Agricultural robots for field operations. Part 2: Operations and systems

Bechar, Avital; Vigneault, Clément

doi:10.1016/j.biosystemseng.2016.11.004

Cited by 236 publications

(118 citation statements)

References 150 publications

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“…For example, soft fruit picking still requires fundamental research in sensing, manipulation and soft robotics. Thus, at least in the short-term, the collaboration of humans and robots is fundamental to increased productivity and food quality [24]. There are a number of comparatively low-cost platforms available now that are certified for use alongside human workers.…”

Section: Technology Visionmentioning

confidence: 99%

Agricultural Robotics: The Future of Robotic Agriculture

et al. 2018

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Section: Technology Visionmentioning

confidence: 99%

Agricultural Robotics: The Future of Robotic Agriculture

et al. 2018

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“…Automated weed control, including weed detection and removal, has gained significant popularity in the community of precision farming over recent years (Bechar & Vigneault, 2017; Fennimore, Slaughter, Siemens, Leon, & Saber, 2016), due to its great potential to improve the weeding efficiency while reducing the environmental and economic costs. Many robotic weed control systems have been proposed with focuses primarily on single tactics (Slaughter, Giles, & Downey, 2008): selective chemical spraying (Lee, Slaughter, & Giles, 1999), mechanical weeding (Pannacci, Lattanzi, & Tei, 2017), flaming (Datta & Knezevic, 2013), and electrical discharging (Blasco, Aleixos, Roger, Rabatel, & Molto, 2002; Vigneault & Benoît, 2001; Xiong, Ge, Liang, & Blackmore, 2017).…”

Section: Introductionmentioning

confidence: 99%

Robotic weed control using automated weed and crop classification

Aravecchia

Lottes

et al. 2020

Journal of Field Robotics

125

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Autonomous robotic weeding systems in precision farming have demonstrated their full potential to alleviate the current dependency on agrochemicals such as herbicides and pesticides, thus reducing environmental pollution and improving sustainability. However, most previous works require fast and constant‐time weed detection systems to achieve real‐time treatment, which forecloses the implementation of more capable but time‐consuming algorithms, for example, learning‐based methods. In this paper, a nonoverlapping multicamera system is applied to provide flexibility for the weed control system in dealing with the indeterminate classification delays. The design, implementation, and testing of our proposed modular weed control unit with mechanical and chemical weeding tools are presented. A framework that performs naive Bayes filtering, 3D direct intra‐ and inter‐camera visual tracking, and predictive control, while integrating state‐of‐the‐art crop/weed detection algorithms, is developed to guide the tools to achieve high‐precision weed removal. The experimental results show that our proposed fully operational weed control system is capable of performing selective mechanical as well as chemical in‐row weeding with indeterminate detection delays in different terrain conditions and crop growth stages.

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“…The main objective is to reduce production costs and manual labor, while increasing yield and raising product quality (Luettel et al, 2012;Bechar and Vigneault, 2017). A significant milestone is to make robots navigate autonomously in dynamic, rough, and unstructured environments, such as agricultural fields or orchards.…”

Section: Related Workmentioning

confidence: 99%

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

et al. 2018

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Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar) and cameras (stereo and thermal). Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles). Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multimodal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes.

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Agricultural robots for field operations. Part 2: Operations and systems

Cited by 236 publications

References 150 publications

Agricultural Robotics: The Future of Robotic Agriculture

Agricultural Robotics: The Future of Robotic Agriculture

Robotic weed control using automated weed and crop classification

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

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