Hasil untuk "Plant culture"

Kundan Kumar, Armando G. McDonald, Vijay Singh et al.

Sugar mills processing sugar beet generate large amounts of sugar beet pulp (SBP), a carbohydrate-rich byproduct composed of 22–30 % cellulose, 24–32 % hemicellulose, and 15–20 % pectin. Mild, chemical-free hydrothermal pretreatment offers a promising approach to recover these compounds while minimizing pectin degradation, reducing chemical use, and lowering operating costs for value-added applications. This study evaluates the effects of chemical-free hydrothermal pretreatment on cellulosic sugars recovery and its synergy with subsequent pectin extraction under pretreatment conditions ranging from 80°C to 120°C for 15–45 min. Results show that increasing pretreatment severity preserved most glucans with 4 % decrease in galacturonan content. Optimal pretreatment (100°C for 45 min) followed by enzymatic hydrolysis achieved the best glucose (92 %) and pentose (74 %) yields, while galacturonan remained concentrated in the residual solids. Subsequent citric acid extraction at 80°C for 3 hr and a solid-to-liquid ratio of 1:15 yielded up to 85 % pectin, significantly lowering water, chemical, and energy requirements compared to conventional industrial operations. The integration of mild hydrothermal pretreatment with enzymatic hydrolysis thus maximized sugar recovery and enabled efficient downstream pectin extraction without compromising product yields. These findings advance low-impact valorization strategies to reinforce SBP’s values in biorefinery.

Ibtisam Alatawi, Haizheng Xiong, Beiquan Mou et al.

The Leafminers, representing a diverse group of insects from various genera within the Agromyzidae family, pose a significant threat to spinach (Spinacia oleracea L.) production. This study aimed to identify single nucleotide polymorphism (SNP) markers associated with leafminer resistance through a genome-wide association study (GWAS) and to evaluate the prediction accuracy (PA) for selecting resistant spinach using genomic prediction (GP). Using a dataset of 84 301 SNPs obtained from whole-genome resequencing, seven GWAS models, including BLINK, FarmCPU, MLM, and MLMM in GAPIT 3, as well as MLM, GLM, and SMR in TASSEL 5, were employed to perform GWAS on a panel of 286 USDA spinach germplasm accessions. Three SNP markers, namely 1_115279256_C_T, 3_157082529_C_T, and 4_168510908_T_G on chromosomes 1, 3, and 4, respectively, were identified as associated with leafminer resistance. In the 30 kb flanking regions of these markers, four candidate genes (SOV1g031330, SOV1g031340, SOV4g047270, and SOV4g047280), encoding LOB domain-containing protein, KH domain-containing protein, were discovered. Nodulin-like domain-containing protein, and SAM domain-containing protein, were discovered. The PA for leafminer resistance selection was estimated using ten different SNP sets, including two GWAS-derived marker sets (three and 51 SNPs) and eight random marker sets (ranging from 51 to 10 K SNPs) analyzed by seven GP models. The findings emphasized the superior performance of GWAS-derived SNP sets, reaching a PA of up to 0.79 using the cBLUP model. Notably, this research marks the pioneering application of GP in the context of insect resistance, providing a significant advancement in the understanding and management of leafminer resistance in spinach cultivation.

Qian Tan, Lei Jiang, Yuting Zeng et al.

Large language models (LLMs) exhibit systematic Western-centric bias, yet whether prompting in non-Western languages (e.g., Chinese) can mitigate this remains understudied. Answering this question requires rigorous evaluation and effective mitigation, but existing approaches fall short on both fronts: evaluation methods force outputs into predefined cultural categories without a neutral option, while mitigation relies on expensive multi-cultural corpora or agent frameworks that use functional roles (e.g., Planner--Critique) lacking explicit cultural representation. To address these gaps, we introduce CEBiasBench, a Chinese--English bilingual benchmark, and Multi-Agent Vote (MAV), which enables explicit ``no bias'' judgments. Using this framework, we find that Chinese prompting merely shifts bias toward East Asian perspectives rather than eliminating it. To mitigate such persistent bias, we propose Multi-Agent Cultural Debate (MACD), a training-free framework that assigns agents distinct cultural personas and orchestrates deliberation via a "Seeking Common Ground while Reserving Differences" strategy. Experiments demonstrate that MACD achieves 57.6% average No Bias Rate evaluated by LLM-as-judge and 86.0% evaluated by MAV (vs. 47.6% and 69.0% baseline using GPT-4o as backbone) on CEBiasBench and generalizes to the Arabic CAMeL benchmark, confirming that explicit cultural representation in agent frameworks is essential for cross-cultural fairness.

