Hasil untuk "Plant culture"

Chakri Saïd, Sraïri Mohamed Taher, Mrani Alaoui Mohammed

Dans le Rif occidental du Maroc, les pelouses semi-naturelles du site Jbel Bouhachem constituent des habitats essentiels aux systèmes d’élevage local. Cette étude vise à analyser comment les facteurs topographiques influencent les pratiques pastorales dans ce territoire montagnard. Quinze pelouses de parcours ont été identifiées, situées entre 1000 et 1500 m d’altitude. Leur superficie varie de 0,42 à 16,72 ha, avec des pentes supérieures à 10 % dans plus de la moitié des cas. L’enquête socio-pastorale, menée auprès de vingt-deux villageois, met en évidence une spécialisation altitudinale marquée : les pelouses d’altitude (> 1400 m) sont exploitées à plus de 95 % par les caprins, tandis que celles à faible altitude accueillent une diversité d’espèces (caprins, ovins, bovins). La transhumance ne subsiste que sur deux pelouses (Menzla et Bab Miiz), pendant une période estivale réduite. L’analyse statistique a mis en évidence des corrélations négatives significatives entre la charge pastorale et les variables topographiques, notamment l’altitude (r = –0,47), la pente (r = –0,49) et l’humidité du sol (TWI, r = –0,53), indiquant une sous-utilisation des zones escarpées ou humides. Une analyse croisée montre que les pelouses les plus sèches (TWI < 8) supportent les charges animales les plus élevées, tandis que les plus humides (TWI > 8,5) sont faiblement exploitées. Une classification topographique a permis d’identifier trois types de pelouses (haute altitude et forte pente, altitude moyenne et pente modérée, basse altitude et pente douce), tandis que l’analyse en composantes principales a dégagé trois profils zootechniques : intensif (souvent caprin, > 300 UGB/ha), modéré (50–150 UGB/ha) et marginal, en lien avec l’accessibilité et les contraintes du milieu. Ces résultats soulignent l’importance des contraintes topographiques dans l’organisation des pratiques pastorales et offrent des bases pour une gestion différenciée et contextualisée de ces milieux de pâturage.

Dipta Sumeru Rinandio, Iin Pertiwi A. Husaini, Mahat Magandhi et al.

Tianlin Shen, Jilai Cui, Shuo Yang et al.

Plant-soil-microbe interactions can affect plant growth, development, and health. Plants can secrete bioactive molecules into the rhizosphere to alter the soil microbiota, further influencing plant growth. In this paper, the effects of flavonols secreted by tea roots on the remodeling of rhizosphere bacteria and on growth of tea plants were explored. Aluminum treatment significantly promoted the growth of tea plants, the accumulation of flavonols glycosides in the roots and the secretion of flavonols glycosides from roots. 16S rRNA analysis indicated that after aluminum treatment, the rhizosphere bacteria Burkholderia of the Proteobacteria phylum were significantly enriched. Compared to 'Longjin43' (LJ43), in roots of 'Huangjinye' (HJY), more flavonols were accumulated, so did in the root exudates. Moreover, Burkholderia in the rhizosphere of 'HJY' was significantly enriched. The results of correlation analysis indicated that the abundance of Burkholderia was significantly positively correlated with the secretion of flavonols under aluminum treatment or in different tea cultivar. Then 0.05 mM kaempferol were exogenous application to confirm the growth-promoting effect of flavonols on tea plants and the recruitment of Burkholderia in rhizosphere of tea roots. In conclusion, tea roots secrete flavonol glycosides into the rhizosphere soil, which can recruit Burkholderia and further promote the growth of tea plants. This study laid foundation for the subsequent development of bacterial fertilizers to promote the growth of tea plants.

Blessing Austin-Gabriel, Aparna S. Varde, Hao Liu

This paper proposes an approach in the area of Knowledge-Guided Machine Learning (KGML) via a novel integrated framework comprising CNN (Convolutional Neural Networks) and ViT (Vision Transformers) along with GIS (Geographic Information Systems) to enhance power plant classification in the context of energy management. Knowledge from geoinformatics derived through Spatial Masks (SM) in GIS is infused into an architecture of CNN and ViT, in this proposed KGML approach. It is found to provide much better performance compared to the baseline of CNN and ViT only in the classification of multiple types of power plants from real satellite imagery, hence emphasizing the vital role of the geoinformatics-guided approach. This work makes a contribution to the main theme of KGML that can be beneficial in many AI systems today. It makes broader impacts on AI in Smart Cities, and Environmental Computing.

