Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques

2023

Sensors

Self Cite

Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.

Section: Methodsmentioning

confidence: 99%

Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Feyzioğlu

2023

Sensors

Self Cite

“…A complexity matrix is a tool used to measure the performance of an algorithm or system design. The matrix contains different combinations of performance metrics such as the algorithm's processing time or processor usage [26]. Usually, the complexity matrix is in the form of a two-dimensional table.…”

Section: Performance Metrics and Confusion Matrixmentioning

confidence: 99%

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Feyzioğlu

International Journal of Applied Mathematics Electronics and Computers

2023

Self Cite

Iron metal is the most widely used metal type. This metal, which is used in countless sectors, is processed in different ways and turned into steel. Since steel has a brittle structure compared to iron, defects may occur in the plates during the rolling process. Detection of these defects at the production stage is of great importance in terms of commercial and safety. Machine learning methods can be used in such problems for fast and high accuracy detection. For this purpose, using a dataset obtained from stainless steel surface defects in this study, classification processes were carried out to detect defects with four different machine learning methods. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for classification processes. The highest classification accuracy was obtained from the 79.44% RF model. Correlation analysis was performed in order to analyze the effects of the features in the dataset on the classification results. It is thought that the classification accuracy of the proposed models is satisfactory for this challenging problem, but needs to be upgraded.

“…Pre-trained CNN models are trained with a large number of images. The information obtained as a result of the training can be stored in the network and used in the classification of new images (Taspinar et al, 2022). The advantage of transfer learning is that high accuracy can be achieved using less training data.…”

Section: 3transfer Learning and Alexnet Inceptionv3 Vgg16 Pre-trained...mentioning

confidence: 99%

Classification and Analysis of Tomato Species with Convolutional Neural Networks

2022

SJAFS

Tomatoes are one of the most used vegetables. There are varieties that can grow in different climates. The taste, usage area and commercial value of each are different from each other. For this reason, identifying and sorting tomato species after the production stage is a problem. In addition, since tomato is a sensitive vegetable, it is extremely important to separate it from a distance. For this purpose, the classification of tomato images belonging to 9 different tomato species was carried out in the study. In total, a dataset containing 6810 tomato images in 9 classes was used. Three different pre-trained Convolutional Neural Network (CNN) models were used with the transfer learning method to classify the images. AlexNet, InceptionV3 and VGG16 models were used for classification. As a result of the classifications made, the highest classification belongs to the AlexNet model with 100%. Evaluation of the performances of the models was also made with precision, recall, F1 Score and specificity performance metrics. It is foreseen that the proposed methods can be used for the separation of tomatoes.