Till Aust, Christoph Karl Heck, Eduard Buss et al.

We present a bio-hybrid environmental sensor system that integrates natural plants and embedded deep learning for real-time, on-device detection of temperature and ozone level changes. Our system, based on the low-power PhytoNode platform, records electric differential potential signals from Hedera helix and processes them onboard using an embedded deep learning model. We demonstrate that our sensing device detects changes in temperature and ozone with good sensitivity of up to 0.98. Daily and inter-plant variability, as well as limited precision, could be mitigated by incorporating additional training data, which is readily integrable in our data-driven framework. Our approach also has potential to scale to new environmental factors and plant species. By integrating embedded deep learning onboard our biological sensing device, we offer a new, low-power solution for continuous environmental monitoring and potentially other fields of application.

Atsushi Masumori, Norihiro Maruyama, Itsuki Doi et al.

We introduce Plantbot, a hybrid lifeform that connects a living plant with a mobile robot through a network of large language model (LLM) modules. Each module - responsible for sensing, vision, dialogue, or action - operates asynchronously and communicates via natural language, enabling seamless interaction across biological and artificial domains. This architecture leverages the capacity of LLMs to serve as hybrid interfaces, where natural language functions as a universal protocol, translating multimodal data (soil moisture, temperature, visual context) into linguistic messages that coordinate system behaviors. The integrated network transforms plant states into robotic actions, installing normativity essential for agency within the sensor-motor loop. By combining biological and robotic elements through LLM-mediated communication, Plantbot behaves as an embodied, adaptive agent capable of responding autonomously to environmental conditions. This approach suggests possibilities for a new model of artificial life, where decentralized, LLM modules coordination enable novel interactions between biological and artificial systems.

Vinh Truong

This study explores how emotions and sentiments differ in customer reviews of products and services on e-commerce platforms. Unlike earlier research that treats all reviews uniformly, this study distinguishes between reviews of products, typically fulfilling basic, functional needs, and services, which often cater to experiential and emotional desires. The findings reveal clear differences in emotional expression and sentiment between the two. Product reviews frequently focus on practicality, such as functionality, reliability, and value for money, and are generally more neutral or pragmatic in tone. In contrast, service reviews involve stronger emotional engagement, as services often entail personal interactions and subjective experiences. Customers express a broader spectrum of emotions, such as joy, frustration, or disappointment when reviewing services, as identified using advanced machine learning techniques. Cultural background further influences these patterns. Consumers from collectivist cultures, as defined by Hofstede cultural dimensions, often use more moderated and socially considerate language, reflecting an emphasis on group harmony. Conversely, consumers from individualist cultures tend to offer more direct, emotionally intense feedback. Notably, gender appears to have minimal impact on sentiment variation, reinforcing the idea that the nature of the offering (product vs. service) and cultural context are the dominant factors. Theoretically, the study extends Maslow hierarchy of needs and Hofstede cultural framework to the domain of online reviews, proposing a model that explains how these dimensions shape consumer expression. Practically, the insights offer valuable guidance for businesses looking to optimize their marketing and customer engagement strategies by aligning messaging and service design with customer expectations across product types and cultural backgrounds.

Yuankai Zhu, Wenwu Lu, Guoqiang Ren et al.

Plant factory cultivation is widely recognized for its ability to optimize resource use and boost crop yields. To further increase the efficiency in these environments, we propose a mixed-integer linear programming (MILP) framework that systematically schedules and coordinates dual-arm harvesting tasks, minimizing the overall harvesting makespan based on pre-mapped fruit locations. Specifically, we focus on a specialized dual-arm harvesting robot and employ pose coverage analysis of its end effector to maximize picking reachability. Additionally, we compare the performance of the dual-arm configuration with that of a single-arm vehicle, demonstrating that the dual-arm system can nearly double efficiency when fruit densities are roughly equal on both sides. Extensive simulations show a 10-20% increase in throughput and a significant reduction in the number of stops compared to non-optimized methods. These results underscore the advantages of an optimal scheduling approach in improving the scalability and efficiency of robotic harvesting in plant factories.

Yanlin Wang, Yanlin Wang, Yanlin Wang et al.

