensuring its effective and reliable application in the field. To support further research and development, we have made the model's checkpoints and source codes accessible to the scientific community. These resources are available for download at \url{https://github.com/Xianjun-Yang/PLLaMa}.

Michael Neumann, Thorben Kuchel, Philipp Diebold et al.

Context: In agile transformations, there are many challenges such as alignment between agile practices and the organizational goals and strategies or issues with shifts in how work is organized and executed. One very important challenge but less considered and treated in research are cultural challenges associated with an agile mindset. Although research shows that cultural clashes and general organizational resistance to change are part of the most significant agile adoption barriers. Objective: We identify challenges that arise from the interplay between agile culture and organizational culture. In doing so, we tackle this field and come up with important contributions for further research regarding a problem that practitioners face today. Method: This is done with a mixed-method research approach. First, we gathered qualitative data among our network of agile practitioners and derived in sum 15 challenges with agile culture. Then, we conducted quantitative data by means of a questionnaire study with 92 participants. Results: We identified 7 key challenges out of the 15 challenges with agile culture. These key challenges refer to the technical agility (doing agile) and the cultural agility (being agile). The results are presented in type of a conceptual model named the Agile Cultural Challenges (ACuCa). Conclusion: Based on our results, we started deriving future work aspects to do more detailed research on the topic of cultural challenges while transitioning or using agile methods in software development and beyond.

Hao Wang

Human culture research has witnessed an opportunity of revolution thanks to the big data and social network revolution. Websites such as Douban.com, Goodreads.com, Pandora and IMDB become the new gold mine for cultural researchers. In 2021 and 2022, the author of this paper invented 2 data-free recommender systems for AI cold-start problem. The algorithms can recommend cultural and commercial products to users without reference to users' past preferences. The social implications of the new inventions are human cultural tastes can be predicted very precisely without any information related to human individuals. In this paper, we analyze the AI technologies and its cultural implications together with other AI algorithms. We show that human culture is (mostly) a history irrelevant and predictable experience.

George Hochmuth, Ed Hanlon, Russell Nagata et al.

This 8-page publication presents UF/IFAS recommendations for fertilization of crisphead lettuce on organic soils in Florida. Written by George Hochmuth, Ed Hanlon, Russell Nagata, George Snyder, Tom Schueneman, J. Mabry McCray, and German Sandoya, and published by the UF/IFAS Horticultural Sciences Department, revised January 2023. SP153/WQ114: Fertilization Recommendations for Crisphead Lettuce Grown on Organic Soils in Florida (ufl.edu)

Marco Santin, Michelangelo Becagli, Maria Calogera Sciampagna et al.

The application of organic amendments, biochar, and wood distillate (WD), as well as the exposure to UV-B radiation, are two sustainable ways to enhance soil fertility and increase plant nutraceutical quality, respectively. However, they have always been studied separately, without testing the eventual synergistic or antagonistic effect when applied together. The present study investigated the effects of biochar (2% <i>w</i>/<i>w</i>), WD (1:100), and their combination (BWD) on some biometric and biochemical parameters of basil plants (<i>Ocimum basilicum</i> L.) exposed to different doses of UV-B radiation (0, 1, 2 h d⁻¹; UV-B irradiance of 1.36 W m⁻²) in controlled conditions. Root and stem length and weight were not affected by soil amendments, while 1 h d⁻¹ UV-B increased the length (+28%) and weight (+62%) of the aerial part. When combining the above- and below-ground factors, a decrease in root length was observed in the 2 h d⁻¹ UV-B-treated plants in both WD (−36%) and BWD (−39%) treatments. The co-application of below- and above-ground treatments generally decreased phenolic and flavonoid concentration in both fully expanded leaves and vegetative shoot apices. This preliminary study highlights an antagonistic action of the combination of the investigated factors, at these doses, on the plant growth and metabolism that should be considered.

Gustavo Rodríguez-Yzquierdo, Barlin O. Olivares, Oscar Silva-Escobar et al.

<i>Fusarium oxysporum</i> f. sp. <i>cubense</i> Tropical Race 4 (Foc TR4) (Syn. <i>Fusarium odoratissimum</i>) is a devastating soil-borne pathogen that infects the roots of banana plants and causes Fusarium wilt disease. Colombia is one of the world’s leading banana producers; therefore, new uncontrolled outbreaks could have serious consequences. Despite this, little is known about the susceptibility of Musaceae lands in Colombia to Foc TR4. This work presents a pioneering study on the susceptibility of Colombian soils to Foc TR4. For this, a study was carried out to characterize climatic, edaphic, and density factors of Musaceae productive systems at the Colombian level, articulated with expert criteria to map and define areas with different levels of susceptibility to Foc R4T. These criteria are typically selected based on the existing scientific literature, consultation with domain experts, and consideration of established methods for assessing soil health and disease susceptibility in Musaceae plantations. By joining the analyzed susceptibility factors, differentiated areas were generated that imply a greater or lesser predisposition to the disease. Subsequently, a validation of the classification was made with Random Forest. The results indicate that at the level of climate, soil, and farm density as a fit factor, practically 50% of the cultivated territory of Musaceae are areas high and very highly susceptible to the pathogen (572,000 km²). The results showed that from the total Musaceae area, Antioquia, Bolívar, Chocó, and Santander turned out to be the departments with the highest proportion of very high susceptibility class of the production farms. The analysis of Random Forest classification performance shows that the model has a relatively low out-of-bag (OOB) error rate (0.023). The study on the susceptibility is highly novel and original, as it represents the first systematic investigation of Foc TR4 susceptibility in Colombian soils. This paper provides important insights into the susceptibility of Musaceae lands in Colombia to Foc TR4. The study highlights the need for ongoing monitoring, containment, and control measures to prevent the spread of this deadly pathogen and protect Colombia’s important banana industry.

Thorben Kuchel, Michael Neumann, Philipp Diebold et al.

Context: Within agile transformations, there are a lot of different challenges coming up. One very important but less considered and treated in research are cultural challenges. Although research shows that cultural clashes and general organizational resistance to change are part of the most significant agile adoption barriers. Objective: Thus, our objective is to tackle this field and come up with important contributions for further research. To this end, we want to identify challenges that arise from the interplay between agility and organizational culture. Method: This is done based on an iterative research approach. On the one hand, we gathered qualitative data among our network of agile practitioners. Then, we derived in sum 15 challenges with agile culture. On the other hand, we gathered quantitative data by means of a questionnaire study with 92 participants. Results: We identified 7 key challenges out of the 15 challenges with agile culture. The results that are presented in a conceptual model show a focus on human aspects that we need to deal with more in future. Conclusion: Based on our results, we started deriving future work aspects to do more detailed research on the topic of cultural challenges while transitioning or using agile methods in software development and beyond.