Graph weeds net: A graph-based deep learning method for weed recognition

Hu, Kun; Coleman, Guy; Zeng, Shan; Zhi-yong, Wang; Walsh, Michael

doi:10.1016/j.compag.2020.105520

Cited by 90 publications

(42 citation statements)

References 20 publications

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“…Figure 1 shows an example of a sprayer UAV for pesticide application. Combining multiple techniques in the integrated weed management (IWM) strategy is a step toward reducing problems related to conventional approaches, such as herbicide resistance [5,15]. UAV photography helps to better categorise results in early-season agronomic conditions where crop and weed seedlings have similar spectral signatures [16].…”

Section: Introductionmentioning

confidence: 99%

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

et al. 2021

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Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.

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Section: Introductionmentioning

confidence: 99%

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

et al. 2021

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“…Relevant scholars began to study semi-supervised learning with only a small amount of labeled data and unsupervised feature learning without data labeling [ 142 , 143 ]. Hu et al [ 34 ] proposed a new image-based deep learning architecture called Graph Weed Network (GWN). The purpose is to identify multiple types of weeds from RGB images collected from complex pastures.…”

Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

154

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Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.

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“…Research on precision agriculture robotics has recently focused on two areas: (i) weed inspection and targeted spraying and (ii) fruit and vegetables harvesting robots [5]. The first area is mostly represented by outdoor robots for weed control such as the Graph Weeds Net [14], the RHEA project centered on both agriculture and forestry [15], BoniRob project dedicated to multipurpose farming [16], or CROPS project focused on precision spraying in vineyards [17]. The navigation of these outdoor robots is largely based on the use of satellite localization systems and their signal is much weaker and unprecise in indoor environments, making them less suitable for greenhouses [18].…”

Section: Related Workmentioning

confidence: 99%

A Generic ROS-Based Control Architecture for Pest Inspection and Treatment in Greenhouses Using a Mobile Manipulator

et al. 2021

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To meet the demands of a rising population greenhouses must face the challenge of producing more in a more efficient and sustainable way. Innovative mobile robotic solutions with flexible navigation and manipulation strategies can help monitor the field in real-time. Guided by Integrated Pest Management strategies, robots can perform early pest detection and selective treatment tasks autonomously. However, combining the different robotic skills is an error prone work that requires experience in many robotic fields, usually deriving on ad-hoc solutions that are not reusable in other contexts. This work presents Robotframework, a generic ROS-based architecture which can easily integrate different navigation, manipulation, perception, and high-decision modules leading to a faster and simplified development of new robotic applications. The architecture includes generic real-time data collection tools, diagnosis and error handling modules, and user-friendly interfaces. To demonstrate the benefits of combining and easily integrating different robotic skills using the architecture, two flexible manipulation strategies have been developed to enhance the pest detection in its early state and to perform targeted spraying in simulated and field commercial greenhouses. Besides, an additional use-case has been included to demonstrate the applicability of the architecture in other industrial contexts.INDEX TERMS Precision agriculture, robotic control architecture, mobile manipulator, pest detection and treatment, greenhouse.

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Graph weeds net: A graph-based deep learning method for weed recognition

Cited by 90 publications

References 20 publications

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Review of Weed Detection Methods Based on Computer Vision

A Generic ROS-Based Control Architecture for Pest Inspection and Treatment in Greenhouses Using a Mobile Manipulator

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