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في معرض «حداثات»، تتناول المعروضات أعمال فنانين مثل برويز تنافولي وفخر النساء زيد ومقبول فدا حسين من منظور مختلف، كما يتضمن المعرض بعضا من مقتنيات مجموعة وثائق آبي ويد غراي في أرشيف جامعة نيويورك، من مراسلات ومدونات ودعوات وغيرها من الوثائق ذات العلاقة بسعيها لاقتناء الأعمال الفنية في منطقة آسيا. وبفضل هذه الوثائق يمكننا الاطلاع على أعمال هؤلاء الفنانين واعتمادهم على إرثهم الثقافي في تعاملهم مع أهم قضايا الحداثة على مستوى عالمي. كما يصاحب المعرض كتاب يتناول تعامل الفنانين من وحي تقاليدهم المحلية مع المتغيرات التي تميز بها المجتمع والعالم في فترة الستينيات، ويساهم في الحوار المستمر الذي يتناول توسعة نطاق الفن الحديث خارج إطاره التقليدي الذي انحصر غالبا في أوروبا وأمريكا الشمالية.‪‪‪

Integrated weed management (IWM) can be defined as a holistic approach to weed management that integrates different methods of weed control to provide the crop with an advantage over weeds. It is practiced globally at varying levels of adoption from farm to farm. IWM has the potential to restrict weed populations to manageable levels, reduce the environmental impact of individual weed management practices, increase cropping system sustainability, and reduce selection pressure for weed resistance to herbicides. There is some debate as to whether simple herbicidal weed control programs have now shifted to more diverse IWM cropping systems. Given the rapid evolution and spread of herbicide-resistant weeds and their negative consequences, one might predict that IWM research would currently be a prominent activity among weed scientists. Here we examine the level of research activity dedicated to weed control techniques and the assemblage of IWM techniques in cropping systems as evidenced by scientific paper publications from 1995 to June 1, 2012. Authors from the United States have published more weed and IWM-related articles than authors from any other country. When IWM articles were weighted as a proportion of country population, arable land, or crop production, authors from Switzerland, the Netherlands, New Zealand, Australia, and Canada were most prominent. Considerable evidence exists that research on nonherbicidal weed management strategies as well as strategies that integrate other weed management systems with herbicide use has increased. However, articles published on chemical control still eclipse any other weed management method. The latter emphasis continues to retard the development of weed science as a balanced discipline. El manejo integrado de malezas (IWM) puede ser definido como un enfoque holístico del manejo de malezas que integra diferentes métodos de control para brindar al cultivo una ventaja sobre las malezas. Esto es practicado globalmente con niveles de adopción que varían de finca a finca. El IWM tiene el potencial de restringir las poblaciones de malezas a niveles manejables, reducir el impacto ambiental de prácticas individuales de manejo de malezas, incrementar la sostenibilidad de los sistemas de cultivos y reducir la presión de selección sobre la resistencia a herbicidas de las malezas. Existe cierto debate acerca de si programas de control de malezas basados simplemente en herbicidas, ahora se han convertido a sistemas de cultivos con IWM más diversos. Dada la rápida evolución y dispersión de malezas resistentes a herbicidas y sus consecuencias negativas, uno podría predecir que la investigación en IWM sería actualmente una actividad prominente entre científicos de malezas. Aquí examinamos el nivel de actividad investigativa dedicada a técnicas de control de malezas y al ensamblaje de técnicas de IWM en sistemas de cultivos, usando como evidencia la publicación de artículos científicos desde 1995 al 1 de Junio, 2012. Autores de los Estados Unidos han publicado más artículos relacionados a malezas y a IWM que autores de cualquier otro país. Cuando se ajustó el peso de los artículos de IWM como proporción de la población del país, tierras arables o producción de cultivos, autores de Suiza, Holanda, Nueva Zelanda, Australia y Canadá fueron los más prominentes. Existe considerable evidencia de que ha incrementado la investigación sobre estrategias no-herbicidas de manejo de malezas y también sobre las estrategias que integran otros sistemas de manejo de malezas con el uso de herbicidas. Sin embargo, los artículos publicados sobre control químico todavía eclipsan cualquier otro método de manejo de malezas. Este último énfasis continúa retrasando el desarrollo de la ciencia de malezas como una disciplina balanceada.

Modernisms explores art from the 1960s and early '70s from Iran, Turkey, and India via selections from an unparalleled collection at New York University. Featuring new scholar ship and seminal essays, this book also illustrates paintings, sculptures, drawings, and prints from these three countries alongside biographical narratives of each Artist. Modernisms will be the first book to provide a cross-cultural study of works from Iran, Turkey, and India. In so doing, it will illuminate our understanding of modern art created outside the long-dominant North American-Western European axis. With nearly 700 works, the Abby Weed Grey Collection comprises the largest institutional holdings of modern art from Iran and Turkey outside those countries, and the most important trove of modern Indian art in an American university museum. Proposing non-Western art as a critical component of modernity, this publication challenges the long held belief that other modernisms are second-rate.