The growth and quality of medicinal plants depend heavily on environmental variables. The quality of Rubia cordifolia, an important medicinal plant, is determined by the two main secondary metabolites of the root, purpurin and mollugin. However, their relationship with environmental factors has not been studied. In this study, the purpurin and mollugin contents of R. cordifolia roots from different sampling sites in China were measured using ultra-high-performance liquid chromatography, and the correlations between the two secondary metabolites and environmental variables were analyzed. The results showed that there were significant differences in the contents of purpurin and mollugin in the roots of R. cordifolia at different sampling points. The content of purpurin ranged from 0.00 to 3.03 mg g-1, while the content of mollugin ranged from 0.03 to 10.09 mg g-1. The quality of R. cordifolia in Shanxi, Shaanxi and Henan border areas and southeastern Liaoning was higher. Liaoning is expected to become a R. cordifolia planting area in Northeast China. Correlation and regression analysis revealed that the two secondary metabolites were affected by different environmental factors, the two secondary metabolites contents were positively correlated with longitude and latitude, and negatively correlated with soil nutrients. In addition, higher temperature and shorter sunshine duration facilitated the synthesis of purpurin. Annual precipitation might be the main factor limiting the quality of R. cordifolia because it had opposite effects on the synthesis of two major secondary metabolites. Therefore, this study is of great significance for the selection of R. cordifolia planting areas and the improvement of field planting quality.

Thiago H Silva, Daniel Silver

Urban research has long recognized that neighbourhoods are dynamic and relational. However, lack of data, methodologies, and computer processing power have hampered a formal quantitative examination of neighbourhood relational dynamics. To make progress on this issue, this study proposes a graph neural network (GNN) approach that permits combining and evaluating multiple sources of information about internal characteristics of neighbourhoods, their past characteristics, and flows of groups among them, potentially providing greater expressive power in predictive models. By exploring a public large-scale dataset from Yelp, we show the potential of our approach for considering structural connectedness in predicting neighbourhood attributes, specifically to predict local culture. Results are promising from a substantive and methodologically point of view. Substantively, we find that either local area information (e.g. area demographics) or group profiles (tastes of Yelp reviewers) give the best results in predicting local culture, and they are nearly equivalent in all studied cases. Methodologically, exploring group profiles could be a helpful alternative where finding local information for specific areas is challenging, since they can be extracted automatically from many forms of online data. Thus, our approach could empower researchers and policy-makers to use a range of data sources when other local area information is lacking.

Nicolau Andrés-Thió, Charles Audet, Miguel Diago et al.

This work introduces solar, a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a concentrated solar power plant simulated by blackbox numerical models. The type of variables (discrete or continuous), dimensionality, and number and types of constraints (including hidden constraints) differ across instances. Some are deterministic, others are stochastic with possibilities to execute several replications to control stochasticity. Most instances offer variable fidelity surrogates, two are biobjective and one is unconstrained. The solar plant model takes into account various subsystems: a heliostats field, a central cavity receiver (the receiver), a molten salt thermal energy storage, a steam generator and an idealized power block. Several numerical methods are implemented throughout the solar code and most of the executions are time-consuming. Great care was applied to guarantee reproducibility across platforms. The solar tool encompasses most of the characteristics that can be found in industrial and real-life blackbox optimization problems, all in an open-source and stand-alone code.

Ece S. Koksal, Erdal Aydin

Constructing first principles models is a challenging task for nonlinear and complex systems such as a wastewater treatment unit. In recent years, data-driven models are widely used to overcome the complexity. However, they often suffer from issues such as missing, low quality or noisy data. Transfer learning is a solution for this issue where knowledge from another task is transferred to target one to increase the prediction performance. In this work, the objective is increasing the prediction performance of an industrial wastewater treatment plant by transferring the knowledge of (i) an open-source simulation model that captures the underlying physics of the process, albeit with dissimilarities to the target plant, (ii) another industrial plant characterized by noisy and limited data but located in the same refinery, and (iii) the model in (ii) and making the objective function of the training problem physics informed where the physics information derived from the open-source model in (ii). The results have shown that test and validation performance are improved up to 27% and 59%, respectively.

