Strawberry harvesting robot for fruits grown on table top culture

Shiigi, Tomowo; Kurita, Mitsutaka; Kondo, Naoshi; Ninomiya, Kazunori; Rajendra, Peter; Kamata, Junzo; Hayashi, Shigehiko; Kobayashi, Ken; Shigematsu, Kenta; Kohno, Yasushi

doi:10.13031/2013.24754

Cited by 4 publications

(1 citation statement)

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“…Some picking robots experience overall vibrations during their work process, leading to identification and positioning failures [171,172], and lens distortion causing inaccurate image imaging. Shiigi [173] developed a three-armed picking robot with a suction gripper as the end effector. After fixing the fruit with the suction device, the fruit stalk is grasped with two fingers and then cut on both sides according to the tilt angle.…”

Section: Uncertainty In Fruit Picking Due To Complex Work Environmentsmentioning

confidence: 99%

An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots

Hou,

Chen,

Jiang

et al. 2023

Agriculture

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Intelligent agriculture imposes higher requirements on the recognition and localization of fruit and vegetable picking robots. Due to its unique visual information and relatively low hardware cost, machine vision is widely applied in the recognition and localization of fruit and vegetable picking robots. This article provides an overview of the application of machine vision in the recognition and localization of fruit and vegetable picking robots. Firstly, the advantages, disadvantages, and the roles of different visual sensors and machine vision algorithms in the recognition and localization of fruit and vegetable picking robots are introduced, including monocular cameras, stereo cameras, structured light cameras, multispectral cameras, image segmentation algorithms, object detection algorithms, and 3D reconstruction algorithms. Then, the current status and challenges faced by machine vision in the recognition and localization of fruit and vegetable picking robots are summarized. These challenges include the stability of fast recognition under complex background interference, stability of recognition under different lighting environments for the same crop, the reliance of recognition and localization on prior information in the presence of fruit overlap and occlusions caused by leaves and branches, and the uncertainty of picking caused by complex working environments. In current research on algorithms dealing with complex background interference and various occlusion disturbances, good results have been achieved. Different lighting environments have a significant impact on the recognition and positioning of fruits and vegetables, with a minimum accuracy of 59.2%. Finally, this article outlines future research directions to address these challenges.

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Section: Uncertainty In Fruit Picking Due To Complex Work Environmentsmentioning

confidence: 99%

An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots

Hou,

Chen,

Jiang

et al. 2023

Agriculture

View full text Add to dashboard Cite

show abstract

Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control

Rajendran

Debnath

Mghames

et al. 2023

Journal of Field Robotics

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This paper provides an overview of the current state‐of‐the‐art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labor costs, and minimize wastage by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning, and control. The paper also discusses the potential benefits of integrating artificial intelligence and soft robots and data‐driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.

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Disease sniffing robots to apps fixing plant diseases: applications of artificial intelligence in plant pathology—a mini review

Prabha¹

2020

Indian Phytopathology

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Strawberry harvesting robot for fruits grown on table top culture

Cited by 4 publications

References 0 publications

An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots

An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots

Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control

Disease sniffing robots to apps fixing plant diseases: applications of artificial intelligence in plant pathology—a mini review

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