Improving plant discrimination in image processing by use of different colour space transformations

Philipp, Isabelle; Rath, Thomas

doi:10.1016/s0168-1699(02)00050-9

Cited by 103 publications

(56 citation statements)

References 11 publications

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“…One example of the illuminant invariant image is shown in Figure 5(b), which reduced the effect of shadow compared with the original color image Figure 5(a). In the research of Philipp & Rath, (2002), the author also found the HSV (Hue-Saturation-Value) color space to be one of the most reliable color space to distinguish green plants from the background. The hue values (H channel) of plants don't change significantly with different light intensity.…”

Section: Color Based Segmentationmentioning

confidence: 99%

Plant Localization and Discrimination using 2D+3D Computer Vision for Robotic Intra-row Weed Control

Gai¹,

Tang²,

Steward³

2016

2016 ASABE International Meeting

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Weed management is vitally important in crop production systems. However, conventional herbicide based weed control can lead to negative environmental impacts. Manual weed control is laborious and impractical for large scale production. Robotic weed control offers a possibility of controlling weeds precisely, particularly for weeds growing near or within crop rows. A computer vision system was developed based on Kinect V2 sensor, using the fusion of two-dimensional textural data and three-dimensional spatial data to recognize and localized crop plants different growth stages. Images were acquired of different plant species such as broccoli, lettuce and corn at different growth stages. A database system was developed to organize these images. Several feature extraction algorithms were developed which addressed the problems of canopy occlusion and damaged leaves. With our proposed algorithms, different features were extracted and used to train plant and background classifiers. Finally, the efficiency and accuracy of the proposed classification methods were demonstrated and validated by experiments.

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Section: Color Based Segmentationmentioning

confidence: 99%

Plant Localization and Discrimination using 2D+3D Computer Vision for Robotic Intra-row Weed Control

Gai¹,

Tang²,

Steward³

2016

2016 ASABE International Meeting

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“…This motivated us to investigate the behavior of ship wakes in different color spaces. Conversion of RGB color space to others such as HSV, YCbCr or L*a*b is often used to separate foreground-background or target-clutter in color images (Philipp and Rath, 2002). In some studies color spaces are used in specific applications.…”

Section: Introductionmentioning

confidence: 99%

A Prealiminary Study of Ship Detection From Uav Images Based on Color Space Conversion and Image Segmentation

Klimkowska

Lee

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/VbKEY WORDS: Ship Detection, Remote Sensing Images, Edge Detection, Colour Space, Marine Surveillance, UAV ABSTRACT:Ship detection is an inherent process supporting tasks such as fishery management, ship search, marine traffic monitoring and control, and helps in the prevention of illegal activities. So far, sea and shore monitoring has been carried out by ship patrols and aircrafts along with sea vessel detection from data from space-borne platforms. Recently an increase interest in applying images delivered by UAV for marine application due to their advantages such as high spatial resolution, independence on time acquisition can be noticed. While investigating state of the art methods used for ship detection from different platforms using optical images, we found a significant problem with occurrence of a ship wake. This phenomena may prohibit correct detection of ship location and results in overestimating the ship size as the ship and its wake are often considered as being part of the same object in image or wakes are distinguished as a separate ship due to their possible similar brightness compared with sea vessel. In order to reduce the impact of ship wakes we investigated the behavior of images in different color spaces to provide data with little or almost no trace of ship wake. We took into consideration following color spaces: HSV, YCbCr, NTSC, XYZ and L*a*b and investigated each channel from new images. Finally we decided to use 2nd channel of L*a*b space where the ship wakes occurrence were significantly reduced. Object of interest were detected through the use of image segmentation. Applied method uses edge detection based on the gradient magnitude calculation. Afterwards several characteristics such as centroids, major and minor axis, size and orientation were calculated for later use to remove false positives and thus improve accuracy of the proposed method.

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“…Blazquez (1989) analyzed the degree of health of peach trees using IR photo analysis and obtained results that were very similar to the health grade judgments of experts. Recently, methods have been studied that use information from the visible light range for weed recognition (Sogaard, 2005;Philipp and Rath, 2002) or methods that use color differences in the visible light range to determine whether diseases have occurred Camargo and Smith, 2009). These have led to studies to measure the growth rate of citrus trees using multispectral images (Fevaerts, 2001) or laser scanners and ultrasonic sensors in combination (Tumboet et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Detection Method for Bean Cotyledon Locations under Vinyl Mulch Using Multiple Infrared Sensors

Lee¹,

Cho²,

Lee³

2016

Journal of Biosystems Engineering

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Purpose: Pulse crop damage due to wild birds is a serious problem, to the extent that the rate of damage during the period of time between seeding and the stage of cotyledon reaches 45.4% on average. This study investigated a method of fundamentally blocking birds from eating crops by conducting vinyl mulching after seeding and identifying the growing locations for beans to perform punching. Methods: Infrared (IR) sensors that could measure the temperature without contact were used to recognize the locations of soybean cotyledons below vinyl mulch. To expand the measurable range, 10 IR sensors were arranged in a linear array. A sliding mechanical device was used to reconstruct the two-dimensional spatial variance information of targets. Spatial interpolation was applied to the two-dimensional temperature distribution information measured in real time to improve the resolution of the bean coleoptile locations. The temperature distributions above the vinyl mulch for five species of soybeans over a period of six days from the appearance of the cotyledon stage were analyzed. Results: During the experimental period, cases where bean cotyledons did and did not come into contact with the bottom of the vinyl mulch were both observed, and depended on the degree of growth of the bean cotyledons. Although the locations of bean cotyledons could be estimated through temperature distribution analyses in cases where they came into contact with the bottom of the vinyl mulch, this estimation showed somewhat large errors according to the time that had passed after the cotyledon stage. The detection results were similar for similar types of crops. Thus, this method could be applied to crops with similar growth patterns. According to the results of 360 experiments that were conducted (five species of bean × six days × four speed levels × three repetitions), the location detection performance had an accuracy of 36.9%, and the range of location errors was 0-4.9 cm (RMSE = 3.1 cm). During a period of 3-5 days after the cotyledon stage, the location detection performance had an accuracy of 59% (RMSE = 3.9 cm). Conclusions: In the present study, to fundamentally solve the problem of damage to beans from birds in the early stage after seeding, a working method was proposed in which punching is carried out after seeding, thereby breaking away from the existing method in which seeding is carried out after punching. Methods for the accurate detection of soybean growing locations were studied to allow punching to promote the continuous growth of soybeans that had reached the cotyledon stage. Through experiments using multiple IR sensors and a sliding mechanical device, it was found that the locations of the crop could be partially identified 3-5 days after reaching the cotyledon stage regardless of the kind of pulse crop. It can be concluded that additional studies of robust detection methods considering environmental factors and factors for crop growth are necessary.

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Improving plant discrimination in image processing by use of different colour space transformations

Cited by 103 publications

References 11 publications

Plant Localization and Discrimination using 2D+3D Computer Vision for Robotic Intra-row Weed Control

Plant Localization and Discrimination using 2D+3D Computer Vision for Robotic Intra-row Weed Control

A Prealiminary Study of Ship Detection From Uav Images Based on Color Space Conversion and Image Segmentation

Detection Method for Bean Cotyledon Locations under Vinyl Mulch Using Multiple Infrared Sensors

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