Saptarshi Banerjee, Tausif Mallick, Amlan Chakroborty et al.

Addressing plant diseases and pests is critical for enhancing crop production and preventing economic losses. Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have significantly improved the precision and efficiency of detection methods, surpassing the limitations of manual identification. This study reviews modern computer-based techniques for detecting plant diseases and pests from images, including recent AI developments. The methodologies are organized into five categories: hyperspectral imaging, non-visualization techniques, visualization approaches, modified deep learning architectures, and transformer models. This structured taxonomy provides researchers with detailed, actionable insights for selecting advanced state-of-the-art detection methods. A comprehensive survey of recent work and comparative studies demonstrates the consistent superiority of modern AI-based approaches, which often outperform older image analysis methods in speed and accuracy. In particular, vision transformers such as the Hierarchical Vision Transformer (HvT) have shown accuracy exceeding 99.3% in plant disease detection, outperforming architectures like MobileNetV3. The study concludes by discussing system design challenges, proposing solutions, and outlining promising directions for future research.

Simeon Adebola, Chung Min Kim, Justin Kerr et al.

Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed "annotated digital twins" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models. We also present robot algorithms for manipulating leaves to take high-resolution indexable images of occluded details such as stem buds and the underside/topside of leaves. Results from experiments suggest that Botany-Bot can segment leaves with 90.8% accuracy, detect leaves with 86.2% accuracy, lift/push leaves with 77.9% accuracy, and take detailed overside/underside images with 77.3% accuracy. Code, videos, and datasets are available at https://berkeleyautomation.github.io/Botany-Bot/.

Amit Yaron, Zhuo Zhang, Dai Akita et al.

Dissociated neuronal cultures provide a simplified yet effective model system for investigating self-organized prediction and information processing in neural networks. This review consolidates current research demonstrating that these in vitro networks display fundamental computational capabilities, including predictive coding, adaptive learning, goal-directed behavior, and deviance detection. We examine how these cultures develop critical dynamics optimized for information processing, detail the mechanisms underlying learning and memory formation, and explore the relevance of the free energy principle within these systems. Building on these insights, we discuss how findings from dissociated neuronal cultures inform the design of neuromorphic and reservoir computing architectures, with the potential to enhance energy efficiency and adaptive functionality in artificial intelligence. The reduced complexity of neuronal cultures allows for precise manipulation and systematic investigation, bridging theoretical frameworks with practical implementations in bio-inspired computing. Finally, we highlight promising future directions, emphasizing advancements in three-dimensional culture techniques, multi-compartment models, and brain organoids that deepen our understanding of hierarchical and predictive processes in both biological and artificial systems. This review aims to provide a comprehensive overview of how dissociated neuronal cultures contribute to neuroscience and artificial intelligence, ultimately paving the way for biologically inspired computing solutions.

B. Iegorov, А. Iegorova, К. Yeryganov

Compound feeds are favorable environments for microorganisms, namely bacteria and fungi. Both groups contain nonpathogenic, opportunistic and pathogenic members. Bacteria can cause infections, and fungi can cause mycotoxicoses, so controlling microbial contamination of feed is of great importance. Sources of microbial contamination of compound feed include both raw materials and the production environment: air, surfaces, equipment, and the hands and clothing of employees. As raw materials and pre-products move through production lines, they undergo certain processes that can either reduce or increase microbial contamination. The reducing processes include grain dehulling and high-temperature processing (moisture and heat treatment, conditioning, extrusion, expansion, drying of minerals, granulation), while the increasing processes include making preliminary mixtures, grinding, dosing and mixing, and the production of grits. In dosing and mixing, the contribution of each component is determined by its dose and has a two-way effect: the component contributes its microbiota, but its mass dilutes the mixture. The sequence of these processes represents a certain dynamics of microbial burden, which will result in the contamination level of the finished product lower or higher than in the initial raw material. This sequence can be represented as a microbiological graph, the vertices of which are the positive or negative contributions of the processes to the microbiological burden of the material. And the system of such vertices can be represented as a simple mathematical equation. Creating microbiological graphs for individual production lines or for the manufacture as a whole can help to understand the microbiological dynamics in a material or product and apply appropriate corrective measures to prevent microbiological hazards in the final product. This paper proposes a method of making a microbiological graph with two types of vertex designations for the IV generation compound feed production, as well as a mathematical apparatus for calculating the vertices of the graph.

