A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier

Kumar, S. Dhakshina; Esakkirajan, S.; Bama, S. Sathya; Keerthiveena, B.

doi:10.1016/j.micpro.2020.103090

Cited by 89 publications

(31 citation statements)

References 31 publications

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“…1 A support vector machine (SVM) is a machine learning algorithm in accordance to mathematical statistics theory. SVMs have been widely used in various industries because of their ability to solve problems such as high dimensionality and small sample sizes, nonlinearity and local minima (Dhakshina et al 2020). In fact, the main feature of the support vector machine is that it uses a subset of the training set to represent a decision boundary, called a support vector, while the decision boundary is called the maximum edge hyperplane.…”

Section: Discussionmentioning

confidence: 99%

Predicting Tunnel Squeezing Using the SVM-BP Combination Model

et al. 2021

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Rock squeezing has a large influence on tunnel construction safety; thus, when designing and constructing tunnels it is highly important to use a reliable method for predicting tunnel squeezing from incomplete data. In this study, a combination SVM-BP (support vector machine-back-propagation) model is proposed to classify the deformation caused by surrounding rock squeezing. We designed different characteristic parameters and three types of classifiers (an SVM model, a BP model, and the proposed SVM-BP model) for the tunnel-squeezing prediction experiments and analysed the accuracy of predictions by different models and the influences of characteristic parameters on the prediction results. In contrast to other prediction methods, the proposed SVM-BP model is verified to be reliable.The results show that four characteristics: tunnel diameter (D), tunnel buried depth (H), rock quality index (Q) and support stiffness (K) reflect the effect of rock squeezing sufficiently for classification.The SVM-BP model combines the advantages of both an SVM and a BP neural network. It possesses flexible nonlinear modelling ability and the ability to perform parallel processing of large-scale information. Therefore, the SVM-BP model achieves better classification performance than do the 2 SVM or BP models separately. Moreover, coupling D, H, and K has a significant impact on the predicted results of tunnel squeezing.

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Section: Discussionmentioning

confidence: 99%

Predicting Tunnel Squeezing Using the SVM-BP Combination Model

et al. 2021

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“…A multi-class support vector machine (MSVM) was used to grade the apples to ensure high accuracy [17]. The MSVM is a specific application of the SVM that assigns one of many class labels to the input [22].…”

Section: Apple Grading Methods Based On Feature Fusionmentioning

confidence: 99%

“…Machine vision has achieved good results for grading agricultural products, such as apples, tomatoes, potatoes, and mangoes [17][18][19][20][21][22][23][24][25]. Pourdarbani et al developed a jujube sorting system based on machine vision.…”

Section: Introductionmentioning

confidence: 99%

Infield Apple Detection and Grading Based on Multi-Feature Fusion

Zhang

Zhou

et al. 2021

Horticulturae

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A field-based apple detection and grading device was developed and used to detect and grade apples in the field using a deep learning framework. Four features were selected for apple grading, namely, size, color, shape, and surface defects, and detection algorithms were designed to discriminate between the four features using machine vision and other methods. Then, the four apple features were fused, and a support vector machine (SVM) was used for infield apple grading into three grades: first-grade fruit, second-grade fruit, and other-grade fruit. The results showed that for a single index, the accuracy of detecting the apple size, the fruit shape, the color, and the surface defects, were 99.04%, 97.71%, 98%, and 95.85%. The grading accuracies for the first-grade fruit, second-grade fruit, other-grade fruit, and the average grading accuracy based on multiple features were 94.55%, 95.71%, 100%, and 95.49%, respectively. The field experiment showed that the average grading accuracy was 94.12% when the feeding interval of the apples was less than 1.5 s and the walking speed did not exceed 0.5 m/s, meeting the accuracy requirements of field-based apple grading.

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“…Both kinds of features are used as an input for a SVM and Sparse Representation Classifier (SRC) for the classification of defects in fruit. In another work, a combination of 18 colour and texture features has been used for grading tomatoes, where SVM has been used as a classifier [35].…”

Section: Quality Gradingmentioning

confidence: 99%

A Sample Weight and AdaBoost CNN-Based Coarse to Fine Classification of Fruit and Vegetables at a Supermarket Self-Checkout

2020

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The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable lighting conditions and human factors arising from customer interactions with the system along with the challenges associated with the colour, texture, shape, and size of a fruit or vegetable. Considering this complex application, we have proposed a progressive coarse to fine classification technique to classify fruit and vegetables at supermarket checkouts. The image and weight of fruit and vegetables have been obtained using a prototype designed to simulate the supermarket environment, including the lighting conditions. The weight information is used to change the coarse classification of 15 classes down to three, which are further used in AdaBoost-based Convolutional Neural Network (CNN) optimisation for fine classification. The training samples for each coarse class are weighted based on AdaBoost optimisation, which are updated on each iteration of a training phase. Multi-class likelihood distribution obtained by the fine classification stage is used to estimate a final classification with a softmax classifier. GoogleNet, MobileNet, and a custom CNN have been used for AdaBoost optimisation, with promising classification results.

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A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier

Cited by 89 publications

References 31 publications

Predicting Tunnel Squeezing Using the SVM-BP Combination Model

Predicting Tunnel Squeezing Using the SVM-BP Combination Model

Infield Apple Detection and Grading Based on Multi-Feature Fusion

A Sample Weight and AdaBoost CNN-Based Coarse to Fine Classification of Fruit and Vegetables at a Supermarket Self-Checkout

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