A Study of Ensemble Deep Learning Method Using Transfer Learning for Horticultural Data Classification

Authors : Gökhan ATALI, Sedanur KIRCI

Pages : 174-180

Doi:10.38016/jista.1229271

View : 40 | Download : 47

Publication Date : 2023-09-23

Article Type : Research Paper

Abstract :Deep learning is an important discipline in which human-specific problems are solved with the help of machines with advanced hardware power. It is seen this discipline is widely used in the fields of industry, health, defense industry, and sports. In addition, the use of deep learning in the field of horticulture is an important requirement. With the integration of deep learning into horticulture, to do product classification is very important for increasing productivity and production. In this study, a method using ensemble learning is proposed to improve the accuracy of the classification problem for horticultural data. For this method, a new dataset was created, containing a total of 24421 images and 15 crop classes, independent of data augmentation. In order to train this created data set with the help of the proposed method, a hierarchical structure has been designed in which the output of one model is the input of the other model. A total of 7 pre-trained models were used in the experimental studies of the proposed method. Since this method is in an ensemble structure, it is possible to add or remove pre-trained models from the structure. With the help of experimental studies, a performance analysis of the proposed method, which is compared with the traditional CNN method, has been made. As a result of these analyses, it has been observed that the proposed method works 3% more successfully.
Keywords : Transfer learning, ensemble learning, convolutional neural network, image classification, deep learning