外文摘要：Hazelnut is an agricultural product that contributes greatly to the economy of the countries where it is grown. The human factor plays a major role in hazelnut classification. The typical approach involves manual inspection of each sample by experts, a process that is both labor-intensive and time-consuming, and often suffers from limited sensitivity. The deep learning techniques are extremely important in the classification and detection of agricultural products. Deep learning has great potential in the agricultural sector. This technology can improve product quality, increase productivity, and offer farmers the ability to classify and detect their produce more effectively. This is important for sustainability and efficiency in the agricultural industry. In this paper aims to the application of deep learning algorithms to streamline hazelnut classification, reducing the need for manual labor, time, and cost in the sorting process. The study utilized hazelnut images from three different varieties: Giresun, Ordu, and Van, comprising a dataset of 1165 images for Giresun, 1324 for Ordu, and 1138 for Van hazelnuts. This dataset is an open-access dataset. In the study, experiments were carried out on the determination of hazelnut varieties with BigTransfer (BiT)-M R50 x 1, BiT-M R101 x 3 and BiT-M R152 x 4 models. Deep learning models, including big transfer was employed for classification. The classification task involved 3627 nut images and resulted in a remarkable accuracy of 99.49% with the BiT-M R152 x 4 model. These innovative methods can also lead to patentable products and devices in various industries, thereby boosting the economic value of the country.
外文关键词：Image recognition;Agricultural automation;Hazelnut Classification;BiT-M Models
作者：Kilicarslan, Serhat;Donmez, Emrah;Diker, Aykut
作者单位：Bandirma 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：25（2）
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