Eugênio Silva Araújo Júnior, Anselmo Ferreira Silva, Josemir Moura Maia et al.

The aim of this study was to evaluate the influence of scion/rootstock genotypes on ionic toxicity, oxidative damage, and photosynthesis in cashew plants subjected to salt stress. Scion/rootstock combinations (CCP 76/CCP 76, CCP 76/CCP 09, CCP 09/CCP 09 and CCP 09/CCP 76) were obtained by reciprocal grafting between two genotypes (CCP 76 and CCP 09) of dwarf cashew and subjected to increased NaCl (0, 50 and 100 mM) for 30 days. Plants with CCP 76 scions had higher leaf fresh weights compared to plants with CCP 09 scions in both moderate (50 mM)- and high (100 mM)-salinity conditions. Under moderate levels of salinity, CCP 76 scions showed lower stomatal conductance, which is associated with weaker leaf toxicity symptoms, as well as lower Na⁺ content and higher K⁺ content in the leaves. Thus, the better foliar exclusion of Na⁺ by CCP 76 scions can be attributed to greater stomatal control, which allows for better growth and sufficient foliar K⁺ nutrition to mitigate foliar toxicity. Under high levels of salinity, a reduction in net photosynthesis occurred in all scion/rootstock combinations, which was apparently due to stomatal and non-stomatal restrictions. The activities of the oxidative enzymes (superoxide dismutase—SOD; ascorbate peroxidase—APX; and phenol peroxidase—POD) were little influenced by salinity, while there was a significant increase in the non-enzymatic antioxidants ascorbate (AsA) and glutathione (GSH). In addition, a reduction in photochemical activity was observed under saline conditions, suggesting that photosystems possess a potential protective mechanism. It was observed that the stomatal closure exhibited by the CCP 76 scion genotype may exert relative control over the flow of Na⁺ to the shoots under salt stress conditions. Taken together, the data show that, in the two genotypes evaluated, oxidative protection was more associated with reduced photochemical activity and higher levels of non-enzymatic antioxidants (AsA and GSH) than it was with the SOD-APX-POD enzymatic system.

Anna Villalba, Eva Martínez-Ispizua, Miguel Morard et al.

This study investigates the impact of sweet potato plant sanitation on the yield and external and internal quality root storage exploring the nutritional content affected by various cooking methods (raw, boiled, and oven-cooked). The presence of viruses, and concretely of the sweet potato leaf curl virus (SPLCV), in sweet potato propagation material is shown to significantly reduce yield and modify storage root quality. Notably, the research reveals a substantial improvement in crop yield and external quality, reinforcing the efficacy of plant sanitation methods, specifically apical meristem culture, in preserving the overall productivity of sweet potato crops. Furthermore, the investigation identifies a noteworthy decrease in starch content, suggesting a dynamic interaction between plant sanitation and starch metabolism in response to viral diseases. The study also delves into the alteration of mineral absorption patterns, shedding light on how plant sanitation influences the uptake of essential minerals in sweet potato storage roots. While the health status of the plants only slightly affected magnesium (Mg) and manganese (Mn) accumulation, indicating a potential resilience of mineral balance under virus-infected conditions. Moreover, the research identifies significant modifications in antioxidant levels, emphasizing the role of plant sanitation in enhancing the nutritional quality of sweet potatoes. Heat-treated storage roots, subjected to various cooking methods such as boiling and oven-cooking, exhibit notable differences in internal quality parameters. These differences include increased concentrations of total soluble solids (SS) and heightened levels of antioxidant compounds, particularly phenolic and flavonoid compounds. The observed increase in antioxidant capacity underscores the potential health-promoting benefits associated with plant sanitation practices. Overall, the study underscores the critical importance of plant sanitation in enhancing sweet potato production sustainability, contributing to food security, and supporting local agricultural economies. The results emphasize the need for further research to optimize plant sanitation methods and promote their widespread adoption globally, providing valuable insights into the complex relationships in food quality.

