Plant Identification From Leaves Using Quasi-Sensor Fusion

Shiraishi, Masashi; Sumiya, Hideyasu

doi:10.1115/1.2831041

Cited by 12 publications

(4 citation statements)

References 3 publications

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“…These may be broadly categorized as approaches that encode the leaf perimeter and margin and those that use other shape and index-based measures. The boundary transformations include Fourier analysis (Kincaid and Schneider 1983), curve fitting (Franz et al 1991b), and a technique using the radial distance from the geometric center of the leaf (Shiraishi and Sumiya 1996). A common challenge with any of the leaf-boundary approaches is their sensitivity to occlusion (parts of leaf edge being hidden).…”

Section: Identification From Shape Featuresmentioning

confidence: 99%

Site-specific weed management: sensing requirements— what do we need to see?

Brown¹,

Noble

2005

Weed Science

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Automated detection and identification of weeds in crop fields is the greatest obstacle to development of practical site-specific weed management systems. Research progress is summarized for two different approaches to the problem, remote sensing weed mapping and ground-based detection using digital cameras or nonimaging sensors. The general spectral and spatial limitations reported for each type of weed identification system are reviewed. Airborne remote sensing has been successful for detection of distinct weed patches when the patches are dense and uniform and have unique spectral characteristics. Identification of weeds is hampered by spectral mixing in the relatively large pixels (typically larger than 1 by 1 m) and will not be possible from imagery where weed seedlings are sparsely distributed among crop plants. The use of multispectral imaging sensors such as color digital cameras on a ground-based mobile platform shows more promise for weed identification in field crops. Spectral features plus spatial features such as leaf shape and texture and plant organization may be extracted from these images. However, there is a need for research in areas such as artificial lighting, spectral band requirements, image processing, multiple spatial resolution systems, and multiperspective images.

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Section: Identification From Shape Featuresmentioning

confidence: 99%

Site-specific weed management: sensing requirements— what do we need to see?

Brown¹,

Noble

2005

Weed Science

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“…Weed detection is undertaken using either common (RGB) cameras or camera systems utilizing both near‐infrared (NIR) and color information. The use of color cameras for the detection processing is common due to their low cost and availability; however, detection using NIR information takes advantage of the way that plant leaves greatly reflect NIR light …”

Section: State Of the Artmentioning

confidence: 99%

Robot for weed species plant‐specific management

Bawden

Kulk

Russell

et al. 2017

Journal of Field Robotics

153

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The rapid evolution of herbicide-resistant weed species has revitalized research in nonchemical methods for weed destruction. Robots with vision-based capabilities for online weed detection and classification are a key enabling factor for the specialized treatment of individual weed species. This paper describes the design, development, and testing of a modular robotic platform with a heterogeneous weeding array for agriculture. Starting from requirements derived from farmer insights, technical specifications are put forward. A design of a robotic platform is conducted based on the required technical specifications, and a prototype is manufactured and tested. The second part of the paper focuses on the weeding mechanism attached to the robotic platform. This includes aspects of vision for weed detection and classification, as well as the design of a weeding array that combines chemical and mechanical methods for weed destruction. Field trials of the weed detection and classification system show an accuracy of 92.3% across a range of weed species, while the heterogeneous weed management system is able to selectively apply a mechanical or chemical control method based on the species of weed. Together, the robotic platform and weeding array demonstrate the potential for robotic plant-species-specific weed management enabled by the vision-based online detection and classification algorithms.

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“…For example, Simonton has developed methods to identify locations on a stem where a gripper can be applied to pick up a plant cutting [9]. Other examples of segmentation techniques can be found in [10] and [11]. Our earlier research developed a segmentation method that uses "erosion" and "dilation" of images based on knowledge of the size of certain plant features…”

Section: Relationship To Other Workmentioning

confidence: 99%

Grading of vegetative cuttings using computer vision

Singh

Montemerlo

1997

Advanced Robotics

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This paper reports on recent progress in the development of system to group populations of vegetative cuttings. The system is required to assign a classification to cuttings such that they appear uniform after a growing period using single two-dimensional monochrome images. We have developed a fast segmentation technique that is able to measure plant features and a supervised learning scheme that learns a mapping from the features to a scalar classification. We report results based on segmentation of over 2000 geranium cuttings. The system is able to process images at 2 Hz and has an accuracy of over 90%. Both metrics exceed human performance.

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Plant Identification From Leaves Using Quasi-Sensor Fusion

Cited by 12 publications

References 3 publications

Site-specific weed management: sensing requirements— what do we need to see?

Site-specific weed management: sensing requirements— what do we need to see?

Robot for weed species plant‐specific management

Grading of vegetative cuttings using computer vision

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