Mandana Mirbakhsh

Over the past half-century, the combination of technology and innovation has been developed to manage the negative impact of synthetic fertilizer on land ecosystems to identify the limitations of sustainability and optimize agricultural systems. The application of nano-fertilizers has achieved considerable interest due to their significant role as environmentally sustainable resources and soil health. Moreover, the increasing global population increased food insecurity, especially under climate change. Nanotechnology has emerged as a promising alternative to help improving crop growth and productivity. However, un-balanced presence and long-term use of nano-particles alter photosynthesis, and induce cellular redox imbalances resulting in lipid peroxidation, protein oxidation and DNA oxidative damage in plants, so more studies are still needed on their safe application with minimum side effects. In this paper, we reviewed nano-fertilizer mechanisms in plant, as well as their effects on microbiome and interaction with soil colloids. This study reviews the recent studies and findings about role of Nanotechnology in plant nutrition use efficiency to summarize a better understanding of this important issue. A comprehensive picture of ecological issues such as soil and water contamination in response to nano-fertilizer application will also be assessed.

Paul K. K. Adu-Gyamfi, Michael Barnnor, Abraham Akpertey et al.

Introduction of exotic clones into the pedigree of commercial cashew clones could constitute a viable strategy to overcome the current low early nut yield and nut weight of the crop in West Africa. The aim of this study was to assess the combining abilities of Beninese, Brazilian and Ghanaian clones for early nut yield and nut weight. Twelve F1 hybrids were evaluated in the field for five traits such as stem diameter increment, canopy spread, nut yield, nut weight and cropping efficiency. Significant difference (p ≤ .01) was observed for most of the traits. Nut weight varied from 5.3 to 10.1 g/year, whilst nut yield ranged from 666.1 to 872.8 kg/ha/year among the top five crosses in the third to fifth year after planting. The Beninese (BE) progenies were comparable to the Ghanaian (SG) progenies for early nut yield but inferior to the Ghanaian and Brazilian (A) progenies for nut weight. Pearson correlation coefficient estimate (r = 0.74; p ≤ .01) suggest that, selecting for canopy spread in the north-south direction might improve early nut yield. GCA effects were more important than SCA effects. Narrow-sense heritability was moderate and exceeded 50% for all the traits. BE203 and SG224 showed positive GCA for early nut yield, whereas A2 and SG273 showed positive GCA for nut weight. Our study suggest that the Brazilian, Beninese and Ghanaian clones had different merits as potential parents for early nut yield and nut weight and could constitute a suitable genetic resource for improving cashew productivity.

Jongchan Lee, Jeongwook Lee, Yongmin Cho et al.

Acidovorax citrulli (Ac) is a gram-negative bacterium that causes bacterial fruit blotch (BFB) disease in cucurbit crops including watermelon. However, despite the great economic losses caused by this disease worldwide, Ac-resistant watermelon cultivars have not been developed. Therefore, characterizing the virulence factors/mechanisms of Ac would enable the development of effective control strategies against BFB disease. The 2,3-bisphosphoglycerate-dependent phosphoglycerate mutase (BdpM) is known to participate in the glycolysis and gluconeogenesis pathways. However, the roles of the protein have not been characterized in Ac. To elucidate the functions of BdpmAc (Bdpm in Ac), comparative proteomic analysis and diverse phenotypic assays were conducted using a bdpmAc knockout mutant (bdpmAc:Tn) and a wild-type strain. The virulence of the mutant to watermelon was remarkably reduced in both germinated seed inoculation and leaf infiltration assays. Moreover, the mutant could not grow with fructose or pyruvate as a sole carbon source. However, the growth of the mutant was restored to levels similar to those of the wild-type strain in the presence of both fructose and pyruvate. Comparative proteomic analyses revealed that diverse proteins involved in motility and wall/membrane/envelop biogenesis were differentially abundant. Furthermore, the mutant exhibited decreased biofilm formation and twitching halo size. Interestingly, the mutant exhibited a higher tolerance against osmotic stress. Overall, our findings suggest that BdpmAc affects the virulence, glycolysis/gluconeogenesis, biofilm formation, twitching halo size, and osmotic tolerance of Ac, suggesting that this protein has pleiotropic properties. Collectively, our findings provide fundamental insights into the functions of a previously uncharacterized phosphoglycerate mutase in Ac.

Jamie Loizzo, Peyton Beattie, Kevin Kent

This is the second publication in the Streaming Science EDIS series focused on how to use mobile hardware and software for engagement with your target audience. This new 6-page article focuses on how to use mobile hardware and cloud-based software for streaming live webcast electronic field trips (EFTs). Streaming Science utilizes EFTs to connect university scientists and Extension professionals across a variety of subject matter areas and contexts with PK–12 students around the world. Written by Jamie Loizzo, Peyton Beattie, and Kevin Kent, and published by the UF/IFAS Department of Agricultural Education and Communication. https://edis.ifas.ufl.edu/wc417

Susumu Naito, Yasunori Taguchi, Kouta Nakata et al.

