A method for accurately segmenting images of medicinal plant leaves with complex backgrounds

Gao, Liwen; Lin, Xiaohua

doi:10.1016/j.compag.2018.10.020

Cited by 29 publications

(13 citation statements)

References 33 publications

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“…PlantCLEF act like a real-life computerised program that can identify and classify plant species using raw images by extracting similar traits/characteristics and matching them defined plant species and family [70]. Similarly, MPID (Medicinal plant images database) which is a premium database maintained by Hong Kong Baptist University that is known to accommodate vast range of phenotypic data related to medicinal and therapeutically important plants [71]. Furthermore, in addition to phenotypic data, it also acts as a repertoire of scientific/botanical names, therapeutic values, physiological and ecological parameters of more than 1000 medicinal plants.…”

Section: Phenomics Based Imaging and Analytical Toolkitsmentioning

confidence: 99%

“…Apart from databases, several computer-based analytical tools and techniques have also been developed and implemented for recording high-resolution images and morpho-physiological parameters in selected plants [70]. Plant computer vision (PlantCV) is a freeware software package written explicitly in python language that provide valuable algorithms for analysing phenotypic data [71]. It can analyse phenotypic data for multiple plant species and compare them with in the database for identification of novel traits/characteristics in genetically un-explored crops [67].…”

Section: Phenomics Based Imaging and Analytical Toolkitsmentioning

confidence: 99%

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Unlocking Pharmacological and Therapeutic Potential of Hyacinth Bean (Lablab purpureus L.): Role of OMICS Based Biology, Biotic and Abiotic Elicitors

Kumar¹,

Rai²,

Pandey-Rai³

2022

Legumes Research - Volume 2

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Hyacinth bean also known as Indian bean is multipurpose legume crops consumed both as food by humans and as forage by animals. Being a rich source of protein, it also produces distinct secondary metabolites such as flavonoids, phenols and tyrosinase which not only help strengthened plant’s own innate immunity against abiotic/biotrophic attackers but also play important therapeutic role in the treatment of various chronic diseases. However, despite its immense therapeutic and nutritional attributes in strengthening food, nutrition and therapeutic security in many developing countries, it is still considered as an “orphan crop” for unravelling its genetic potential and underlying molecular mechanisms for enhancing secondary metabolite production. Several lines of literatures have well documented the use of OMICS based techniques and biotic and abiotic elicitors for stimulating secondary metabolite production particularly in model as well as in few economically important crops. However, only limited reports have described their application for stimulating secondary metabolite production in underutilised crops. Therefore, the present chapter will decipher different dimensions of multi-omics tools and their integration with other conventional techniques (biotic and abiotic elicitors) for unlocking hidden genetic potential of hyacinth bean for elevating the production of secondary metabolites having pharmaceutical and therapeutic application. Additionally, the study will also provide valuable insights about how these advance OMICS tools can be successfully exploited for accelerating functional genomics and breeding research for unravelling their hidden pharmaceutical and therapeutic potential thereby ensuring food and therapeutic security for the betterment of mankind.

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Section: Phenomics Based Imaging and Analytical Toolkitsmentioning

confidence: 99%

Section: Phenomics Based Imaging and Analytical Toolkitsmentioning

confidence: 99%

Unlocking Pharmacological and Therapeutic Potential of Hyacinth Bean (Lablab purpureus L.): Role of OMICS Based Biology, Biotic and Abiotic Elicitors

Kumar¹,

Rai²,

Pandey-Rai³

2022

Legumes Research - Volume 2

View full text Add to dashboard Cite

show abstract

“…To test the performance of the algorithm to the greatest extent, the test set consisted of the apple surface images under three conditions: varying degrees of shadow, light, and both conditions simultaneously. To quantitatively evaluate the effectiveness of segmentation by the proposed algorithm, the test results were evaluated in terms of the recall rate, precision rate, F-measure index, false positive rate (FPR), and false negative rate (FNR) [41]. These metrics are calculated as follows.…”

Section: Experimental and Analysismentioning

confidence: 99%

Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition

et al. 2021

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In the vision system of apple-picking robots, the main challenge is to rapidly and accurately identify the apple targets with varying halation and shadows on their surfaces. To solve this problem, this study proposes a novel, multi-feature, patch-based apple image segmentation technique using the gray-centered red–green–blue (RGB) color space. The developed method presents a multi-feature selection process, which eliminates the effect of halation and shadows in apple images. By exploring all the features of the image, including halation and shadows, in the gray-centered RGB color space, the proposed algorithm, which is a generalization of K-means clustering algorithm, provides an efficient target segmentation result. The proposed method is tested on 240 apple images. It offered an average accuracy rate of 98.79%, a recall rate of 99.91%, an F1 measure of 99.35%, a false positive rate of 0.04%, and a false negative rate of 1.18%. Compared with the classical segmentation methods and conventional clustering algorithms, as well as the popular deep-learning segmentation algorithms, the proposed method can perform with high efficiency and accuracy to guide robotic harvesting.

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“…Breast cancer detection procedures can be divided into a few stages, namely region of interest extraction, identification of suspicious tissues and tissue classification [42]. A good edge detection algorithm is vital in the region of interest extraction stage which can result in good identification and segmentation [5] [1].…”

Section: Edge Detection On Mammogramsmentioning

confidence: 99%

“…In the application of image processing, machine vision, and computer vision, edge detection is one of the crucial steps in pre-processing stages for finding the boundaries of objects within an image, for instance detecting local discontinuities in pixels intensity or brightness for boundaries extraction [1]. Edge detection is widely implemented in the application of car's license plate detection [2], human face recognition through iris localization for eye tracking [3], synthetic aperture radar images to detect edges of ships, aircraft, terrain, meteorological forms and mobile vehicles [4], agricultural plant leaves recognition [5], and dehaze or deblurring method [6]. Furthermore, biomedical image, i.e.…”

Section: Introductionmentioning

confidence: 99%

Mini Kirsch Edge Detection and Its Sharpening Effect

Sia¹,

Swee²,

Yahya³

et al. 2021

IJEEI

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In computer vision, edge detection is a crucial step in identifying the objects' boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true nonedge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy.

show abstract

A method for accurately segmenting images of medicinal plant leaves with complex backgrounds

Cited by 29 publications

References 33 publications

Unlocking Pharmacological and Therapeutic Potential of Hyacinth Bean (Lablab purpureus L.): Role of OMICS Based Biology, Biotic and Abiotic Elicitors

Unlocking Pharmacological and Therapeutic Potential of Hyacinth Bean (Lablab purpureus L.): Role of OMICS Based Biology, Biotic and Abiotic Elicitors

Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition

Mini Kirsch Edge Detection and Its Sharpening Effect

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