Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation

Fernandes, Miguel; Scaldaferri, Antonello; Fiameni, Giuseppe; Teng, Tao; Gatti, Matteo; Poni, Stefano; Semini, Claudio; Caldwell, Darwin G.; Chen, Fei

doi:10.1109/cyber53097.2021.9588303

Cited by 13 publications

(11 citation statements)

References 11 publications

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“…We believe that soft robotics may represent a technology that can serve humanity and the planet by addressing global challenges and environmental degradation through targeted productive applications (Mengaldo et al, 2022). Figure 1 (Fernandes et al, 2021), soft grippers are more versatile and robust during an interaction because they can deform, adapt, and easily control while colliding with the external world (Bonilla et al, 2014;Mazzolai et al, 2019;Navas et al, 2021). 2.…”

Section: Synergy Between Soft Robotics and Sustainable Development Goalsmentioning

confidence: 99%

Soft robotics towards sustainable development goals and climate actions

2023

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Soft robotics technology can aid in achieving United Nations’ Sustainable Development Goals (SDGs) and the Paris Climate Agreement through development of autonomous, environmentally responsible machines powered by renewable energy. By utilizing soft robotics, we can mitigate the detrimental effects of climate change on human society and the natural world through fostering adaptation, restoration, and remediation. Moreover, the implementation of soft robotics can lead to groundbreaking discoveries in material science, biology, control systems, energy efficiency, and sustainable manufacturing processes. However, to achieve these goals, we need further improvements in understanding biological principles at the basis of embodied and physical intelligence, environment-friendly materials, and energy-saving strategies to design and manufacture self-piloting and field-ready soft robots. This paper provides insights on how soft robotics can address the pressing issue of environmental sustainability. Sustainable manufacturing of soft robots at a large scale, exploring the potential of biodegradable and bioinspired materials, and integrating onboard renewable energy sources to promote autonomy and intelligence are some of the urgent challenges of this field that we discuss in this paper. Specifically, we will present field-ready soft robots that address targeted productive applications in urban farming, healthcare, land and ocean preservation, disaster remediation, and clean and affordable energy, thus supporting some of the SDGs. By embracing soft robotics as a solution, we can concretely support economic growth and sustainable industry, drive solutions for environment protection and clean energy, and improve overall health and well-being.

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Section: Synergy Between Soft Robotics and Sustainable Development Goalsmentioning

confidence: 99%

Soft robotics towards sustainable development goals and climate actions

2023

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“…This is derived from a list of points found in the initial search. Each possible row line could be found using the determinant of the grapevine points detected from the list of points, as shown in (2). If the determinant is 0, the list of points is a line.…”

Section: A Higher Level Controlmentioning

confidence: 99%

“…The VINUM 2 project uses IIT's HyQReal quadruped robot to do the winter pruning of grapevines autonomously with the Kivoa Gen3 robotic arm [2] [3]; see Fig. 1, [4].…”

mentioning

confidence: 99%

Computer-Vision Based Real Time Waypoint Generation for Autonomous Vineyard Navigation with Quadruped Robots

Milburn¹,

Gamba

Fernandes

et al. 2023

2023 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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The VINUM project seeks to address the shortage of skilled labor in modern vineyards by introducing a cuttingedge mobile robotic solution. Leveraging the capabilities of the quadruped robot, HyQReal, this system, equipped with arm and vision sensors, offers autonomous navigation and winter pruning of grapevines reducing the need for human intervention. At the heart of this approach lies an architecture that empowers the robot to easily navigate vineyards, identify grapevines with unparalleled accuracy, and approach them for pruning with precision. A state machine drives the process, deftly switching between various stages to ensure seamless and efficient task completion. The system's performance was assessed through experimentation, focusing on waypoint precision and optimizing the robot's workspace for single-plant operations. Results indicate that the architecture is highly reliable, with a mean error of 21.5cm and a standard deviation of 17.6cm for HyQReal. However, improvements in grapevine detection accuracy are necessary for optimal performance. This work is based on a computer-vision-based navigation method for quadruped robots in vineyards, opening up new possibilities for selective task automation. The system's architecture works well in ideal weather conditions, generating and arriving at precise waypoints that maximize the attached robotic arm's workspace. This work is an extension of our short paper presented at the Italian Conference on Robotics and Intelligent Machines (I-RIM), 2022 [1].

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“…Dıáz et al (2018) used fast library for approximate nearest neighbor (FLANN), support vector machine (SVM), and densitybased spatial clustering of applications with noise (DBSCAN) to locate grape buds in three-dimensional space, where FLANN was used to match key points, SVM was used to classify 3D points, and finally, DBSCAN was used to locate grape buds. Fernandes et al (2021) used Mask R-CNN to identify potential pruning points of grape trees with a simple tree shape and finally determined the pruning locations of branches in combination with the graph theory algorithm. Adhikari et al (2011) and Karkee et al (2014) used the medial axis thinning algorithm to generate the threedimensional skeleton of the apple tree point cloud and recognized pruning points under the guidance of the pruning rules.…”

Section: Introductionmentioning

confidence: 99%

Skeleton extraction and pruning point identification of jujube tree for dormant pruning using space colonization algorithm

Xia²,

Zhang³

et al. 2023

Front. Plant Sci.

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The dormant pruning of jujube is a labor-intensive and time-consuming activity in the production and management of jujube orchards, which mainly depends on manual operation. Automatic pruning using robots could be a better way to solve the shortage of skilled labor and improve efficiency. In order to realize automatic pruning of jujube trees, a method of pruning point identification based on skeleton information is presented. This study used an RGB-D camera to collect multi-view information on jujube trees and built a complete point cloud information model of jujube trees. The space colonization algorithm acts on the global point cloud to generate the skeleton of jujube trees. The iterative relationship between skeleton points was represented by constructing a directed graph. The proposed skeleton analysis algorithm marked the skeleton as the trunk, the primary branches, and the lateral branches and identified the pruning points under the guidance of pruning rules. Finally, the visual model of the pruned jujube tree was established through the skeleton information. The results showed that the registration errors of individual jujube trees were less than 0.91 cm, and the average registration error was 0.66 cm, which provided a favorable database for skeleton extraction. The skeleton structure extracted by the space colonization algorithm had a high degree of coincidence with jujube trees, and the identified pruning points were all located on the primary branches of jujube trees. The study provides a method to identify the pruning points of jujube trees and successfully verifies the validity of the pruning points, which can provide a reference for the location of the pruning points and visual research basis for automatic pruning.

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Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation

Cited by 13 publications

References 11 publications

Soft robotics towards sustainable development goals and climate actions

Soft robotics towards sustainable development goals and climate actions

Computer-Vision Based Real Time Waypoint Generation for Autonomous Vineyard Navigation with Quadruped Robots

Skeleton extraction and pruning point identification of jujube tree for dormant pruning using space colonization algorithm

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