Advances in application technology and innovative techniques within the agricultural industry have revolutionized the potential for weed management on both spatial and temporal scales. These precision tools have enabled more site-specific approaches thereby maximizing weed control effectiveness, as well as reducing potential environmental impacts. However, numerous aspects must be currently researched to fully understand and implement these strategies. The investigation of these novel tools, sensors, nozzle technology, and machine learning algorithms are all important pieces of this revolutionary weed science research arena. Additionally, understanding how these pieces must work together, their potential impacts on the environment, and the underlying effectiveness on weed management must all be conceptualized as well.Herbicide resistance, environmental concerns, and more stringent regulatory demands are enhancing the need for more precise and site-specific weed management strategies. There have been vast advancements in technology made in recent years, particularly in the agricultural sector. These advancements paired with modern machine learning approaches allow for the use of innovative tools and techniques never before feasible to manage weeds more effectively in a site-specific approach.The goal of this Research Topic article collection is to highlight these innovative technological advancements and novel techniques for detecting, identifying, and successfully controlling problematic weed species, as well as reducing negative environmental effects.The scope of this Research Topic is far reaching including any such topics that incorporate new technologies or techniques for implementing effective weed management. Many subtopics will fit under the scope of this Research Topic, but specific focus within the manuscript should be placed on the impacts for resulting weed control. Such subtopics may include, but are not limited to:- Application technology (spray equipment, nozzles, formulations, adjuvants, etc.)- Drones (remote sensing, spraying, aerial seeding)- Proximal sensing tools and implementation- Specialized control equipment (laser, electrical, etc.)- Use of machine learning and artificial intelligence for weed detection, classification, and management- Detect and remove technologies (post-processing and live)- Harvest weed seed control (seed destructors, chaff lining, tramlines, etc.)- Innovative cultural control strategies (targeted tillage, unique cover crop establishment, intercropping, etc.)- Herbicide drift reduction techniques- Spot or selective spraying (spray drones, sense and spray systems, etc.)- Alternative pesticide application methods- Sensing and control systems for precision applications- Pulse-width modulation- Variable rate applications.