A real time quality control application for animal production by image processing

Sungur, Cemil; Özkan, Halil

doi:10.1002/jsfa.7025

Cited by 4 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The researcher stated that some of the algorithms he developed have been successful in determining the fruit at the level of 85%. Sungur and Özkan (2015) made a quality control application using MATLAB software to detect pollution in chicken eggs and calculate egg volume. The researcher used the fuzzy logic algorithm to determine the degree of quality.…”

Section: Introductionmentioning

confidence: 99%

Classification of Some Fruits using Image Processing and Machine Learning

Gerdan

Vatandaş

2021

Turkish JAF Sci.Tech.

View full text Add to dashboard Cite

In this study, an image processing algorithm and classification unit were developed to classify the fruits according to their size and color characteristics. For this purpose, a total of 300 fruits (50 fruit samples from each of the Starkrimson Delicious and Golden Delicious apple varieties, Washington Navel and Valencia Midknight orange varieties, Ekmek and Eşme quince varieties) were used in the experiments. The size and color values measured with a caliper and a spectrophotometer were entered in the developed image processing algorithm to determine the success rates of classifying the fruits. The integration of image processing algorithm with the classification unit classified 88%, 100%, 96%, 82%, 86%, respectively. On the other hand, the size and color values read in fruits with the image processing algorithm were evaluated using predictive techniques used in data mining. For this purpose, K Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes classification and Multilayer Perceptron Neural Network (MLP) algorithms were used. Algorithms were run with 10-fold cross validation method. In the training of artificial classifiers, the success was 93.6% for KNN, 90.3% for DT, 88.3% for Naive Bayes, 92.6% for MLP and 94.3% for RF.

show abstract

Section: Introductionmentioning

confidence: 99%

Classification of Some Fruits using Image Processing and Machine Learning

Gerdan

Vatandaş

2021

Turkish JAF Sci.Tech.

View full text Add to dashboard Cite

show abstract

“…In the literature, image processing techniques (IPT) are widely used for the classification of agricultural products. In addition, image processing techniques and artificial intelligence techniques (AITs) are used in combination to increase classification accuracy [1][2]. Neural networks such as artificial neural network (ANN), support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS) and decision tree (DT), K-nearest neighbors (KNN), Naive Bayes (NB) and discriminant analysis (DA) are the most utilized with IPT for classifying agricultural products [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains

Kayabaşı

2018

ijisae

View full text Add to dashboard Cite

Artificial Neural Networks (ANNs) have emerged as an important tool for classification problem. This paper presents an application of ANN model trained by artificial bee colony (ABC) optimization algorithm for classification the wheat grains into bread and durum. ABC algorithm is used to optimize the weights and biases of three-layer multilayer perceptron (MLP) based ANN. The classification is carried out through data of wheat grains (#200) acquired using image-processing techniques (IPTs). The data set includes five grain's geometric parameters: length, width, area, perimeter and fullness. The ANN-ABC model input with the geometric parameters are trained through 170 wheat grain data and their accuracies are tested via 30 data. The ANN-ABC model numerically calculate the outputs with mean absolute error (MAE) of 0.0034 and classify the grains with accuracy of 100% for the testing process. The results of ANN-ABC model are compared with other ANN models trained by 4 different learning algorithms. These results point out that the ANN trained by ABC optimization algorithm can be successfully applied to classification of wheat grains.

show abstract

“…Computer‐aided systems are being developed for evaluating the quality of agricultural products. Systems based on computer vision employ the visual attributes of grains or products obtained from image‐processing techniques (IPTs) . Artificial intelligence (AI) can be integrated with computer vision so as to provide automatic quality assessment.…”

Section: Introductionmentioning

confidence: 99%

“…Systems based on computer vision 1 employ the visual attributes of grains or products obtained from image-processing techniques (IPTs). 2 Artificial intelligence (AI) can be integrated with computer vision so as to provide automatic quality assessment. Thus a rapid and unmanned system with high accuracy can be developed for the classification of grains.…”

Section: Introductionmentioning

confidence: 99%

Computer vision‐based method for classification of wheat grains using artificial neural network

2016

View full text Add to dashboard Cite

show abstract

A real time quality control application for animal production by image processing

Abstract: According to tests carried out on thousands of eggs, a quality control process with an accuracy of 98% was possible.

Cited by 4 publications

References 11 publications

Classification of Some Fruits using Image Processing and Machine Learning

Classification of Some Fruits using Image Processing and Machine Learning

An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains

Computer vision‐based method for classification of wheat grains using artificial neural network

Contact Info

Product

Resources

About