外文摘要：Wheat plant is one of the most basic food sources for the whole world. There are many species of wheat that differ according to the conditions of the region where they are grown. In this context, wheat species can exhibit different characteristics. Issues such as resistance to geographical conditions and productivity are at the forefront in this plant as in all other plants. The wheat species should be correctly distinguished for correct agricultural practice. In this study, a hybrid model based on the Vision Transformer (VT) approach and the Convolutional Neural Network (CNN) model was developed to classify wheat species. For this purpose, ResMLP architecture was modified and the EfficientNetV2b0 model was fine-tuned and improved. A hybrid transformer model has been developed by combining these two methods. As a result of the experiments, the overall accuracy performance has been determined as 98.33%. The potential power of the proposed method for computer-aided agricultural analysis systems is demonstrated.
外文关键词：CNN;Vision transformer;fine-tuning;multi-layer perceptron;wheat species
作者：Donmez, Emrah
作者单位：Bandırma Onyedi Eylul Univ
期刊名称：EUROPEAN FOOD RESEARCH AND TECHNOLOGY
期刊影响因子：0.0
出版年份：2024
出版刊次：250（5）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台