Esther Tinco Mamani

Existe la posibilidad de que el tiempo de sumersión en los enraizadores naturales, respecto a la propagación vegetativa por estacas de higo presente un efecto significativo o se observan diferencias en la formación y características de las raíces. Por ello, se planteó la investigación realizada en el Vivero Multipropósito, perteneciente al Centro Experimental Cota Cota, bajo un diseño completamente al azar con arreglo factorial, empleándose una planta madre de más de 12 años, lo que es un indicio de que la propagación sería dificultosa, por las características del material vegetal. Las variables de respuesta evaluadas fueron: días a la formación de raíz, porcentaje de sobrevivencia, longitud de raíz, número de raíces, porcentaje de prendimiento. Obteniéndose los siguientes resultados en base a las medias de los datos recolectados en la investigación en campo, el T9 (agua de lenteja, 48 horas) enraizó en 54 días, siendo el mejor tiempo de enraizamiento. El porcentaje de sobrevivencia fue influido por el factor enraizante de forma directa, respondiendo mucho mejor los enraizadores naturales de agua de lenteja e infusión de sauce, con 85.70 y 83.66 % respectivamente. El enraizante y el tiempo de sumersión presentó una significancia directa en la longitud de raíz que es igual a 7.60 cm, con el tratamiento 5 (infusión de sauce, 24 horas). En la variable número de raíces tanto el agua de lenteja y la infusión de sauce lograron 8 y 7 unidades de raíces respectivamente, ambos en un tiempo de sumersión de 24 horas, además es importante observar la significancia en ambos factores. El tratamiento 9 (agua de lenteja, 48 horas) con 90 % fue el mejor promedio en la variable porcentaje de prendimiento. Confirmándose que, a más tiempo de sumersión, el enraizamiento de las estacas fue positiva, dependiendo del enraizador utilizado.

Nasla Saleem, Aditya Balu, Talukder Zaki Jubery et al.

Data augmentation is a powerful tool for improving deep learning-based image classifiers for plant stress identification and classification. However, selecting an effective set of augmentations from a large pool of candidates remains a key challenge, particularly in imbalanced and confounding datasets. We propose an approach for automated class-specific data augmentation using a genetic algorithm. We demonstrate the utility of our approach on soybean [Glycine max (L.) Merr] stress classification where symptoms are observed on leaves; a particularly challenging problem due to confounding classes in the dataset. Our approach yields substantial performance, achieving a mean-per-class accuracy of 97.61% and an overall accuracy of 98% on the soybean leaf stress dataset. Our method significantly improves the accuracy of the most challenging classes, with notable enhancements from 83.01% to 88.89% and from 85.71% to 94.05%, respectively. A key observation we make in this study is that high-performing augmentation strategies can be identified in a computationally efficient manner. We fine-tune only the linear layer of the baseline model with different augmentations, thereby reducing the computational burden associated with training classifiers from scratch for each augmentation policy while achieving exceptional performance. This research represents an advancement in automated data augmentation strategies for plant stress classification, particularly in the context of confounding datasets. Our findings contribute to the growing body of research in tailored augmentation techniques and their potential impact on disease management strategies, crop yields, and global food security. The proposed approach holds the potential to enhance the accuracy and efficiency of deep learning-based tools for managing plant stresses in agriculture.

Lingfeng Li, Xiangwen Zheng

Emojis have become a universal language in the digital world, enabling users to express emotions, ideas, and identities across diverse cultural contexts. As emojis incorporate more cultural symbols and diverse representations, they play a crucial role in cross-cultural communication. This research project aims to analyze the representation of different cultures in emojis, investigate how emojis facilitate cross-cultural communication and promote inclusivity, and explore the impact of emojis on understanding and interpretation in different cultural contexts.

