Corn Kernel Breakage Classification by Machine Vision Using a Neural Network Classifier

Liao, Kai‐Sheng; Paulsen, Marvin R; Reid, John F.; Ni, Bin; Bonifacio-Maghirang, E.P.

doi:10.13031/2013.28547

Cited by 54 publications

(28 citation statements)

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“…Early applications include modeling sensory color quality of tomato and peach [99], investigation of combined effects of CO 2 and sucrose on the growth of alfalfa cuttings using a Kalman filter neural network [100], classification of apple surface features [101], corn kernel breakage classification [102], and machine vision inspection of potatoes [103].…”

Section: Ann Applications In Agricultural and Biological Engineeringmentioning

confidence: 99%

Advances in Artificial Neural Networks – Methodological Development and Application

Huang

2009

Algorithms

162

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Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological engineering.

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Section: Ann Applications In Agricultural and Biological Engineeringmentioning

confidence: 99%

Advances in Artificial Neural Networks – Methodological Development and Application

Huang

2009

Algorithms

162

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“…The second application ranges from the simplest (sorting by size using sieving methods) to the most challenging problems, such as the detection of recent bruises on apples (Kleynen et al, 2005). Other examples of possible applications include the sorting of corn kernels (Liao et al, 1993), rice kernels (Wan, 2002), wheat kernels (Luo et al, 1999), soybeans (Casady et al, 1992;Wang et al, 2002) and ore (Cutmore et al, 1998). Recently, techniques such as image processing are used to extract more relevant information from images of the objects.…”

Section: Objectivesmentioning

confidence: 99%

A sorting optimization curve with quality and yield requirements

Ooms

Palm

Leemans

et al. 2010

Pattern Recognition Letters

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Binary classifiers used for sorting can be compared and optimized using receiver-operating characteristic (ROC) curves which describe the trade-off between the false positive rate and true positive rate of the classifiers. This approach is well suited for the diagnosis of human diseases where individual costs of misclassification are of great concern. While it can be applied to the sorting of merchandise or other materials, the variables described by the ROC curve and its existing alternatives are less relevant for that range of applications and another approach is needed. In this paper, quality and yield factors are introduced into a sorting optimization curve (SOC) for the choice of the operating point of the classifier, associated with the prediction of output quantity and quality. Given examples are the sorting of seeds and apples with specific requirements. In both cases the operating point of the classifier is easily chosen on the SOC, while the output characteristics of the sorted product are accurately predicted.

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“…This method can effectively identify the broken maize kernels from the overall. Liao K [6] used plurality parameters to describe the image feature of the maize kernels and combined computer vision technology and neural network algorithm to distinguish the integrity maize kernels and the broken maize kernels.…”

Section: Introductionmentioning

confidence: 99%

Detection of maize kernels breakage rate based on K-means clustering

Yang¹,

Wang

Gao

et al. 2017

AIP Conference Proceedings

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yangliangsia@sina.cnAbstract. In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively.

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Corn Kernel Breakage Classification by Machine Vision Using a Neural Network Classifier

Cited by 54 publications

References 0 publications

Advances in Artificial Neural Networks – Methodological Development and Application

Advances in Artificial Neural Networks – Methodological Development and Application

A sorting optimization curve with quality and yield requirements

Detection of maize kernels breakage rate based on K-means clustering

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