外文摘要：Selecting wheat hybrids is vital for enhancing crop yield, adapting to changing climates, and ensuring food security. These hybrids align with market demands and sustainable farming practices, contributing to efficient crop management. Traditional methods for wheat hybrid selection, such as molecular techniques, are costly and time-consuming, and are prone to human error. However, advancements in artificial intelligence and machine learning offer non-destructive, objective, and more efficient solutions. This study is explored the classification of wheat varieties and hybrids using two deep learning models, MobileNetv2 and GoogleNet. These models are achieved impressive classification accuracy, with MobileNetv2 reaching 99.26% and GoogleNet achieving 97.41%. In the second scenario, the deep features obtained from these models are classified with Support Vector Machine (SVM). In the classification made with the MobileNetv2-SVM hybrid model, an accuracy of 99.91% is achieved. This study is provided rapid and accurate wheat variety and hybrid identification method, as well as contributing to breeding programs and crop management.
外文关键词：SVM classifier;MobileNetV2;GoogleNet;Wheat hybrid selection
作者：Sabanci, Kadir;Sonmez, Mesut Ersin;Aydin, Nevzat
作者单位：Karamanoglu Mehmetbey Univ;Karamanoğlu Mehmetbey 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