Lisabeth Marie Santana, Chandralekha Singh

We present comparative case study of three physics department culture from different institutions using the experiences undergraduate women. The three studies conducted in the United States include Johnson's 2020 study in a small physics department at a small predominantly White liberal arts college, Santana and Singh's 2023 study at a large predominantly White research institution, and Santana and Singh's 2024 study in a medium-sized physics department at a small predominantly White private liberal arts college. Using synergistic frameworks such as Standpoint Theory, Domains of Power, and the Holistic Ecosystem for Learning Physics in an Inclusive and Equitable Environment and reflections from undergraduate women, we aim to understand how those in the position of power, e.g., instructors, have important roles in establishing and maintaining safe, equitable, and inclusive environments for undergraduate students. Their accounts help us contrast the experiences of undergraduate women in physics departments with very different cultures. This comparative analysis is especially important for reflecting upon what can be done to improve the physics culture so that historically marginalized students such as women and ethnic and racial minority students in physics feel supported and thrive. In particular, this comparative case study can be invaluable to understand the positive and negative aspects of the physics culture as they impact undergraduate women majoring in physics within these three departments. This analysis can help other physics departments contemplate strategies to improve the physics culture so that all undergraduate physics majors have validating experiences while navigating their physics journey regardless of their identities.

D. Adriana Gómez-Rosal, Max Bergau, Georg K. J. Fischer et al.

In today's chemical plants, human field operators perform frequent integrity checks to guarantee high safety standards, and thus are possibly the first to encounter dangerous operating conditions. To alleviate their task, we present a system consisting of an autonomously navigating robot integrated with various sensors and intelligent data processing. It is able to detect methane leaks and estimate its flow rate, detect more general gas anomalies, recognize oil films, localize sound sources and detect failure cases, map the environment in 3D, and navigate autonomously, employing recognition and avoidance of dynamic obstacles. We evaluate our system at a wastewater facility in full working conditions. Our results demonstrate that the system is able to robustly navigate the plant and provide useful information about critical operating conditions.

Pravesh K. Kothari, Santosh S. Vempala, Alexander S. Wein et al.

We study the computational complexity of two related problems: recovering a planted $q$-coloring in $G(n,1/2)$, and finding efficiently verifiable witnesses of non-$q$-colorability (a.k.a. refutations) in $G(n,1/2)$. Our main results show hardness for both these problems in a restricted-but-powerful class of algorithms based on computing low-degree polynomials in the inputs. The problem of recovering a planted $q$-coloring is equivalent to recovering $q$ disjoint planted cliques that cover all the vertices -- a potentially easier variant of the well-studied planted clique problem. Our first result shows that this variant is as hard as the original planted clique problem in the low-degree polynomial model of computation: each clique needs to have size $k \gg \sqrt{n}$ for efficient recovery to be possible. For the related variant where the cliques cover a $(1-ε)$-fraction of the vertices, we also show hardness by reduction from planted clique. Our second result shows that refuting $q$-colorability of $G(n,1/2)$ is hard in the low-degree polynomial model when $q \gg n^{2/3}$ but easy when $q \lesssim n^{1/2}$, and we leave closing this gap for future work. Our proof is more subtle than similar results for planted clique and involves constructing a non-standard distribution over $q$-colorable graphs. We note that while related to several prior works, this is the first work that explicitly formulates refutation problems in the low-degree polynomial model. The proofs of our main results involve showing low-degree hardness of hypothesis testing between an appropriately constructed pair of distributions. For refutation, we show completeness of this approach: in the low-degree model, the refutation task is precisely as hard as the hardest associated testing problem, i.e., proving hardness of refutation amounts to finding a "hard" distribution.

Andres Ferraro, Gustavo Ferreira, Fernando Diaz et al.

Recommender systems have become the dominant means of curating cultural content, significantly influencing individual cultural experience. Since recommender systems tend to optimize for personalized user experience, they can overlook impacts on cultural experience in the aggregate. After demonstrating that existing metrics do not center culture, we introduce a new metric, commonality, that measures the degree to which recommendations familiarize a given user population with specified categories of cultural content. We developed commonality through an interdisciplinary dialogue between researchers in computer science and the social sciences and humanities. With reference to principles underpinning public service media systems in democratic societies, we identify universality of address and content diversity in the service of strengthening cultural citizenship as particularly relevant goals for recommender systems delivering cultural content. We develop commonality as a measure of recommender system alignment with the promotion of content toward a shared cultural experience across a population of users. We empirically compare the performance of recommendation algorithms using commonality with existing metrics, demonstrating that commonality captures a novel property of system behavior complementary to existing metrics. Alongside existing fairness and diversity metrics, commonality contributes to a growing body of scholarship developing `public good' rationales for machine learning systems.

