hyperparameter optimization of ann, svm, and knn models for classification of hazelnuts images based on shell cracks and feature selection method

In some countries, people commonly consume hazelnuts in their shells to extend shelf life or due to technological limitations. therefore, open-shell hazelnuts are more marketable. at the semi-industrial scale, open-shell and closed-shell hazelnuts are currently separated from each other through visual inspection. this study aims to develop a new algorithm to separate open-shell hazelnuts from cracked or closed-shell hazelnuts. in the first approach, dimension reduction techniques such as sequential forward feature selection (sffs) and principal component analysis (pca) were used to select or extract a combination of color, texture, and grayscale features for the model’s input. in the second approach, individual features were used directly as inputs. in this study, three famous machine learning models, including support vector machine (svm), k-nearest neighbors (knn), and multi-layer perceptron (mlp) were employed. the results indicated that the sffs method had a greater effect on improving the performance of the models than the pca method. however, there was no significant difference between the performance of the models developed with combined features (98.00%) and that of the models using individual features (98.67%). the overall results of this study indicated that the mlp model, with one hidden layer, a dropout of 0.3, and 10 neurons using histogram of oriented gradients (hog) features as input, is a good choice for classifying hazelnuts into two classes of open-shell and closed-shell.

hyperparameter optimization of ann, svm, and knn models for classification of hazelnuts images based on shell cracks and feature selection method

in some countries, people commonly consume hazelnuts in their shells to extend shelf life or due to technological limitations. therefore, open-shell hazelnuts are more marketable. at the semi-industrial scale, open-shell and closed-shell hazelnuts are currently separated from each other through visual inspection. this study aims to develop a new algorithm to separate open-shell hazelnuts from cracked or closed-shell hazelnuts. in the first approach, dimension reduction techniques such as sequential forward feature selection (sffs) and principal component analysis (pca) were used to select or extract a combination of color, texture, and grayscale features for the model’s input. in the second approach, individual features were used directly as inputs. in this study, three famous machine learning models, including support vector machine (svm), k-nearest neighbors (knn), and multi-layer perceptron (mlp) were employed. the results indicated that the sffs method had a greater effect on improving the performance of the models than the pca method. however, there was no significant difference between the performance of the models developed with combined features (98.00%) and that of the models using individual features (98.67%). the overall results of this study indicated that the mlp model, with one hidden layer, a dropout of 0.3, and 10 neurons using histogram of oriented gradients (hog) features as input, is a good choice for classifying hazelnuts into two classes of open-shell and closed-shell.