Sensing for Weed Detection

Christensen, Svend; Dyrmann, Mads; Jørgensen, Rasmus Nyholm; Rasmussen, Jesper

doi:10.1007/978-3-030-78431-7_10

Cited by 4 publications

(2 citation statements)

References 54 publications

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“…Recent advances in computer vision systems that can detect and identify weeds at the species level enable monitoring and precision management applications that are increasing knowledge on weed management, reducing herbicide inputs, and providing more effective control of herbicide-resistant weeds (Beckie et al 2019; Christensen et al 2021). Training and validating the machine learning (ML) algorithms that enable species-level classification of weeds within crop fields requires a large number of annotated images of individual species at multiple growth stages; the lack of suitable data sets is a major barrier to further innovation in this area (Wu et al 2021).…”

Section: Present—ars Weed Science Researchmentioning

confidence: 99%

Agricultural Research Service Weed Science Research: Past, Present, and Future

Young¹,

Anderson²,

Baerson

et al. 2023

Weed Sci

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The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.

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Section: Present—ars Weed Science Researchmentioning

confidence: 99%

Agricultural Research Service Weed Science Research: Past, Present, and Future

Young¹,

Anderson²,

Baerson

et al. 2023

Weed Sci

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“…UAVs or drones are preferred over satellite and manned aerial platforms for image acquisition/remote sensing due to their cost effectiveness, flexibility in data acquisition, ability to conduct low altitude operations, immunity to cloud covers, and ease of operation [4]. Optical sensors are the most preferred technology for data acquisition in UAV based automated weed detection systems [5]. The UAVs equipped with multispectral and hyperspectral sensors help in capturing spectral information of the crops in multiple wavelengths.…”

Section: Introductionmentioning

confidence: 99%

Automated Grassweed Detection in Wheat Cropping System: Current Techniques and Future Scope

Shrestha,

Ojha,

Sharma

et al. 2024

JPA

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Wheat is a staple grain crop in the United States and around the world. Weed infestation, particularly grass weeds, poses significant challenges to wheat production, competing for resources and reducing grain yield and quality. Effective weed management practices, including early identification and targeted herbicide application are essential to avoid economic losses. Recent advancements in unmanned aerial vehicles (UAVs) and artificial intelligence (AI), offer promising solutions for early weed detection and management, improving efficiency and reducing negative environment impact. The integration of robotics and information technology has enabled the development of automated weed detection systems, reducing the reliance on manual scouting and intervention. Various sensors in conjunction with proximal and remote sensing techniques have the capability to capture detailed information about crop and weed characteristics. Additionally, multi-spectral and hyperspectral sensors have proven highly effective in weed vs crop detection, enabling early intervention and precise weed management. The data from various sensors consecutively processed with the help of machine learning and deep learning models (DL), notably Convolutional Neural Networks (CNNs) method have shown superior performance in handling large datasets, extracting intricate features, and achieving high accuracy in weed classification at various growth stages in numerous crops. However, the application of deep learning models in grass weed detection for wheat crops remains underexplored, presenting an opportunity for further research and innovation. In this review we underscore the potential of automated grass weed detection systems in enhancing weed management practices in wheat cropping systems. Future research should focus on refining existing techniques, comparing ML and DL models for accuracy and efficiency, and integrating UAV-based mapping with AI algorithms for proactive weed control strategies. By harnessing the power of AI and machine learning, automated weed detection holds the key to sustainable and efficient weed management in wheat cropping systems.

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Review of Current Robotic Approaches for Precision Weed Management

Zhang

et al. 2022

Curr Robot Rep

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Purpose of ReviewThe goal of this review is to provide an overview of current robotic approaches to precision weed management. This includes an investigation into applications within this field during the past 5 years, identifying which major technical areas currently preclude more widespread use, and which key topics will drive future development and utilisation. Recent Findings Studies combining computer vision with traditional machine learning and deep learning are driving progress in weed detection and robotic approaches to mechanical weeding. Integrating key technologies for perception, decisionmaking, and control, autonomous weeding robots are emerging quickly. These effectively save effort while reducing environmental pollution caused by pesticide use. Summary This review assesses different weed detection methods and weeder robots used in precision weed management and summarises the trends in this area in recent years. The limitations of current systems are discussed, and ideas for future research directions are proposed.

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Sensing for Weed Detection

Cited by 4 publications

References 54 publications

Agricultural Research Service Weed Science Research: Past, Present, and Future

Agricultural Research Service Weed Science Research: Past, Present, and Future

Automated Grassweed Detection in Wheat Cropping System: Current Techniques and Future Scope

Review of Current Robotic Approaches for Precision Weed Management

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