Cut-edge detection method for wheat harvesting based on stereo vision

Zhang, Zhenqian; Zhang, Xisen; Cao, Ruyue; Zhang, Man; Li, Han; Yin, Yong; Wu, Shulan

doi:10.1016/j.compag.2022.106910

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…It turns to the next row when another feature indicating the end of the row is recognised. (English et al, 2014;Li et al, 2020b;Papadakis, 2013;Rabab et al, 2021;Zhai et al, 2016;Zhang et al, 2022). This simple idea is just kin to autonomous guided vehicles used in warehouses, factories, and controlled hazardous zones.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of autonomous mobile platform for agricultural task in corridor-like environments

Tobias,

Lim,

Duke

et al. 2024

Preprint

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The role of autonomous vehicles (AVs) in assisting people is recognised and, therefore, is in constant development in numerous fields. Specifically, the ability of AV to alleviate global stressors, including an increased potential for food shortages and the decline in workers for potentially laborious work. An area where AVs developments are particularly prevalent is in agriculture. However, the few AVs being used in agriculture are often custom-built for specific purposes and require long development time as a result. This article aims to build and evaluate a versatile architecture of a mobile platform that is implemented using off-the-shelf components so that it can be transferred to any agricultural vehicle, thus reducing the development time. The research has involved investigating and incorporating various sensors, and also developing a common software module to perform the localisation, navigation and mapping particularly suited for corridor crop agricultural environment. This architecture has been integrated and implemented on a Yamaha golf cart, infusing it with purposely positioned sensors and supportive electronics to allow a Robotic Operating System (ROS) framework to gather information and control the vehicle. As the architecture is modular in nature, it can be transferred to different customised platforms. To determine the efficacy of the mobile platform, it has gone through several outdoor field trials to test the fundamentals of an agricultural AV. The evaluation demonstrates that both mapping and navigation have satisfactory results and the mobile platform remains within 5mm of the specified distance when aiming to follow the row in a vineyard. The results from these experiments demonstrate the ability of the mobile platform to successfully transform a Yamaha golf cart into an autonomous agricultural vehicle.

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Section: Literature Reviewmentioning

confidence: 99%

Implementation of autonomous mobile platform for agricultural task in corridor-like environments

Tobias,

Lim,

Duke

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Zhang 13 developed a machine-vision-based tip detection method that selected the Cr component of the YCbCr color model as the grayscale feature factor and automatically acquired the region of interest (ROI). In 2022, another method 14 was proposed to extract the wheat harvesting navigation path using binocular cameras by extracting point clouds and using a polynomial fitting method. Traditional image processing methods are mostly based on the extraction of features such as color and texture and implement algorithms such as clustering and watershed segmentation, which require manual extraction of feature information.…”

Section: Introductionmentioning

confidence: 99%

DeeplabV3+-based navigation line extraction for the sunlight robust combine harvester

Cheng,

Jin,

Chen

2024

Science Progress

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Visual navigation is widely used in intelligent combine harvesters, but the existing algorithms do not have sufficiently high accuracy of the visual navigation line recognition under different sunlight conditions. To address this problem, this article proposes a sunlight-robust DeepLabV3+-based navigation line extraction method for combine harvesters. The navigation lines are extracted by constructing a new dataset and predicting the boundaries of the areas that have been and have not been cut. To address the problem that DeeplabV3+ is not sufficient light in the DCNN part, improvement is proposed by incorporating the MobileNetV2 module. In image segmentation, the prediction time is 22.5 ms, and the mean intersection over union ( FMIOU) is 0.79. After image segmentation, the navigation lines are drawn using the line segment detection algorithm for the harvester. The proposed method is compared with other mainstream networks, and the prediction results are compared using the line segment detection method. The results show that this method can more quickly identify the navigation lines under different conditions of sunlight with less labeled data than the improved U-Net and DeeplabV3+, which uses Xception as the backbone. Compared to the traditional method and the improved U-Net, this method achieves good results and improves the recognition speed by 27 and 9 ms, respectively.

show abstract

Implementation of an autonomous mobile platform for agricultural tasks in corridor-like environments

Tobias,

Lim,

Duke

et al. 2024

Int J Intell Robot Appl

View full text Add to dashboard Cite

The role of autonomous vehicles (AVs) in assisting people is recognised and, therefore, is in constant development in numerous fields. Specifically, the ability of an autonomous vehicle (AV) to alleviate global stressors, such as the increased potential for food shortages and the decline in available workers for labor-intensive tasks. An area where the development of AVs are particularly prevalent is in agriculture. However, the few AVs being used in agriculture are often custom-built for specific purposes and require long development time as a result. This article aims to build and evaluate a versatile architecture for a mobile platform that is implemented using off-the-shelf components so that it can be transferred to any agricultural vehicle, thus reducing the development time. The research has involved investigating and incorporating various sensors, and also developing a common software module to perform the localisation, navigation and mapping particularly suited for corridor crop agricultural environments. This architecture has been integrated and implemented on a Yamaha golf cart, integrating it with sensors and electronics to allow a Robotic Operating System (ROS) framework to gather information and control the vehicle. As the architecture is modular in nature, it can be transferred to different customised platforms. To determine the efficacy of the mobile platform, it has undergone evaluation in simulation and in the field. The evaluation demonstrates that both mapping and navigation have satisfactory results, and the mobile platform remains within 5 mm of the specified distance when aiming to follow the row in a vineyard. The results from these experiments demonstrate the ability of the mobile platform to successfully transform a Yamaha golf cart into an autonomous agricultural vehicle.

show abstract

Cut-edge detection method for wheat harvesting based on stereo vision

Cited by 8 publications

References 19 publications

Implementation of autonomous mobile platform for agricultural task in corridor-like environments

Implementation of autonomous mobile platform for agricultural task in corridor-like environments

DeeplabV3+-based navigation line extraction for the sunlight robust combine harvester

Implementation of an autonomous mobile platform for agricultural tasks in corridor-like environments

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