Convolutional neural network model for the qualitative evaluation of geometric shape of carrot root

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外文摘要:The main objective of the study is the development of an automatic carrot root classification model, marked as CRNET, with the use of a Convolutional Neural Network (CNN). CNN with a constant architecture was built, consisting of an alternating arrangement of five Conv2D, MaxPooling2D and Dropout classes, for which in the Python 3.9 programming language a calculation algorithm was developed. It was found that the classification process of the carrot root images was carried out with an accuracy of 89.06%, meaning that 50 images were misclassified. The highest number of 21 erroneously classified photographs were from the extra class, of which 15 to the first class, thus not resulting in significant loss. However, assuming the number of refuse as the classification basis, the model accuracy greatly increases to 98.69%, as only 6 photographs were erroneously assigned.
外文关键词:machine learning;food quality;CNN;Python
作者:Rybacki, Piotr;Sawinska, Zuzanna;Kacaniova, Miroslava;Kowalczewski, Przemyslaw L;Osuch, Andrzej;Durczak, Karol
作者单位:Poznan Univ Life Sci;Slovak Univ Agr
期刊名称:AGRICULTURAL AND FOOD SCIENCE
期刊影响因子:0.0
出版年份:2024
出版刊次:33(1)
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

  1. 编译服务:智慧农业
  2. 编译者:虞德容
  3. 编译时间:2025-02-08