Zanthoxylum bungeanum Fruit Detection by Adaptive Thresholds in HSV Space for an Automatic Picking System

He, Longke; Cheng, Xiao; Jiwa, Aying; Li, Dan; Fang, Jing; Du, Zhencong

doi:10.1109/jsen.2023.3277042

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…HSV color space-based segmentation could be adapted to lighting and reduce noise. A similar conclusion can be found in the article [29].…”

Section: Discussionsupporting

confidence: 89%

Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Wang,

Han,

et al. 2024

Agriculture

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Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification in drone images. This study proposed a deep learning method, including an improved YOLOv8 model and a new image-processing framework, which could accurately and comprehensively identify mature strawberries, immature strawberries, and strawberry flowers in drone images. The improved YOLOv8 model used the shuffle attention block and the VoV–GSCSP block to enhance identification accuracy and detection speed. The environmental stability-based region segmentation was used to extract the strawberry plant area (including fruits, stems, and leaves). Edge extraction and peak detection were used to estimate the number of strawberry plants. Based on the number of strawberry plants and the distribution of mature strawberries, we draw a growth chart of strawberries (reflecting the urgency of picking in different regions). The experiment showed that the improved YOLOv8 model demonstrated an average accuracy of 82.50% in identifying immature strawberries, 87.40% for mature ones, and 82.90% for strawberry flowers in drone images. The model exhibited an average detection speed of 6.2 ms and a model size of 20.1 MB. The proposed new image-processing technique estimated the number of strawberry plants in a total of 100 images. The bias of the error for images captured at a height of 2 m is 1.1200, and the rmse is 1.3565; The bias of the error for the images captured at a height of 3 m is 2.8400, and the rmse is 3.0199. The assessment of picking priorities for various regions of the strawberry field in this study yielded an average accuracy of 80.53%, based on those provided by 10 experts. By capturing images throughout the entire growth cycle, we can calculate the harvest index for different regions. This means farmers can not only obtain overall ripeness information of strawberries in different regions but also adjust agricultural strategies based on the harvest index to improve both the quantity and quality of fruit set on strawberry plants, as well as plan the harvesting sequence for high-quality strawberry yields.

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“…HSV color space-based segmentation could be adapted to lighting and reduce noise. A similar conclusion can be found in the article [29].…”

Section: Discussionsupporting

confidence: 89%

Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Wang,

Han,

et al. 2024

Agriculture

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“…Robot competitions are widely held in Indonesia and other countries as part of efforts to develop robotics. One example is the ball wheel robot competition [1], [2]. The ball wheel robot utilizes a camera as a means to gather information about the objects it will track [3].…”

Section: Introductionmentioning

confidence: 99%

“…In this research, we explore an innovative method for recognizing and tracking a ball for a ballwheeled robot. The robot relies on a camera as its primary source of information for detecting and following objects [1]- [4]. The image analysis process involves visual perception and image processing, characterised by input data and output information in the form of images [5], [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Implementing hue-saturation-value filter and circle hough transform for object tracking on ball-wheeled robot

Sugiarto,

Kusuma,

Firdaus

et al. 2024

IJRES

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The ball-wheeled robot relies on a camera for receiving information on the object to be followed. Object tracing is one of the methods that can be used for detecting object movement. In recognizing objects around it, the robot requires an image analysis process that involves visual perception. Image processing is the process of processing and analyzing images that involves visual perception, and is characterized by input data and output information in the form of images. This is how the robot can see objects around it and then be assisted by computer vision to make a decision. The object tracking method with hue-saturation-value (HSV) colour filtering and shape recognition with circle hough transform (CHT) is applied to the ball-wheeled robot. The front vision of the robot uses HSV colour filtering with various test values to determine the thresholding value, and it was found that the ball could be identified up to a distance of 1,000 cm. To further improve the performance of recognizing the ball object, CHT was applied. It was found that the ball could be identified up to a distance of 700 cm. Furthermore, the ball can be identified in obstructed conditions up to 75%.

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Rapid and non-destructive identification of Panax ginseng origins using hyperspectral imaging, visible light imaging, and X-ray imaging combined with multi-source data fusion strategies

Ping,

Ying,

Hao

et al. 2024

Food Research International

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Zanthoxylum bungeanum Fruit Detection by Adaptive Thresholds in HSV Space for an Automatic Picking System

Cited by 6 publications

References 18 publications

Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Implementing hue-saturation-value filter and circle hough transform for object tracking on ball-wheeled robot

Rapid and non-destructive identification of Panax ginseng origins using hyperspectral imaging, visible light imaging, and X-ray imaging combined with multi-source data fusion strategies

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