Base position detection of grape stem considering its displacement for weeding robot in vineyards

Igawa, Hisashi; Tanaka, Takayuki; Kaneko, Shun’ichi; Tada, Tatsumi; Suzuki, Shinichi; Ohmura, Isao

doi:10.1109/iecon.2012.6388846

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…These four centre values (colours) are experimentally determined (by trial and error) during the experimental phase. In [16] a tactile sensing technique is employed to haptically recognise grape stems by means of a multi-link manipulator. A supervised classifier based on the Mahalanobis distance is applied in [17] for characterising the grapevine canopy and assess leaf area and yield using RGB images.…”

Section: Introductionmentioning

confidence: 99%

Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements

Fernández

Montes

Salinas

et al. 2013

Sensors

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This paper proposes a sequential masking algorithm based on the K-means method that combines RGB and multispectral imagery for discrimination of Cabernet Sauvignon grapevine elements in unstructured natural environments, without placing any screen behind the canopy and without any previous preparation of the vineyard. In this way, image pixels are classified into five clusters corresponding to leaves, stems, branches, fruit and background. A custom-made sensory rig that integrates a CCD camera and a servo-controlled filter wheel has been specially designed and manufactured for the acquisition of images during the experimental stage. The proposed algorithm is extremely simple, efficient, and provides a satisfactory rate of classification success. All these features turn out the proposed algorithm into an appropriate candidate to be employed in numerous tasks of the precision viticulture, such as yield estimation, water and nutrients needs estimation, spraying and harvesting.

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Section: Introductionmentioning

confidence: 99%

Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements

Fernández

Montes

Salinas

et al. 2013

Sensors

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“…Berenstein and Edan (2017) used the color and distance cameras to detect the target for their autonomous spraying platform, however, use of the color‐based thresholding for the image processing algorithm limits its applicability in the natural lighting conditions. Igawa et al (2012) used the stereo‐vision cameras to detect the trunk for the automated weeding operation in vineyard rows, however, the results were affected by trunk detection errors due to use of traditional machine vision approaches (considering only area and orientation for trunk detection). Luo et al (2016) used the color‐based segmentation approaches to detect the peduncle of grape clusters for grape harvesting robot which can be easily affected by the changing light conditions, hence affecting the overall performance of the robot (Lin et al, 2020).…”

Section: Relevant Prior Workmentioning

confidence: 99%

Development and performance evaluation of a machine vision system and an integrated prototype for automated green shoot thinning in vineyards

Majeed

Karkee

Zhang

et al. 2021

Journal of Field Robotics

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Green shoot thinning in vineyards is an essential, perennial operation for maintaining canopy health and optimizing yield and quality of wine grapes. Use of mechanized thinning system, which is essential to reduce labor dependency and associated cost, causes high variability in shoot removal efficiency due to difficulty in precisely positioning the thinning end‐effector along cordon trajectories. Automated/robotic solution for precise positioning of the thinning end‐effector could significantly improve the performance and efficiency of mechanical green shoot thinning. This study presents: (i) a machine vision‐based cordon detection system that can estimate cordon trajectories at different shoot growth stages in a vineyard; and (ii) evaluation of an integrated green shoot thinning system capable of automatically positioning the thinning end‐effector following vine cordon trajectories. The developed machine vision system uses deep learning‐based techniques that could accurately estimate cordon trajectories with root mean square error (RMSE) of 7.3, 10.3, and 16.1 pixels for canopy images captured in 2–4 weeks of shoot growth, respectively. Then, a control strategy was presented for the integrated system, which receives the computed cordon trajectories from machine vision system to automatically position the thinning end‐effector to cordon trajectories. Field evaluations in a research vineyard showed that the integrated system can achieve an RMSE of 1.47 cm in following the cordon trajectories at 6.6 cm·s−1 forward speed. Future work will incorporate additional sensing system to detect individual shoot on cordon and integrating it with an existing system to achieve higher level of precision in green shoot thinning.

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Neural Network as a Tool for Detection of Wine Grapes

Doležel

Škrabánek

Gago

2016

Advances in Intelligent Systems and Computing

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Base position detection of grape stem considering its displacement for weeding robot in vineyards

Cited by 5 publications

References 7 publications

Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements

Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements

Development and performance evaluation of a machine vision system and an integrated prototype for automated green shoot thinning in vineyards

Neural Network as a Tool for Detection of Wine Grapes

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