This paper focuses on anomaly detection for multivariate time series data in large-scale fluid handling plants with dynamic components, such as power generation, water treatment, and chemical plants, where signals from various physical phenomena are observed simultaneously. In these plants, the need for anomaly detection techniques is increasing in order to reduce the cost of operation and maintenance, in view of a decline in the number of skilled engineers and a shortage of manpower. However, considering the complex behavior of high-dimensional signals and the demand for interpretability, the techniques constitute a major challenge. We introduce a Two-Stage AutoEncoder (TSAE) as an anomaly detection method suitable for such plants. This is a simple autoencoder architecture that makes anomaly detection more interpretable and more accurate, in which based on the premise that plant signals can be separated into two behaviors that have almost no correlation with each other, the signals are separated into long-term and short-term components in a stepwise manner, and the two components are trained independently to improve the inference capability for normal signals. Through experiments on two publicly available datasets of water treatment systems, we have confirmed the high detection performance, the validity of the premise, and that the model behavior was as intended, i.e., the technical effectiveness of TSAE.

Dorje C. Brody

Stochastic Schrödinger equations that govern the dynamics of open quantum systems are given by the equations for signal processing. In particular, the Brownian motion that drives the wave function of the system does not represent noise, but provides purely the arrival of new information. Thus the wave function is guided by the optimal signal detection about the conditions of the environments under noisy observations. This behaviour is similar to biological systems that detect environmental cues, process this information, and adapt to them optimally by minimising uncertainties about the conditions of their environments. It is postulated that information-processing capability is a fundamental law of nature, and hence that models describing open quantum systems can equally be applied to biological systems to model their dynamics. For illustration, simple stochastic models are considered to capture heliotropic and gravitropic motions of plants. The advantage of such dynamical models is that it allows for the quantification of information processed by the plants. By considering the consequence of information erasure, it is argued that biological systems can process environmental signals relatively close to the Landauer limit of computation, and that loss of information must lie at the heart of ageing in biological systems.

Shixing Zhou, Shixing Zhou, Caixia Han et al.

The chemical profile of Thymus proximus essential oil (EO) and its allelopathic, phytotoxic, and insecticidal activity was evaluated. Carvacrol, p-cymene, and γ-terpinene were detected as the major components of the EO, representing 85.9% of the total oil. About 50 g fresh plant material of T. proximus in a 1.5-L air tight container completely inhibited the seed germination of Amaranthus retroflexus and Poa anuua. Meanwhile, the EO exhibited potent phytotoxic activity, which resulted in 100% germination failure of both the test species when 2 mg/ml (for A. retroflexus) and 5 mg/ml (for Poa annua) oil was applied. The EO also triggered a significant insecticidal activity on Aphis gossypii with a LC50 value of 6.34 ppm. Carvacrol was identified as the main active compound responsible for both the plant suppressing effect and the insecticidal activity of the EO. Our study is the first on the allelopathic, phytotoxic, and insecticidal activity of T. proximus EO, and the determination of the responsible compound, which indicated their potential of being further explored as environment friendly biopesticides.

Dakshina R. Seal, Qingren Wang, Ramdas Kanissery et al.

Chapter 10 of the Vegetable Production Handbook. Accessibility Summary: In accordance with Title II regulations this content meets all points of exemption as Archived web content and/or Preexisting conventional electronic documents.

Atul Sharma, Bulla Rajesh, Mohammed Javed

Plant leaf diseases pose a significant danger to food security and they cause depletion in quality and volume of production. Therefore accurate and timely detection of leaf disease is very important to check the loss of the crops and meet the growing food demand of the people. Conventional techniques depend on lab investigation and human skills which are generally costly and inaccessible. Recently, Deep Neural Networks have been exceptionally fruitful in image classification. In this research paper, plant leaf disease detection employing transfer learning is explored in the JPEG compressed domain. Here, the JPEG compressed stream consisting of DCT coefficients is, directly fed into the Neural Network to improve the efficiency of classification. The experimental results on JPEG compressed leaf dataset demonstrate the efficacy of the proposed model.