Visual and tactual recognition of trunk of grape for weeding robot in vineyards

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

doi:10.1109/iecon.2009.5415067

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…The vision system was only tested in indoor operations, such as in greenhouses that have constant illumination. Subsequently, intrarow weeding operations in orchards based on vision using a stereo camera system have also been designed, primarily focused on trunk detection [17]. However, weed location, which is less efficient for intrarow weeding, has not been considered in these systems.…”

Section: Related Workmentioning

confidence: 99%

Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations

Sampurno,

Liu,

Abeyrathna

et al. 2024

Sensors

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Mechanical weed management is a drudging task that requires manpower and has risks when conducted within rows of orchards. However, intrarow weeding must still be conducted by manual labor due to the restricted movements of riding mowers within the rows of orchards due to their confined structures with nets and poles. However, autonomous robotic weeders still face challenges identifying uncut weeds due to the obstruction of Global Navigation Satellite System (GNSS) signals caused by poles and tree canopies. A properly designed intelligent vision system would have the potential to achieve the desired outcome by utilizing an autonomous weeder to perform operations in uncut sections. Therefore, the objective of this study is to develop a vision module using a custom-trained dataset on YOLO instance segmentation algorithms to support autonomous robotic weeders in recognizing uncut weeds and obstacles (i.e., fruit tree trunks, fixed poles) within rows. The training dataset was acquired from a pear orchard located at the Tsukuba Plant Innovation Research Center (T-PIRC) at the University of Tsukuba, Japan. In total, 5000 images were preprocessed and labeled for training and testing using YOLO models. Four versions of edge-device-dedicated YOLO instance segmentation were utilized in this research—YOLOv5n-seg, YOLOv5s-seg, YOLOv8n-seg, and YOLOv8s-seg—for real-time application with an autonomous weeder. A comparison study was conducted to evaluate all YOLO models in terms of detection accuracy, model complexity, and inference speed. The smaller YOLOv5-based and YOLOv8-based models were found to be more efficient than the larger models, and YOLOv8n-seg was selected as the vision module for the autonomous weeder. In the evaluation process, YOLOv8n-seg had better segmentation accuracy than YOLOv5n-seg, while the latter had the fastest inference time. The performance of YOLOv8n-seg was also acceptable when it was deployed on a resource-constrained device that is appropriate for robotic weeders. The results indicated that the proposed deep learning-based detection accuracy and inference speed can be used for object recognition via edge devices for robotic operation during intrarow weeding operations in orchards.

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Section: Related Workmentioning

confidence: 99%

Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations

Sampurno,

Liu,

Abeyrathna

et al. 2024

Sensors

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“…Robotics for vineyards have been well studied for different applications in both academics and industries [7]. Both single [8,9] and multi-robot scenarios [10] for localization with trunk recognition have been proposed. Other researchers have focused on autonomous pruning [11] and total yield estimation [12,13].…”

Section: Introductionmentioning

confidence: 99%

Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

Ravankar

Rawankar³

et al. 2021

Agriculture

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In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

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“…In contrast, improvements in and adoption of improved weed management technologies have been made in other countries. Such technologies include precision weed management (Partel et al, 2019;Westwood et al, 2018), use of herbicides (Hale et al, 2019;Harker & O'Donovan, 2013), biotechnological approaches (Beckie et al, 2019;Duke, 2003) robotic weeders (Fennimore & Cutulle, 2019;Igawa et al, 2009;Lowenberg-DeBoer et al, 2019;Reiser et al, 2019;Sabanci & Aydin, 2017;Siemens, 2014;Slaughter et al, 2008), automated systems with sensor and computer technologies (Young et al, 2014), crop allelopathy (Alsaadawi et al, 2015;Macías et al, 2019;Trezzi et al, 2016;Uddin et al, 2014), flaming (Stepanovic et al, 2016) and other technologies providing site-specific weed control (Coleman et al, 2019). It is feared that in emerging economies and rural areas, weak technological infrastructure, high costs of technology, low levels of e-literacy and digital skills, weak regulatory framework and limited access to services mean these areas risk being left behind in the digitalization process (Trendov et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A survey of problem weeds of sorghum and their management in two sorghum-producing districts of Zimbabwe

Chiduza

Mashingaidze

2020

Cogent Social Sciences

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Sorghum is an important staple food crop in dry areas of Zimbabwe but yields are reduced due to inefficient weed management strategies that largely rely on hand weeding. A study to establish weeds associated with sorghum and their management was conducted in two sorghum-growing districts, Insiza and Chipinge, during the 2016/17 cropping season. Physical weed sampling in farmers' fields was done to identify weeds infesting sorghum. A questionnaire was used to collect survey data from 80 respondents who were randomly selected. Physical weed sampling established that the dominant weeds that infested sorghum in the two districts were Amaranthus hybridus, Richardia scabra, Tagetes minuta, Striga asiatica, Commelina benghalensis, Eleusine indica, Datura stramonium and Panicum spp. Many of the weeds are broadleaf species, offering opportunity to harness sorghum allelopathy for weed control. Allelopathic compounds exuded by sorghum, such as ABOUT THE AUTHORSHandsen Tibugari is a PhD student studying sorghum allelopathy at the

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Visual and tactual recognition of trunk of grape for weeding robot in vineyards

Cited by 8 publications

References 3 publications

Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations

Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations

Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

A survey of problem weeds of sorghum and their management in two sorghum-producing districts of Zimbabwe

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