Weed management is a critically important activity on both agricultural and non-agricultural lands, but it is faced with a daunting set of challenges: environmental damage caused by control practices, weed resistance to herbicides, accelerated rates of weed dispersal through global trade, and greater weed impacts due to changes in climate and land use. Broad-scale use of new approaches is needed if weed management is to be successful in the coming era. We examine three approaches likely to prove useful for addressing current and future challenges from weeds: diversifying weed management strategies with multiple complementary tactics, developing crop genotypes for enhanced weed suppression, and tailoring management strategies to better accommodate variability in weed spatial distributions. In all three cases, proof-of-concept has long been demonstrated and considerable scientific innovations have been made, but uptake by farmers and land managers has been extremely limited. Impediments to employing these and other ecologically based approaches include inadequate or inappropriate government policy instruments, a lack of market mechanisms, and a paucity of social infrastructure with which to influence learning, decision-making, and actions by farmers and land managers. We offer examples of how these impediments are being addressed in different parts of the world, but note that there is no clear formula for determining which sets of policies, market mechanisms, and educational activities will be effective in various locations. Implementing new approaches for weed management will require multidisciplinary teams comprised of scientists, engineers, economists, sociologists, educators, farmers, land managers, industry personnel, policy makers, and others willing to focus on weeds within whole farming systems and land management units.

Sustainable strategies for managing weeds are critical to meeting agriculture’s potential to feed the world’s population while conserving the ecosystems and biodiversity on which we depend. The dominant paradigm of weed management in developed countries is currently founded on the two principal tools of herbicides and tillage to remove weeds. However, evidence of negative environmental impacts from both tools is growing, and herbicide resistance is increasingly prevalent. These challenges emerge from a lack of attention to how weeds interact with and are regulated by the agroecosystem as a whole. Novel technological tools proposed for weed control, such as new herbicides, gene editing, and seed destructors, do not address these systemic challenges and thus are unlikely to provide truly sustainable solutions. Combining multiple tools and techniques in an Integrated Weed Management strategy is a step forward, but many integrated strategies still remain overly reliant on too few tools. In contrast, advances in weed ecology are revealing a wealth of options to manage weeds at the agroecosystem level that, rather than aiming to eradicate weeds, act to regulate populations to limit their negative impacts while conserving diversity. Here, we review the current state of knowledge in weed ecology and identify how this can be translated into practical weed management. The major points are the following: (1) the diversity and type of crops, management actions and limiting resources can be manipulated to limit weed competitiveness while promoting weed diversity; (2) in contrast to technological tools, ecological approaches to weed management tend to be synergistic with other agroecosystem functions; and (3) there are many existing practices compatible with this approach that could be integrated into current systems, alongside new options to explore. Overall, this review demonstrates that integrating systems-level ecological thinking into agronomic decision-making offers the best route to achieving sustainable weed management.

Laser weeding may contribute to less dependency on herbicides and soil tillage. Several research and commercial projects are underway to develop robots equipped with lasers to control weeds. Artificial intelligence can be used to locate and identify weed plants, and mirrors can be used to direct a laser beam towards the target to kill it with heat. Unlike chemical and mechanical weed control, laser weeding only exposes a tiny part of the field for treatment. Laser weeding leaves behind only ashes from the burned plants and does not disturb the soil. Therefore, it is an eco-friendly method to control weed seedlings. However, perennial weeds regrow from the belowground parts after the laser destroys the aerial shoots. Depletion of the belowground parts for resources might be possible if the laser continuously kills new shoots, but it may require many laser treatments. We studied how laser could be used to destroy the widespread and aggressive perennial weed Elymus repens after the rhizomes were cut into fragments. Plants were killed with even small dosages of laser energy and stopped regrowing. Generally, the highest efficacy was achieved when the plants from small rhizomes were treated at the 3-leaf stage.

Weed control is necessary to ensure a high crop yield with good quality. Herbicide application and mechanical weeding are the most common methods worldwide. The use of herbicides has led to the increasing occurrence of herbicide-resistant weeds and unwanted contamination of the environment. Mechanical weed control harms beneficial organisms, increases the degradation of organic matter, may dry out the soil, and stimulate new cohorts of weed seeds to germinate. Therefore, there is a need to develop more sustainable weed control means. We suggest using small autonomous vehicles equipped with lasers as a sustainable alternative method. Laser beams are based on electricity, which can be produced from non-fossil fuels. Deep learning methods can be used to locate and identify weed and crop plants for targeting and delivery of laser energy with robotic actuators. Given the targeted nature of laser beams, the area exposed for weed control can be reduced substantially compared to commonly used weed control methods. Therefore, the risk of affecting non-target organisms is minimized, and the soil will be kept untouched in the field, avoiding triggering weed seeds to germinate. Small autonomous vehicles may have limited weeding capacity, and precautions need to be taken as reflections from the laser beam can be harmful to humans and animals. In this paper, we discuss the pros and cons of replacing or supplementing common used weed control methods with laser weeding. The ability to use laser weeding technology is relatively new and not yet widely practiced or commercially available. Therefore, we do not discuss and compare the costs of the various methods at this early stage of the development of the technology.