Faizal Anam Al Ubaidah Lubis, Saktiyono Sigit Tri Pamungkas, Fitria Nugraheni Sukmawati

Sugarcane (Saccharum officinarum L.) is a plantation crop that is used as raw material for the consumer sugar and industrial sugar. The need for sugar is increasing every year but is not matched by an increase in sugarcane production due to several factors including cultivation management that is not optimal. Sugarcane production begins with good nursery management, including using genetic of seeds and the right planting media. One alternative to improve the quality of growing media is to use humic acid (HA) as a soil enhancer. This study aims to determinate the effect of giving HA on the morphological characteristics of sugarcane seedlings of Bululawang variety (BL). This research was carried out in an integrated laboratory greenhouse at the Polytechnic LPP Yogyakarta from Maret to July 2021. This study used a non-factorial completely randomized design (CRD) with five treatments and three replications consisting of P0 (control), P1 (25 ml.polybag-1), P2 (50 ml.polybag-1), P3 (75 ml.polybag-1), and P4 (100 ml.polybag-1). The morphological characteristics observed is plants height (cm), number of leaves (strands), stem diameter (mm) and longest root length (cm). the results of the study were analyzed using ANOVA at the 5% level and continued using the Duncan Multiple Range Test (DMRT) at the 5% level. The result showed the effect on morphological characters on all observation variables where the P3 treatment had the best growth and morphological characters, so that in general the additional of HA affected the morphological characteristics of sugarcane seedlings of BL varieties.

Weicheng Ma, Samiha Datta, Lili Wang et al.

While cultural backgrounds have been shown to affect linguistic expressions, existing natural language processing (NLP) research on culture modeling is overly coarse-grained and does not examine cultural differences among speakers of the same language. To address this problem and augment NLP models with cultural background features, we collect, annotate, manually validate, and benchmark EnCBP, a finer-grained news-based cultural background prediction dataset in English. Through language modeling (LM) evaluations and manual analyses, we confirm that there are noticeable differences in linguistic expressions among five English-speaking countries and across four states in the US. Additionally, our evaluations on nine syntactic (CoNLL-2003), semantic (PAWS-Wiki, QNLI, STS-B, and RTE), and psycholinguistic tasks (SST-5, SST-2, Emotion, and Go-Emotions) show that, while introducing cultural background information does not benefit the Go-Emotions task due to text domain conflicts, it noticeably improves deep learning (DL) model performance on other tasks. Our findings strongly support the importance of cultural background modeling to a wide variety of NLP tasks and demonstrate the applicability of EnCBP in culture-related research.

Karoline Heiwolt, Cengiz Öztireli, Grzegorz Cielniak

Plants are dynamic organisms and understanding temporal variations in vegetation is an essential problem for robots in the wild. However, associating repeated 3D scans of plants across time is challenging. A key step in this process is re-identifying and tracking the same individual plant components over time. Previously, this has been achieved by comparing their global spatial or topological location. In this work, we demonstrate how using shape features improves temporal organ matching. We present a landmark-free shape compression algorithm, which allows for the extraction of 3D shape features of leaves, characterises leaf shape and curvature efficiently in few parameters, and makes the association of individual leaves in feature space possible. The approach combines 3D contour extraction and further compression using Principal Component Analysis (PCA) to produce a shape space encoding, which is entirely learned from data and retains information about edge contours and 3D curvature. Our evaluation on temporal scan sequences of tomato plants shows, that incorporating shape features improves temporal leaf-matching. A combination of shape, location, and rotation information proves most informative for recognition of leaves over time and yields a true positive rate of 75%, a 15% improvement on sate-of-the-art methods. This is essential for robotic crop monitoring, which enables whole-of-lifecycle phenotyping.