A nearest neighbor approach for fruit recognition in RGB-D images based on detection of convex surfaces

Nyarko, Emmanuel Karlo; Vidović, Ivan; Radočaj, Kristijan; Cupec, Robert

doi:10.1016/j.eswa.2018.07.048

Cited by 49 publications

(20 citation statements)

References 37 publications

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“…Some research focuses on simple image analysis frameworks by detecting geometric objects in images. Nyarko et al [16] detect fruits in uncontrolled conditions by detecting convex surfaces. The surfaces are classified with a k-nearest neighbour approach into 'fruit' and 'not fruit'.…”

Section: Related Workmentioning

confidence: 99%

Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks

Zabawa

Kicherer

Klingbeil

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Yield estimation and forecasting are of special interest in the field of grapevine breeding and viticulture. The number of harvested berries per plant is strongly correlated with the resulting quality. Therefore, early yield forecasting can enable a focused thinning of berries to ensure a high quality end product. Traditionally yield estimation is done by extrapolating from a small sample size and by utilizing historic data. Moreover, it needs to be carried out by skilled experts with much experience in this field. Berry detection in images offers a cheap, fast and non-invasive alternative to the otherwise time-consuming and subjective on-site analysis by experts. We apply fully convolutional neural networks on images acquired with the Phenoliner, a field phenotyping platform. We count single berries in images to avoid the error-prone detection of grapevine clusters. Clusters are often overlapping and can vary a lot in the size which makes the reliable detection of them difficult. We address especially the detection of white grapes directly in the vineyard. The detection of single berries is formulated as a classification task with three classes, namely 'berry', 'edge' and 'background'. A connected component algorithm is applied to determine the number of berries in one image. We compare the automatically counted number of berries with the manually detected berries in 60 images showing Riesling plants in vertical shoot positioned trellis (VSP) and semi minimal pruned hedges (SMPH). We are able to detect berries correctly within the VSP system with an accuracy of 94.0 % and for the SMPH system with 85.6 %.

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Section: Related Workmentioning

confidence: 99%

Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks

Zabawa

Kicherer

Klingbeil

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…This measure is comprised of segment size, shape, position, and neighborhood similarity. The proposed similarity measures are based on the convex template instance (CTI) descriptor, proposed in [3], which describes object segments by approximating their shape with convex polyhedrons. A neighborhood similarity is defined by a novel descriptor, named the topological relation descriptor (TRED), which describes the topological relations between two segments in the model, which is also based on the CTI descriptor.…”

Section: The Problem and The Contributionsmentioning

confidence: 99%

“…The computation of the SCLM is based on the CTI descriptor proposed in [3]. In this subsection, a brief description of this descriptor is provided.…”

Section: Cti Descriptormentioning

confidence: 99%

“…The approach proposed in this paper requires that for each unit vector a m ∈ A, there exist its opposite vector a m ∈ A. The CTI descriptor was originally designed for fruit recognition [3]. Later, it was applied for the alignment of similar shapes with the purpose of object classification on depth images [18].…”

Section: Cti Descriptormentioning

confidence: 99%

“…Every segment was represented by a CTI descriptor. Analogously to [3,19], we used a convex template consisting of n d = 66 unit vectors uniformly distributed on the unit sphere.…”

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confidence: 99%

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Semantic Component Association within Object Classes Based on Convex Polyhedrons

2020

Self Cite

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Most objects are composed of semantically distinctive parts that are more or less geometrically distinctive as well. Points on the object relevant for a certain robot operation are usually determined by various physical properties of the object, such as its dimensions or weight distribution, and by the purpose of object parts. A robot operation defined for a particular part of a representative object can be transferred and adapted to other instances of the same object class by detecting the corresponding components. In this paper, a method for semantic association of the object’s components within the object class is proposed. It is suitable for real-time robotic tasks and requires only a few previously annotated representative models. The proposed approach is based on the component association graph and a novel descriptor that describes the geometrical arrangement of the components. The method is experimentally evaluated on a challenging benchmark dataset.

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A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture

Yang

Wang

et al. 2020

J Sci Food Agric

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BACKGROUND Rice blast fungus is a worldwide disease, and it is one of the most serious rice diseases in the north and south rice fields in China. The initial symptoms of rice blast are not obvious, and the speed of transmission is fast. Manual identification is time‐consuming and laborious. At present, it is a great challenge to realize rapid and accurate early identification of rice blast. RESULTS In this paper, an identification method based on crop disease spores' diffraction fingerprint texture for rice blast was studied; this method utilizes the light field and texture features of diffraction images. To verify the reliability of the model that we proposed, we selected two methods of manual identification and machine recognition to compare and detect rice blast spores. The experimental results show that the identification of light diffraction characteristics is not only higher than the traditional manual recognition by microscope (increased by more than 0.3%), but also faster after neural network training (increased by more than 90%). The diffraction recognition method used in this study, based on crop disease spores' diffraction fingerprint texture, can be completed in a few seconds, and its test accuracy is 97.18%. CONCLUSION The proposed method, a rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture, has certain advantages compared with the existing manual identification by microscope. This method can be applied to the recognition of rice blast in agricultural research. © 2020 Society of Chemical Industry

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A nearest neighbor approach for fruit recognition in RGB-D images based on detection of convex surfaces

Cited by 49 publications

References 37 publications

Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks

Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks

Semantic Component Association within Object Classes Based on Convex Polyhedrons

A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture

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