Weeds are a fundamental component of agroecosystems and, if not appropriately managed, can cause severe crop yield losses. New perspectives on weed management are required, because current approaches, such as herbicide application or soil tillage, have significant environmental and agronomic drawbacks. We propose the concept of “neutral weed communities,” which are weed communities that coexist with crops and do not negatively affect crop yield and quality compared with weed-free conditions. Management practices that promote neutral weed communities can enable reduced use of herbicides and soil tillage while enhancing ecosystem services and biodiversity. We report scientific evidence of neutral weed communities and survey ecological explanations for why different weed communities have different effects on crop production. We also propose two weed management approaches for attaining neutral weed communities. The first approach aims to maximize weed biodiversity using traditional approaches such as cropping system diversification and integrated weed management. Higher weed biodiversity is associated with lower dominance of competitive weed species that reduce crop yield. The second approach relies on modern tools such as robots and biotechnology to manipulate the density of specific weed species. This approach can remove highly problematic species and minimize niche overlap between the weeds and crops. Given the complexity of interactions among crops, weeds, and other components of the agroecosystem, we highlight the need for multidisciplinary research to illuminate mechanisms that determine the neutrality of weed communities.

Weed detection and classification using computer vision and deep learning techniques have emerged as crucial tools for precision agriculture, offering automated solutions for sustainable farming practices. This study presents a comprehensive approach to weed identification across multiple growth stages, addressing the challenges of detecting and classifying diverse weed species throughout their developmental cycles. We introduce two extensive datasets: the Alpha Weed Dataset (AWD) with 203,567 images and the Beta Weed Dataset (BWD) with 120,341 images, collectively documenting 16 prevalent weed species across 11 growth stages. The datasets were preprocessed using both traditional computer vision techniques and the advanced SAM-2 model, ensuring high-quality annotations with segmentation masks and precise bounding boxes. Our research evaluates several state-of-the-art object detection architectures, including DINO Transformer (with ResNet-101 and Swin backbones), Detection Transformer (DETR), EfficientNet B4, YOLO v8, and RetinaNet. Additionally, we propose a novel WeedSwin Transformer architecture specifically designed to address the unique challenges of weed detection, such as complex morphological variations and overlapping vegetation patterns. Through rigorous experimentation, WeedSwin demonstrated superior performance, achieving 0.993 ± 0.004 mAP and 0.985 mAR while maintaining practical processing speeds of 218.27 FPS, outperforming existing architectures across various metrics. The comprehensive evaluation across different growth stages reveals the robustness of our approach, particularly in detecting challenging “driver weeds” that significantly impact agricultural productivity. By providing accurate, automated weed identification capabilities, this research establishes a foundation for more efficient and environmentally sustainable weed management practices. The demonstrated success of the WeedSwin architecture, combined with our extensive temporal datasets, represents a significant advancement in agricultural computer vision, supporting the evolution of precision farming techniques while promoting reduced herbicide usage and improved crop management efficiency.

The discipline of weed science is at a critical juncture. Decades of efficient chemical weed control have led to a rise in the number of herbicide-resistant weed populations, with few new herbicides with unique modes of action to counter this trend and often no economical alternatives to herbicides in large-acreage crops. At the same time, the world population is swelling, necessitating increased food production to feed an anticipated 9 billion people by the year 2050. Here, we consider these challenges along with emerging trends in technology and innovation that offer hope of providing sustainable weed management into the future. The emergence of natural product leads in discovery of new herbicides and biopesticides suggests that new modes of action can be discovered, while genetic engineering provides additional options for manipulating herbicide selectivity and creating entirely novel approaches to weed management. Advances in understanding plant pathogen interactions will contribute to developing new biological control agents, and insights into plant–plant interactions suggest that crops can be improved by manipulating their response to competition. Revolutions in computing power and automation have led to a nascent industry built on using machine vision and global positioning system information to distinguish weeds from crops and deliver precision weed control. These technologies open multiple possibilities for efficient weed management, whether through chemical or mechanical mechanisms. Information is also needed by growers to make good decisions, and will be delivered with unprecedented efficiency and specificity, potentially revolutionizing aspects of extension work. We consider that meeting the weed management needs of agriculture by 2050 and beyond is a challenge that requires commitment by funding agencies, researchers, and students to translate new technologies into durable weed management solutions. Integrating old and new weed management technologies into more diverse weed management systems based on a better understanding of weed biology and ecology can provide integrated weed management and resistance management strategies that will be more sustainable than the technologies that are now failing.